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 .
Claim Rejections - 35 USC § 103
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) 1-8, 10, 15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Reynolds (US 20170262978 A1) in view of Benech (US 20250299824 A1).
Regarding claim 1, Reynold teaches A medical image processing apparatus comprising processing circuitry configured to (Para: 19, and 21-22: Reynold describes a medical image processing system including processing circuitry configured to process volumetric medical imaging data): receive a slab comprising a plurality of samples determined by a camera model ( Para: 29, 63-67, 71: Reynold describes receiving a three dimensional medical imaging dataset comprising voxels and sampling points along ray paths); determine whether one or more first samples of the plurality of samples are part of an anatomical region of interest (Para 38: 96-97: Reynolds performs segmentation and identification of anatomical structures such as ribs or spine within the medical image volume); project the slab along a view direction onto an image plane to form an image( Para 63,69, and 74: Reynold describes casting rays along ray paths through volume dataset and computing image values through projection techniques such as maximum intensity projection) wherein in response to a determination that the one or more first samples are part of the anatomical region of interest, the processing circuitry is configured to (Para 38, and 96-97).
Reynolds fails to teach project the one or more first samples using a first projection mode; and project one or more second samples of the plurality of samples that are not part of the anatomical region of interest using a second projection mode.
Benech teaches the project the one or more first samples using a first projection mode ( Para.225: teaches a first projection); and project one or more second samples of the plurality of samples that are not part of the anatomical region of interest using a second projection mode (Para.225:teaches a second projection and each of them include a different intensity projection. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the volume rendering technique of Reynolds to incorporate the first and second projection approach based rendering approach taught by Benech in order to improve visualization efficiency and emphasize clinically relevant anatomical structures ).
Regarding claim 2, Reynolds in view of Benech teaches the apparatus according to claim 1, wherein the first and second projection modes are the same or different (Para.225:teaches a first and second projection and each of them include a different intensity projection).
Regarding claim 3, Reynolds in view of Benech teaches The apparatus of claim 1, wherein the first projection mode comprises at least one of: a minimum intensity projection (MinIP) algorithm, a maximum intensity projection (MIP) algorithm and an average intensity projection (AveIP) algorithm and the second projection mode comprises at least one of: a minimum intensity projection (MinIP) algorithm, a maximum intensity projection (MIP) algorithm and an average intensity projection (AveIP) algorithm ( Reynolds, Para. 71-74: discloses generating projection images from volumetric medical imaging datasets using maximum intensity projection techniques. The same projection algorithm can also be applied to other voxel samples within the dataset).
Regarding claim 4, Reynolds in view of Benech teaches The apparatus according to claim 2, wherein when the first and second projection modes are different at least one of the first and second projection modes comprises a minimum intensity projection (MinIP) algorithm and at least one other of the first and second projection modes comprises a maximum intensity projection (MIP) algorithm (Reynolds, Para. 71-74: discloses generating projection images from volumetric medical imaging data using MIP techniques. This can be applied to one projection. Minimