DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1, 3-14, 16-22 are currently pending in the present application, with claim 1, 14, and 19 being independent, claims 21-22 being newly added.
Response to Arguments/Amendments
Applicant’s arguments, see Pg. 13, filed 01/05/2026, with respect to claims 19-20 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection of claims 19-20 has been withdrawn.
Applicant’s arguments, see 13-21, filed 01/05/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. §§ 102 and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 8, 14, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”.
Regarding claim 1, Sudarsky discloses a method for a computed tomography (CT) system, the method comprising:
performing a CT scan of a patient (Par. 0027; a medical scanner acquires a set of voxels. The set represents a volume of the patient…The interior of the patient is scanned, such as with magnetic resonance (MR), x-ray (e.g., computed tomography (CT)), ultrasound, or emission tomography (e.g., positron emission tomography (PET) or single photon emission computed tomography (SPECT)) injected with a contrast agent (Par. 0029; The intensities represent response from…contrast agents in the patient);
reconstructing an image based on projection data acquired during the CT scan (Par. 0028; A renderer or the medical imaging system reconstructs a volume 31…The reconstruction determines scalar values or intensities for each of a plurality of voxels distributed in three dimensions);
performing an automated segmentation of a plurality of anatomical regions of the reconstructed image (Fig.2; automatic segmentation and Par. 0040; In another embodiment, the ROI designation is received as automated detection of the ROI and/or landmarks defining the ROI. Segmentation may be used to position the ROI, such as the ROI being a border of detected anatomy…),
after the segmentation is finished, assessing opacity values of a respective plurality of anatomical reference points of the plurality of segmented anatomical regions, the opacity values indicating a level of contrast agent uptake at the anatomical reference points (Fig. 3-6 and Par. 0003-0004; A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image…A second transfer function (e.g., new transfer function in the sense of having a different value for one or more parameters) is determined from a distribution of the intensities of the voxels of the subset. Par. 0029; The intensities represent response from blood, tissue, bone, other object, and/or contrast agents in the patient. Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used);
adjusting a first contrast of a first anatomical region of the image based on a first set of display parameter settings of the CT system (Par. 0049-0052; the window width and/or window level are changed. The change is to increase contrast for the tissue or structure of the ROI…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function…To increase contrast, the window width is reduced, and the window level is centered about the mean. Using the window width and level formulas above, a new window width and a new window level are determined. FIG. 4 shows the new transfer function 29. The width is decreased, and the center of the window is shifted to a lower scalar value. FIG. 6 shows the new transfer function 29 with the overlaid histogram. The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram))
adjusting a second contrast of a second anatomical region of the image based on a second set of display parameter settings of the CT system, the second anatomical region different from the first anatomical region, the second set of display parameter settings different from the first set of display parameter settings (Par. 0048-0052; the window width and/or window level are changed. The change is to increase contrast for the tissue or structure of the ROI…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function…To increase contrast, the window is centered by a mean for the intensities of the voxels identified by the ROI, and/or the window width is reduced to map the full range of colors to a smaller range of scalar values…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function…To increase contrast, the window width is reduced, and the window level is centered about the mean. Using the window width and level formulas above, a new window width and a new window level are determined. FIG. 4 shows the new transfer function 29. The width is decreased, and the center of the window is shifted to a lower scalar value. FIG. 6 shows the new transfer function 29 with the overlaid histogram. The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram));
and displaying a contrast-optimized image on a display device of the CT system, the contrast-optimized image showing the first anatomical region of the image in the first contrast, and the second anatomical region of the image in the second contrast, the second contrast different from the first contrast (Fig. 7-14 and Par. 0062; The two-dimensional image 33 is rendered to a display…The two-dimensional image 33 is shown by itself or is shown adjacent to one or more other two-dimensional images 33, such as renderings of the same scan data with different transfer functions.);
wherein: adjusting the first contrast of the first anatomical region of the image (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…) based on the first set of display parameter settings of the CT system (Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level. The color map controls what colors are assigned to the scalar values) further comprises applying a set of one or more algorithms to the image (transfer functions) based on a first opacity value of a first anatomical reference point of the first anatomical region to determine the first set of display parameter settings (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above); and
adjusting the second contrast of the second anatomical region of the image (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…The placement of the ROI facilitates exploration of the volumetric data set by interactively defining new transfer functions) based on the second set of display parameter settings of the CT system (Par. 0044; the graphics processing unit, medical scanner, and/or processor determines another transfer function 29 to use for rendering in act 40 (or a repeat of act 32). The new transfer function has one or more characteristics changed, such as the window level, window width, color pallet, opacity, and/or opacity range. The new transfer function may be the same color transfer function, but with different settings for one or more parameters of the transfer function (e.g., different window width)) further comprises applying the set of one or more algorithms to the image (transfer functions) based on a second opacity value of a second anatomical reference point of the second anatomical region to determine the second set of display parameter settings (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above).
Regarding performing an automated segmentation of a plurality of anatomical regions of the reconstructed image, it is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art to combine and employ combinations and sub-combinations of these complementary embodiments because Sudarsky explicitly motivates doing so. Sudarsky teaches selecting a region of interest (ROI) in a rendered image and determining transfer function parameters based on voxel intensities corresponding to that ROI (Par. 0003-0004 and Par. 0065-0066), the reference further teaches that segmentation may be used to position the ROI and that the segmentation represented by intensities also controls the color (Par. 0040 and Par. 0057). Accordingly, incorporating automated segmentation into the region-based transfer function determination framework of Sudarsky would have been an obvious design choice and otherwise motivating experimentation and optimization.
Regarding applying a set of one or more algorithms to the image based on a first opacity value of a first anatomical reference point of the first anatomical region to determine the first set of display parameter settings, and applying the set of one or more algorithms to the image based on a second opacity value of a second anatomical reference point of the second anatomical region to determine the second set of display parameter settings, it is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art to combine and employ combinations and sub-combinations of these complementary embodiments because Sudarsky explicitly motivates doing so. Sudarsky teaches that transfer function characteristics include window width, window level, and opacity (Par. 0035), and further teaches algorithmic determination of window parameters based on statistical characteristics of the voxel intensity distribution (e.g., Gaussian curve fitting) (Par. 0049-0052). Sudarsky also discloses determining a new transfer function based on a distribution of voxel intensities corresponding to a region of interest (Par 0003-0004) and that the opacity modification may be used with or without window or other modifications of the transfer function 29 (Par. 0054-0056). Accordingly, incorporating measured voxel intensity values (opacity values) to algorithmically determine display parameter settings is a predictable use of known image-processing techniques to optimize contrast presentation, otherwise motivating experimentation and optimization.
Regarding claim 3, Sudarsky discloses wherein the first set of display parameter settings includes a first window width (WW) setting and a first window level (WL) setting, and the second set of display parameter settings includes a second WW setting and a second WL setting (Par. 0049-0052; window width and level formulas…The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram)), where the second WW setting is different from the first WW setting and the second WL setting is different from the first WL setting (Par. 0001; Different combinations of the window width and level parameters lead to different findings and have to be adjusted repeatedly to improve the diagnostic information present in the image. Par. 0065-0066; different ROIs are selected and the system automatically re-computes the optimal windowing parameters. Multiple views are generated by placing ROIs on different tissue types…For each of the different transfer functions, the window level and width are modified. By selecting different ROIs, the viewer or processor may navigate through the scan data or explore the volumetric data to view different tissue with greater contrast. Examiner's note: different display parameters settings (window width and window level) are based on different ROIs).
Regarding claim 8, Sudarsky discloses the method of claim 1, and further discloses adjusting the first set of display parameter settings further comprises digitally adjusting the first contrast of the first anatomical region as a function of a first set of image data (Par. 0049-0052; the window width and/or window level are changed. The change is to increase contrast for the tissue or structure of the ROI…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function…To increase contrast, the window width is reduced, and the window level is centered about the mean. Using the window width and level formulas above, a new window width and a new window level are determined. FIG. 4 shows the new transfer function 29. The width is decreased, and the center of the window is shifted to a lower scalar value. FIG. 6 shows the new transfer function 29 with the overlaid histogram. The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram)) acquired at each voxel of the first anatomical region (Par. 0003; A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image. Both the visual aspect of the rendered image and the voxel data from the scan are used to set the transfer function. Par. 0035; The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level) and adjusting the second set of display parameter settings further comprises adjusting the second contrast of the second anatomical region as a function of a second set of image data (Par. 0049-0052; the window width and/or window level are changed. The change is to increase contrast for the tissue or structure of the ROI…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function…To increase contrast, the window width is reduced, and the window level is centered about the mean. Using the window width and level formulas above, a new window width and a new window level are determined. FIG. 4 shows the new transfer function 29. The width is decreased, and the center of the window is shifted to a lower scalar value. FIG. 6 shows the new transfer function 29 with the overlaid histogram. The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram)) acquired at each voxel of the second anatomical region (Par. 0003; A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image. Both the visual aspect of the rendered image and the voxel data from the scan are used to set the transfer function. Par. 0035; The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level).
Regarding claim 14, Sudarsky discloses a computed tomography (CT) system, comprising a processor and a non-transitory memory including instructions that when executed, cause the processor to (Fig. 15):
reconstruct an image based on scan data of a patient acquired during a CT scan of the CT system (Par. 0028; A renderer or the medical imaging system reconstructs a volume 31…The reconstruction determines scalar values or intensities for each of a plurality of voxels distributed in three dimensions);
segment a plurality of anatomical regions of the reconstructed image (Fig.2; automatic segmentation and Par. 0040; In another embodiment, the ROI designation is received as automated detection of the ROI and/or landmarks defining the ROI. Segmentation may be used to position the ROI, such as the ROI being a border of detected anatomy…);
after the segmentation is finished, assess opacity values of a respective plurality of anatomical reference points of the plurality of segmented anatomical regions, the opacity values indicating a level of contrast agent injected into the patient prior to the CT scan at the anatomical reference points (Fig. 3-6 and Par. 0003-0004; A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image…A second transfer function (e.g., new transfer function in the sense of having a different value for one or more parameters) is determined from a distribution of the intensities of the voxels of the subset. Par. 0029; The intensities represent response from blood, tissue, bone, other object, and/or contrast agents in the patient. Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used);
apply a first set of one or more algorithms to the reconstructed image (transfer functions) based on a first opacity value of a first anatomical reference point (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above) to adjust a first contrast of a first anatomical region of the plurality of anatomical regions (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…The placement of the ROI facilitates exploration of the volumetric data set by interactively defining new transfer functions);
apply a second set of one or more algorithms to the reconstructed image (transfer functions) based on a second opacity value of a second anatomical reference point (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above) to adjust a second contrast of a second anatomical region of the plurality of anatomical regions (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…The placement of the ROI facilitates exploration of the volumetric data set by interactively defining new transfer functions), the second anatomical region different from the first anatomical region, the second set of one or more algorithms different from the first set of one or more algorithms, the second contrast different from the first contrast (Par. 0044; the graphics processing unit, medical scanner, and/or processor determines another transfer function 29 to use for rendering in act 40 (or a repeat of act 32). The new transfer function has one or more characteristics changed, such as the window level, window width, color pallet, opacity, and/or opacity range. The new transfer function may be the same color transfer function, but with different settings for one or more parameters of the transfer function (e.g., different window width)); and
display a contrast-optimized image on a display device of the CT system, the contrast-optimized image showing the first anatomical region of the image in the first contrast, and the second anatomical region of the image in the second contrast (Fig. 7-14 and Par. 0062; The two-dimensional image 33 is rendered to a display…The two-dimensional image 33 is shown by itself or is shown adjacent to one or more other two-dimensional images 33, such as renderings of the same scan data with different transfer functions).
Regarding segment a plurality of anatomical regions of the reconstructed image, it is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art to combine and employ combinations and sub-combinations of these complementary embodiments because Sudarsky explicitly motivates doing so. Sudarsky teaches selecting a region of interest (ROI) in a rendered image and determining transfer function parameters based on voxel intensities corresponding to that ROI (Par. 0003-0004 and Par. 0065-0066), the reference further teaches that segmentation may be used to position the ROI and that the segmentation represented by intensities also controls the color (Par. 0040 and Par. 0057). Accordingly, incorporating automated segmentation into the region-based transfer function determination framework of Sudarsky would have been an obvious design choice and otherwise motivating experimentation and optimization.
Regarding claim 21, Sudarsky discloses wherein applying the set of one or more algorithms to the image (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…) based on the first opacity value of the first anatomical reference point of the first anatomical region to determine the first set of display parameter settings (Fig. 3-6 and Par. 0003-0004; A characteristic of the selected voxel data is used to determine the transfer function for rendering another image…Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used. Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29) further comprises performing a first process on the first anatomical region (Par. 0004-0006; A graphics processing unit renders, using a first transfer function, a first two-dimensional image from the intensities representing the voxels. A region of interest determined based on the first two-dimensional image is received, and a subset of the voxels are identified based on the region of interest. A second transfer function (e.g., new transfer function in the sense of having a different value for one or more parameters) is determined from a distribution of the intensities of the voxels of the subset. The graphics processing unit renders a second two-dimensional image using the second transfer function), and applying the set of one or more algorithms to the image (Par. 0044; the graphics processing unit, medical scanner, and/or processor determines another transfer function 29 to use for rendering in act 40 (or a repeat of act 32). The new transfer function has one or more characteristics changed, such as the window level, window width, color pallet, opacity, and/or opacity range. The new transfer function may be the same color transfer function, but with different settings for one or more parameters of the transfer function (e.g., different window width)) based on the second opacity value of the second anatomical reference point of the second anatomical region to determine the second set of display parameter settings (Fig. 3-6 and Par. 0003-0004; A characteristic of the selected voxel data is used to determine the transfer function for rendering another image…Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used. Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29) further comprises performing a second process on the second anatomical region, the second process different from the first process (Par. 0004-0006; A graphics processing unit renders, using a first transfer function, a first two-dimensional image from the intensities representing the voxels. A region of interest determined based on the first two-dimensional image is received, and a subset of the voxels are identified based on the region of interest. A second transfer function (e.g., new transfer function in the sense of having a different value for one or more parameters) is determined from a distribution of the intensities of the voxels of the subset. The graphics processing unit renders a second two-dimensional image using the second transfer function).
Regarding the first opacity value of the first anatomical reference point of the first anatomical region to determine the first set of display parameter settings, and the second opacity value of the second anatomical reference point of the second anatomical region to determine the second set of display parameter settings, it is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art to combine and employ combinations and sub-combinations of these complementary embodiments because Sudarsky explicitly motivates doing so. Sudarsky teaches that transfer function characteristics include window width, window level, and opacity (Par. 0035), and further teaches algorithmic determination of window parameters based on statistical characteristics of the voxel intensity distribution (e.g., Gaussian curve fitting) (Par. 0049-0052). Sudarsky also discloses determining a new transfer function based on a distribution of voxel intensities corresponding to a region of interest (Par 0003-0004) and that the opacity modification may be used with or without window or other modifications of the transfer function 29 (Par. 0054-0056). Accordingly, incorporating measured voxel intensity values (opacity values) to algorithmically determine display parameter settings is a predictable use of known image-processing techniques to optimize contrast presentation, otherwise motivating experimentation and optimization.
Claim(s) 4-5, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Worstell et al. (US 20170023498), hereinafter referred to as “Worstell”.
Regarding claim 4, Sudarsky discloses the first set of display parameter settings and the second set of display parameter settings (Par. 0049-0052; window width and level formulas…The window is positioned to capture most of the intensities for voxels corresponding to the ROI (i.e., capture the larger count bins of the histogram)), but does not disclose the first set of display parameter settings includes a first kiloelectron voltage (keV) setting, based on a first linear combination reconstructed with a first set of keV values and a second basis image reconstructed with a second set of keV values; and the second set of display parameter settings includes a second keV setting based on a second linear combination of the first basis image and the second basis image.
In the same art of CT image processing, Worstell discloses includes a first kiloelectron voltage (keV) setting (Par. 0017; two different X-ray energy ranges…"low energy measurement") based on a first linear combination (Fig. 7-9 and Par. 0019; R and g) of a first basis image reconstructed with a first set of keV values (Par. 0020; a corresponding image for that monoenergy 40 kev) and a second basis image reconstructed with a second set of keV values (Par. 0020; a corresponding image for that monoenergy 160 kev),
and includes a second keV setting (Par. 0017; two different X-ray energy ranges…"high energy measurement") based on a second linear combination (Fig. 7-9) of the first basis image and the second basis image (Fig. 8 and Par. 0031).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Worstell having keV settings based on linear combinations of basis images for reconstruction with Sudarsky’s volume rendering system for medical imaging. The motivation lies in the advantage of dynamically adjusting energy levels per anatomical region, improving image visualization and accuracy. The combination yields predictable results, since different ROIs often exhibit different tissue composition and attenuation properties, and therefore, would benefit from being imaged at different energy levels (keV), and DECT being a common practice in CT systems.
Regarding claim 5, Sudarsky discloses first opacity values and second opacity values (Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used), but does not disclose retrieving the first keV setting from a lookup table stored in a memory of the CT system based on the first opacity values, and retrieving the second keV setting from the lookup table based on the second opacity value
In the same art of CT image processing, Worstell discloses retrieving the first keV setting (Par. 0020; a lookup table (comprising the appropriate multiplicative factors “A.sub.40” and “B.sub.40”) can be generated to produce a corresponding monochromatic sinogram G.sub.40 (i.e., a corresponding monochromatic sinogram at monoenergy 40 kev), and from there, a corresponding image I.sub.40 for that monoenergy (i.e., a corresponding image for that monoenergy 40 kev)) from a lookup table (Par. 0020; A lookup table) stored in a memory of the CT system (dual energy CT imaging system) , and retrieving the second keV setting (Par. 0020; a lookup table (comprising the appropriate multiplicative factors “A.sub.160” and “B.sub.160”) can be generated to produce a corresponding monochromatic sinogram G.sub.160 (i.e., a corresponding monochromatic sinogram at monoenergy 160 kev), and from there, a corresponding image I.sub.160 for that monoenergy (i.e., a corresponding image for that monoenergy 160 kev)) from a lookup table (Par. 0020; A lookup table)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teachings of Worstell to retrieve the first and second kEV setting from a lookup table stored in memory, with the segmentation and calculation of opacity as taught by Sudarsky. Storing values in a lookup table and retrieving them based on patient or region-specific parameters would be a routine design choice, as it is common practice to store, retrieve, and map scan data. The motivation lies in the advantage of automating the process and efficiency.
Regarding claim 22, Sudarsky discloses the method of claim 1, and further discloses the first opacity value is lower than expected for the patient (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above),
The second opacity value is higher than expected for the patient (Par. 0054-0056; The distribution of scalar values of corresponding to the ROI is used to modify the corresponding opacity values on the transfer function 29. The opacity modification may be used with or without window or other modifications of the transfer function 29… In addition to calculating the window level and width to increase contrast, a transparency range is also calculated using the formulas above),
Sudarsky does not disclose ; and
In the same art of CT imaging, Worstell discloses (detection regions) is displayed with lower keV values than the image (Par. 0020; corresponding image I.sub.40 for that monoenergy (i.e., a corresponding image for that monoenergy 40 kev); and
(detected regions) is displayed with higher keV values than the image (Par. 0020; corresponding image I.sub.160 for that monoenergy (i.e., a corresponding image for that monoenergy 160 kev)).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the monoenergetic image selection techniques of Worstell into Sudarsky’s opacity-driven display parameter optimization framework. Sudarsky teaches measuring opacity values at anatomical reference points and adjusting display parameters based on those measured values, while Worstell teaches generating monoenergetic images at different keV values, where lower keV enhances attenuation contrast of materials and higher keV reduces beam hardening artifacts and alters material appearance. Because attenuation properties of contrast agents and tissues are well known to vary with photon energy, a person of ordinary skill in the art seeking to improve contrast visualization based on measured opacity would have been motivated to select lower keV images when measured opacity is lower than expected and higher keV images when opacity is higher than expected, representing predictable use of known energy-dependent attenuation behavior to optimize image visualization in display adjustment systems.
Claim(s) 6 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45, no. 10 (2018): 4541-4557, hereinafter referred to as “Dorn”.
Regarding claim 6, Sudarsky discloses the method of claim 1, but does not disclose adjusting the first set of display parameter settings further comprises adjusting frequency contents of a first set of projection data of the first anatomical region based on a first kernel, and adjusting the second set of display parameter settings further comprises adjusting frequency contents of a second set of projection data of the second anatomical region based on a second kernel, the second kernel different from the first kernel.
In the same art of CT image processing, Dorn discloses adjusting the first set of display parameter settings further comprises adjusting frequency contents of a first set of projection data of the first anatomical region based on a first kernel, and adjusting the second set of display parameter settings further comprises adjusting frequency contents of a second set of projection data of the second anatomical region based on a second kernel, the second kernel different from the first kernel (Pg. 4544, Section 2.C.1. CSR; The basis images are reconstructed with different reconstruction kernels leading to various resolution levels. The CSR is defined as (4). Fig. 5 and Section 3.C.1, Pg. 4549; we chose the number of basis images B=2 and the smooth basis image fsmooth denotes a reconstruction with the D20f kernel and the sharp basis image fsharp denotes a reconstruction with the B80f kernel. The image is composed of the smooth basis image for soft tissue, fat, organs etc., and sharp basis image for lung and bones…showin in three typical window level settings, namely the body window, lung window, and bone window…There are many analytic reconstructions that are adapted to different anatomical regions. For instance, a B23f kernel…often recommended for a reconstruction of the vascular system. Section 4, Pg. 4556; there are a great variety of convolution kernels, each resulting in different image properties regarding the noise level and spatial resolution…kernel selection and the kernel to organ assignment would be freely specifiable by the user).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dorn’s organ-specific reconstruction kernel selection into Sudarsky’s region-based contrast optimization framework. The motivation lies in the advantage of enhanced contrast differentiation, improved noise resolution tradeoffs per anatomical region, and provide a higher quality intensity distribution for transfer-function computation. Such combination is a routine optimization of known CT reconstruction parameters applied to known region-based visualization techniques, yielding predictable improvements in image quality.
Regarding claim 9, Sudarsky discloses the method of claim 1, but does not disclose displaying a visualization of material decomposition information acquired from the patient during the CT scan superimposed on the contrast-optimized image, the visualization of the material decomposition information including one or more colorized overlays generated based on the material decomposition information the one or more colorized overlays including anatomical regions having a 1:1 correspondence with anatomical regions of the contrast-optimized image with respect to size and positioning, the one or more colorized overlays applying different colors to different anatomical regions of the reconstructed image.
In the same art of CT image processing, Dorn discloses displaying a visualization of material decomposition information acquired from the patient during the CT scan superimposed on the contrast-optimized image, the visualization of the material decomposition information including one or more colorized overlays generated based on the material decomposition information (Pg, 4551-4552, Section 3.D. and Fig. 11; Within one single DE image, we combine material decomposition and classification tasks and are able to show color overlays wherever appropriate), the one or more colorized overlays including anatomical regions having a 1:1 correspondence with anatomical regions of the contrast-optimized image with respect to size and positioning, the one or more colorized overlays applying different colors to different anatomical regions of the reconstructed image (Fig. 11-12).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dorn’s material decomposition overlay visualization into Sudarsky’s contrast-optimized rendering framework. The motivation lies in the advantage of providing enhanced diagnostic visualization of region-specific contrast enhancement, structural visibility, and anatomical alignment. Integrating Dorn’s overlay visualization into Sudarsky’s rendered output would have been a routine implementation of known overlay rendering techniques, yielding predictable benefits of improved differentiation between materials, enhanced diagnostic clarity, and simultaneous structural and compositional visualization.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Kojima et al. (US 10905388 B2), hereinafter referred to as “Kojima”.
Regarding claim 7, Sudarsky discloses the method of claim 1, and further discloses adjusting the first set of display parameter settings further comprises adjusting the first contrast of the first anatomical region (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…The placement of the ROI facilitates exploration of the volumetric data set by interactively defining new transfer functions)
adjusting the second set of display parameter settings further comprises adjusting the second contrast of the second anatomical region (Par. 0021; To provide more contrast, the transfer function is set based on the voxels for that tissue designated by the ROI. The transfer function may be set to increase the contrast within the selected region…The placement of the ROI facilitates exploration of the volumetric data set by interactively defining new transfer functions)
wherein the adjusted first contrast is different from the adjusted second contrast (Par. 0044; the graphics processing unit, medical scanner, and/or processor determines another transfer function 29 to use for rendering in act 40 (or a repeat of act 32). The new transfer function has one or more characteristics changed, such as the window level, window width, color pallet, opacity, and/or opacity range. The new transfer function may be the same color transfer function, but with different settings for one or more parameters of the transfer function (e.g., different window width)).
Sudarsky does not disclose adjusting the second contrast of the second anatomical region based on a second photon count at a second photon counting energy bin associated with the second anatomical region;
In the same art of CT imaging, Kojima discloses ) based on a first photon count at a first photon counting energy bin (Column 1, lines 26-42; a photon counting type detector counts photons of X-rays…categorizes individual X-ray photons being counted, according to energy values, and then X-ray intensity can be obtained on the basis of energy band (energy bin). Column 2, Lines 17-19; the number of X-ray photons that have passed through an object is counted in each of at least three bins; a low energy bin, a middle energy bin, and a high energy bin) associated with the first anatomical region (Fig. 3(a)-3(b) and Column 4, lines 18-59 ; The energy band setter 404 sets an energy range of at least one of the multiple energy bands in the X-ray detector 321, on the basis of the distribution of degrees of X-ray attenuation at respective energy levels…as shown in FIG. 3(a), in a section (region) 41 including many bones or in a section (region) 42 embedded with metal, within the subject 101, there is a high degree of attenuation in low-energy level X-rays. Thus, the number of X-ray photons of low-energy level reaching the X-ray detector 321 through such regions becomes small…); and
(Column 1, lines 26-42; a photon counting type detector counts photons of X-rays…categorizes individual X-ray photons being counted, according to energy values, and then X-ray intensity can be obtained on the basis of energy band (energy bin). Column 2, Lines 17-19; the number of X-ray photons that have passed through an object is counted in each of at least three bins; a low energy bin, a middle energy bin, and a high energy bin)associated with the second anatomical region (Fig. 3(a)-3(b) and Column 4, lines 18-59 ; The energy band setter 404 sets an energy range of at least one of the multiple energy bands in the X-ray detector 321, on the basis of the distribution of degrees of X-ray attenuation at respective energy levels…as shown in FIG. 3(a), in a section (region) 41 including many bones or in a section (region) 42 embedded with metal, within the subject 101, there is a high degree of attenuation in low-energy level X-rays. Thus, the number of X-ray photons of low-energy level reaching the X-ray detector 321 through such regions becomes small…);
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to implement Sudarsky’s region-based contrast adjustment frame working using Kojima’s photon-counting CT data from energy bins. It is well known that there are energy-dependent attenuation differences between tissues, therefore, using bin-specific photon counts allows more precise contrast tuning for different anatomical regions. Substituting bin-specific photon counts as input to Sudarsky’s contrast optimization framework would have been a straightforward design choice, yielding predictable results in improved contrast adjustment when combining known photon-counting spectral techniques with known algorithmic contrast optimizations in CT imaging systems.
Claim(s) 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45, no. 10 (2018): 4541-4557, hereinafter referred to as “Dorn”, and in further view of Hofmann et al. (DE 102019210473), hereinafter “Hofmann”.
Regarding claim 10, Sudarsky in view of Dorn discloses the method of claim 9, but does not disclose wherein a first colorized overlay of the one or more colorized overlays shows healthy tissues of the patient in a first color, and shows diseased tissues of the patient in a second color, wherein the first color and the second color highlight a contrast between the healthy tissues and the diseased tissues of the patient.
In the same art of imaging in computed tomography, Hofmann discloses wherein a first colorized overlay of the one or more colorized overlays shows healthy tissues of the patient in a first color (Par. 0014; suppressed healthy tissue), and shows diseased tissues of the patient in a second color (Par. 0011; The method according to the invention relates to an improvement in CT imaging and serves to better distinguish between different types of tissue in an object. In particular, diseases that are associated with a change in the water content (edema formation) can be recognized in regions with different types of tissue that are close together. The disease can spread to both types of tissue and be of different therapeutic or diagnostic relevance there, e.g. in tumor diseases or fractures), wherein the first color and the second color highlight a contrast between the healthy tissues and the diseased tissues of the patient (Par. 0031; It is preferable to display different compartments, i.e. areas in which different tissue characteristics were detected during processing, differently, e.g. with different colors or a different texture. It is conceivable that the information could be presented as a color-overlaid edema content on the gray-white images of a CT scan of the brain).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Hofmann having colorized overlays to distinguish healthy tissue from diseased tissue with the contrast-optimized image techniques taught by Sudarsky in view of Dorn. The motivation lies in the advantage of improved diagnostic clarity and visual differentiation, more specifically in identifying diseased tissue (Hofmann Par. 0031.; This overlay advantageously provides essential information with regard to the evaluation of the edema, since the significance of the edema differs significantly in the two substances). Applying color coded overlays to identify specific regions, more specifically healthy and diseased tissues, is a routine enhancement in medical imaging visualization, a predictable use of known techniques in imaging practice.
Regarding claim 17, Sudarsky discloses the CT system of claim 14, but does not disclose cause the processor to display a colorized overlay superimposed on the contrast-optimized image, the colorized overlay showing healthy tissues of the patient in a first color, and diseased tissues of the patient in a second color, the colorized overlay based on material decomposition data generated from the CT scan.
In the same art of CT imaging, Dorn discloses cause the processor to display a colorized overlay superimposed on the contrast-optimized image, the colorized overlay based on material decomposition data generated from the CT scan (Pg, 4551-4552, Section 3.D. and Fig. 11; Within one single DE image, we combine material decomposition and classification tasks and are able to show color overlays wherever appropriate. Pg. 4545-4546, Section; material decomposition — quantification and color coding of the iodine concentration in the lung 21 (Lung PBV) and heart (Heart PBV).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dorn’s material decomposition overlay visualization into Sudarsky’s contrast-optimized rendering framework. The motivation lies in the advantage of providing enhanced diagnostic visualization of region-specific contrast enhancement, structural visibility, and anatomical alignment. Integrating Dorn’s overlay visualization into Sudarsky’s rendered output would have been a routine implementation of known overlay rendering techniques, yielding predictable benefits of improved differentiation between materials, enhanced diagnostic clarity, and simultaneous structural and compositional visualization.
Sudarsky in view of Dorn does not disclose the colorized overlay showing healthy tissues of the patient in a first color, and diseased tissues of the patient in a second color
In the same art of CT imaging, Hofmann discloses the colorized overlay (Par. 0031; It is preferable to display different compartments, i.e. areas in which different tissue characteristics were detected during processing, differently, e.g. with different colors or a different texture. It is conceivable that the information could be presented as a color-overlaid edema content on the gray-white images of a CT scan of the brain) showing healthy tissues of the patient in a first color, and diseased tissues of the patient in a second color (Par. 0011; The method according to the invention relates to an improvement in CT imaging and serves to better distinguish between different types of tissue in an object. In particular, diseases that are associated with a change in the water content (edema formation) can be recognized in regions with different types of tissue that are close together. The disease can spread to both types of tissue and be of different therapeutic or diagnostic relevance there, e.g. in tumor diseases or fractures).
The motivation to combine would’ve been the same as that set forth in claim 10.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45, no. 10 (2018): 4541-4557, hereinafter referred to as “Dorn”, in further view of Hofmann et al. (DE 102019210473), hereinafter “Hofmann”, and in further view of Min (US 20220392065).
Regarding claim 11, Sudarsky in view of Dorn and in further view of Hofmann discloses the method of claim 10, and Sudarsky further discloses the second contrast-optimized image generated from previous scan data acquired from the patient and stored in a picture archiving and communications system (PACS) coupled to the CT system (Sudarsky Par. 0030; the scan or voxel data is stored, such as in a picture archiving communications system (PACS) or patient medical record database. The stored scan data is loaded from memory for rendering).
Sudarsky in view of Dorn and in further view of Hofmann does not disclose generating an automated report comparing the contrast-optimized image including the one or more colorized overlays with a second contrast-optimized image including a second set of colorized overlays,
In the same art of CT image processing, Min discloses generating an automated report (Par. 0013; Patient-specific medical report) comparing the contrast-optimized image (Par. 0204; the medical image comprises one or more of a contrast-enhanced CT image) including the one or more colorized overlays (Par. 0215; quantized color mapping of calcified plaque, non-calcified plaque, good plaque, bad plaque, stable plaque, and/or unstable plaque. Fig. 7A; plaque overlay 707) with a second contrast-optimized image (Par. 0204; the medical image comprises one or more of a contrast-enhanced CT image) including a second set of colorized overlays (Par. 0215; the quantified color mapping can also include arteries and/or epicardial fat), the second contrast-optimized image generated from previous scan data stored in a picture archiving and communications system (PACS) coupled to the CT system, the automated report describing at least a progression of a disease (Fig. 23D and Par. 0008; track disease progression. See Par. 0186-0187 on embodiments)) in an anatomical region of the patient (regions of plaque), the progression of the disease determined by comparing a first size of a first area of diseased tissue in the contrast-optimized image with a second size of a second area of diseased tissue in the second contrast-optimized image (Par. 0303; the system can be configured to determine and/or use the location, size, shape, diffusivity and/or the attenuation radiodensity of one or more regions of calcified plaque to determine the cause of an increase in calcium score)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Min into the contrast-optimized imaging method of Sudarsky, Dorn, and Hofmann in order to generate an automated report comparing contrast-optimized images and track progression of diseases. The motivation lies in the advantage of automatically quantifying and tracking changes in anatomical regions over time. Reporting features to contrast-optimized and color-overlayed images yield predictable results in improving CT imaging by providing users with measurements, data, and visual/textual interpretation for subsequent use.
Regarding claim 12, Sudarsky in view of Dorn, in further view of Hofmann, and in further view of Min discloses the method of claim 11, but Sudarsky in view of Dorn in further view of Hofmann does not disclose wherein describing the progression of the disease in the anatomical region of the patient further comprises displaying the contrast-optimized and the second contrast-optimized image side by side in the automated report.
In the same art of CT image processing, Min discloses describing the progression of the disease in the anatomical region of the patient further comprises displaying the contrast-optimized and the second contrast-optimized image side by side in the automated report (Fig. 5-9).
The motivation to combine would’ve been the same as that set forth in claim 11.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45, no. 10 (2018): 4541-4557, hereinafter referred to as “Dorn”, in further view of Hofmann et al. (DE 102019210473), hereinafter “Hofmann”, in further view of Min (US 20220392065), and in further view of Golden (WO 2019103912).
Regarding claim 13, Sudarsky in view of Dorn in further view of Hoffman, and in further view of Min discloses the method of claim 11, and further discloses wherein the automated report includes a summary of the progression of the disease (Min Par. 0443-0444; The full text Report presents a textual summary of the atherosclerosis, stenosis, and CAD-RADS measures), but does not disclose and additional information in natural language.
Golden discloses additional information in natural language (Pg. 79, lines 1-14; In one case, the report is created as a simple paragraph with text describing the findings. This can be done by populating fields in a paragraph with the findings, or via NLP methods of creating text. The automatic report can be structured so that findings are presented based on urgency and severity).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the natural language processes taught by Golden with the automated report for CT imaging methods taught by Sudarsky, Dorn, Hofmann, and Min. Incorporating an additional AI-generated summary of clinical observations automates the reporting process based on previous data, improving usability and reporting efficiency.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45, no. 10 (2018): 4541-4557, hereinafter referred to as “Dorn”, and in further view of Worstell et al. (US 20170023498), hereinafter referred to as “Worstell”.
Regarding claim 16, Sudarsky discloses the CT system of claim 14, and further discloses wherein applying the first or second set of one or more algorithms further comprises adjusting:
a window width setting and/or a window level setting for displaying a respective segmented anatomical region (Par. 0049-0052; the window width and/or window level are changed. The change is to increase contrast for the tissue or structure of the ROI…the window width is determined from a mean and standard deviation of the Gaussian curve and a window width of the initial transfer function, and the window level is determined from the window width of the second transfer function, the mean, and the window width of the first transfer function); and
a contrast of the respective segmented anatomical region as a function of image data of each voxel of the respective segmented anatomical region (Fig. 3-6 and Par. 0003-0004; A region of interest on a rendered image is used to select some of the voxel data. A characteristic of the selected voxel data is used to determine the transfer function for rendering another image…A second transfer function (e.g., new transfer function in the sense of having a different value for one or more parameters) is determined from a distribution of the intensities of the voxels of the subset. Par. 0029; The intensities represent response from blood, tissue, bone, other object, and/or contrast agents in the patient. Par. 0035; The transfer function 29 is a color map used to assign color to scalar values of the scan data. One or more characteristics distinguish one transfer function 29 from another. The characteristics include the color map or pallet, the opacity (or transparency) level, transparency range or variation, a window width, and/or a window level…The opacity may be one value or may be a range of scalar values over which a given opacity is used).
Sudarsky does not disclose a keV setting for displaying the respective segmented anatomical region; a kernel to apply to adjust frequency contents of projection data of the respective segmented anatomical region; a contrast of the respective segmented anatomical region as a function of material decomposition information associated with the respective segmented anatomical region;
In the same art of CT image processing, Dorn discloses a kernel to apply to adjust frequency contents of projection data of the respective segmented anatomical region (Pg. 4544, Section 2.C.1. CSR; The basis images are reconstructed with different reconstruction kernels leading to various resolution levels. The CSR is defined as (4). Pg. 4549, Section 3.C.1.; The image is composed of the smooth basis image for soft tissue, fat, organs etc., and sharp basis image for lung and bone revealing no information loss),
a contrast of the respective segmented anatomical region as a function of material decomposition information associated with the respective segmented anatomical region (Section 3.D. and Fig. 11; Within one single DE image, we combine material decomposition and classification tasks and are able to show color overlays wherever appropriate),
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dorn’s organ-specific reconstruction kernel selection into Sudarsky’s region-based contrast optimization framework. The motivation lies in the advantage of enhanced contrast differentiation, improved noise resolution tradeoffs per anatomical region, and provide a higher quality intensity distribution for transfer-function computation. Such combination is a routine optimization of known CT reconstruction parameters applied to known region-based visualization techniques, yielding predictable improvements in image quality. Further it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Dorn’s material decomposition overlay visualization into Sudarsky’s contrast-optimized rendering framework. The motivation lies in the advantage of material decomposition providing enhanced compositional differentiation between materials (e.g., iodine vs soft tissue), further yielding predictable results in improved diagnostic clarity and structural/compositional visualization.
Sudarsky in view of Dorn does not disclose a keV setting for displaying the respective segmented anatomical region.
In the same art of multi-energy computed tomography imaging, Worstell discloses a keV setting for displaying the respective segmented anatomical region (Par. 0020).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Worstell having keV settings for displaying anatomical regions with Sudarsky’s and Dorn’s volume rendering system for medical imaging. The motivation lies in the advantage of dynamically adjusting energy levels per anatomical region, improving image visualization and accuracy. The combination yields predictable results, since different ROIs often exhibit different tissue composition and attenuation properties, and therefore, would benefit from being imaged at different energy levels (keV), and DECT being a common practice in CT systems.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sudarsky et al. (US 20190147639), hereinafter referred to as “Sudarsky”, in view of Min (US 20220392065).
Regarding claim 18, Sudarsky discloses the CT system of claim 14, and further discloses the second contrast- optimized image generated from previous scan data stored in a picture archiving and communications system (PACS) coupled to the CT system (Par. 0030; the scan or voxel data is stored, such as in a picture archiving communications system (PACS) or patient medical record database. The stored scan data is loaded from memory for rendering).
Sudarsky does not disclose to generate an automated report comparing the contrast-optimized image with a second contrast-optimized image,
In the same art of CT imaging, Min discloses generate an automated report (Par. 0013; Patient-specific medical report) comparing the contrast-optimized image (Par. 0204; the medical image comprises one or more of a contrast-enhanced CT image) with a second contrast-optimized image (Par. 0204; the medical image comprises one or more of a contrast-enhanced CT image), (Fig. 23D and Par. 0008; track disease progression. See Par. 0186-0187 on embodiments) in one of the first anatomical region and the second anatomical region of the patient (Par. 0194; the system can be configured to utilize a plaque identification algorithm to identify and/or analyze one or more regions of plaque within the medical image…plurality of medical images wherein one or more vessels, coronary arteries, and/or regions of plaque), the progression of the disease determined by comparing a first size of a first area of diseased tissue in the contrast-optimized image with a second size of a second area of diseased tissue in the second contrast-optimized image (Par. 0303; the system can be configured to determine and/or use the location, size, shape, diffusivity and/or the attenuation radiodensity of one or more regions of calcified plaque to determine the cause of an increase in calcium score).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Min into the contrast-optimized imaging method of Dorn in view of Hoffman in order to generate an automated report comparing contrast-optimized images and track progression of diseases. The motivation lies in the advantage of automatically quantifying and tracking changes in anatomical regions over time. Reporting features to contrast-optimized and color-overlayed images yield predictable results in improving CT imaging by providing users with measurements, data, and visual/textual interpretation for subsequent use.
Claim(s) 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dorn et al. "Towards context‐sensitive CT imaging—organ‐specific image formation for single (SECT) and dual energy computed tomography (DECT)." Medical physics 45.10 (2018): pages 4541-4557 (hereinafter “Dorn”), in view of Hofmann et al. (DE 102019210473), hereinafter “Hofmann”, in further view of Min (US 20220392065).
Regarding claim 19, Dorn discloses performing a scan of the patient using a computed tomography (CT) system (Section 2.A, Pg. 4543; Our data pool include 42 contrast-enhanced patient DECT scans in the arterial and portal venous phase with varying clinical indications. We used 30 scans for training…), and reconstructing a first image from projection data acquired during the scan (Section 2.C.1, Pg. 4544; basis image most suitable for the organ, tissue type, and clinical indication is chosen automatically from the set of B pre-reconstructed basis image fb® on a per-voxel basis…reconstruct the basis images using varying reconstruction methods…);
segmenting a plurality of anatomical regions of the reconstructed image (Section 1-2.A, Pg. 4542-4543; 1. Perform an automatic multiorgan segmentation (Section 2.A) in varying anatomical regions using a cascaded three-dimensional (3D) fully convolutional neural network (CNN)…the dataset is segmented into the organs liver, left and right kidney, spleen aorta, and left and right lungs…);
after the segmentation is finished, applying a set of one or more algorithms to the reconstructed image to adjust display parameter settings of the CT system individually for each of the plurality of anatomical of the patient (Section 2.B; Formula (1)-(3)),
to generate a first contrast-optimized image showing at least a first anatomical region of the plurality of anatomical regions in a first desired contrast (Section 1 and 2.B, Pg. 4543; Transform the segmentation result to tissue-related weighting coefficients (Section 2.B). The binary-segmented masks are converted to weights that introduce smooth transition zones between the different anatomical regions…Use the tissue-related coefficients to allow for individual settings for each anatomical region…Section 3.C.3, Pg. 4551; the aorta and the heart, is windowed with an angiography window. This window reduces the bright iodine contrast in particular in the heart and aorta. The liver window is narrower than the applied body window I in order to improve the soft tissue contrast and therefore to highlight the liver vessels. The third CS window III aims at maximizing the visual contrast…),
the first desired contrast based on a first assessed organ perfusion status of a contrast at a first anatomical reference point within the first anatomical region (Section 3.D. 1; mean values of the iodine content in five ROIs that are placed in different anatomical structures, that is, aorta, lung, spleen, kidney and liver…Table III…values are used as reference iodine concentrations to evaluate the automatic patient-specific calibration and Fig. 12; perfused blood volume in the lung (LungPBV)),
and a second anatomical region of the plurality of anatomical regions in a second desired contrast (Section 1 and 2.B, Pg. 4543; Transform the segmentation result to tissue-related weighting coefficients (Section 2.B). The binary-segmented masks are converted to weights that introduce smooth transition zones between the different anatomical regions…Use the tissue-related coefficients to allow for individual settings for each anatomical region…Section 3.C.3, Pg. 4551; the aorta and the heart, is windowed with an angiography window. This window reduces the bright iodine contrast in particular in the heart and aorta. The liver window is narrower than the applied body window I in order to improve the soft tissue contrast and therefore to highlight the liver vessels. The third CS window III aims at maximizing the visual contrast…),
the second desired contrast based on a second, different assessed organ perfusion status of the contrast agent at a second anatomical reference point within the second anatomical region, the second desired contrast different from the first desired contrast (Section 3.D. 1; mean values of the iodine content in five ROIs that are placed in different anatomical structures, that is, aorta, lung, spleen, kidney and liver…Table III…values are used as reference iodine concentrations to evaluate the automatic patient-specific calibration and Fig. 12; perfused blood volume in the lung (LungPBV)),
adjusting the display parameter settings of the CT system (Table 1 Window settings; Center (HU), Width (HU)) individually for each of the plurality of anatomical regions of the patient in the second image (Pg. 4542-4543; Using the provided segmentation method, the dataset is segmented into the organs liver, left and right kidney, spleen, aorta, and left and right lungs), to generate a second contrast-optimized image showing at least the first anatomical region in the first desired contrast, and the second anatomical region in the second desired contrast (Section 3.C.3, Pg. 4551; the aorta and the heart, is windowed with an angiography window. This window reduces the bright iodine contrast in particular in the heart and aorta. The liver window is narrower than the applied body window I in order to improve the soft tissue contrast and therefore to highlight the liver vessels. The third CS window III aims at maximizing the visual contrast…).
Dorn does not disclose, retrieving a second image of the patient from a picture archiving and communications system (PACS) coupled to the CT system.
In the same art of imaging in computed tomography, Hofmann discloses retrieving a second image of the patient from a picture archiving and communications system (PACS) coupled to the CT system (Par. 0051; A comparison dataset can be provided using a RIS, whereby the comparison dataset can be recorded directly or retrieved from a PACS).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the use of PACS as taught by Hofmann into the method of Dorn, in order to retrieve previously acquired contrast-optimized image for comparison. The motivation lies in the routine and standard practice of using PACS systems in radiology, allowing for storing, managing, and retrieving CT data.
Dorn in view of Hofmann does not disclose a method for visualizing a progression of a disease of a patient, comparing a first size of an area of diseased tissue in the first anatomical region of the first contrast-optimized image with a second size of a the area of diseased tissue in the first anatomical region of the second contrast-optimized image to determine the progression of the disease, generating a visualization showing the first contrast-optimized image side-by-side with the second contrast-optimized image, the visualization including a measured difference between the first size and the second size, and sending an automated report including the visualization to a user of the CT system.
In the same art of imaging in computed tomography, Min discloses a method for visualizing a progression of a disease of a patient (Par. 0007; systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking),
comparing a first size of an area of diseased tissue in the first anatomical region of the first contrast-optimized image with a second size of the area of diseased tissue in the first anatomical region of the second contrast-optimized image to determine the progression of the disease (Par. 0303; the system can be configured to determine and/or use the location, size, shape, diffusivity and/or the attenuation radiodensity of one or more regions of calcified plaque to determine the cause of an increase in calcium score),
generating a visualization showing the first contrast-optimized image side-by-side with the second contrast-optimized image, the visualization including a measured difference between the first size and the second size (Fig. 5-9),
and sending an automated report including the visualization to a user of the CT system (Par. 013, Fig. 5A-5B; medical report for a patient).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Min into the contrast-optimized imaging method of Dorn in view of Hoffman in order to generate an automated report comparing contrast-optimized images and track progression of diseases. The motivation lies in the advantage of automatically quantifying and tracking changes in anatomical regions over time. Reporting features to contrast-optimized and color-overlayed images yield predictable results in improving CT imaging by providing users with measurements, data, and visual/textual interpretation for subsequent use.
Regarding claim 20, Dorn in view of Hofmann in further view of Min discloses the method of claim 19, and further discloses Dorn the one or more superimposed colorized overlays based on material decomposition data generated from the scan (Dorn Pg, 4551-4552, Section 3.D. and Fig. 11; Within one single DE image, we combine material decomposition and classification tasks and are able to show color overlays wherever appropriate).
Dorn does not disclose wherein the first contrast-optimized image and the second contrast-optimized image include one or more superimposed colorized overlays showing healthy tissues of the patient in a first color, and diseased tissues of the patient in a second color.
In the same art of CT imaging, Hofmann discloses wherein the first contrast-optimized image and the second contrast-optimized image include one or more superimposed colorized overlays (Par. 0031; It is preferable to display different compartments, i.e. areas in which different tissue characteristics were detected during processing, differently, e.g. with different colors or a different texture. It is conceivable that the information could be presented as a color-overlaid edema content on the gray-white images of a CT scan of the brain) showing healthy tissues of the patient in a first color (Par. 0014; suppressed healthy tissue), and diseased tissues of the patient in a second color (Par. 0011; The method according to the invention relates to an improvement in CT imaging and serves to better distinguish between different types of tissue in an object. In particular, diseases that are associated with a change in the water content (edema formation) can be recognized in regions with different types of tissue that are close together. The disease can spread to both types of tissue and be of different therapeutic or diagnostic relevance there, e.g. in tumor diseases or fractures).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate the teachings of Hofmann having colorized overlays to distinguish healthy tissue from diseased tissue with the contrast-optimized image techniques taught by Dorn. The motivation lies in the advantage of improved diagnostic clarity and visual differentiation, more specifically in identifying diseased tissue (Hofmann Par. 0031.; This overlay advantageously provides essential information with regard to the evaluation of the edema, since the significance of the edema differs significantly in the two substances). Applying color coded overlays to identify specific regions, more specifically healthy and diseased tissues, is a routine enhancement in medical imaging visualization, a predictable use of known techniques in imaging practice.
Conclusion
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/JENNY N TRAN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615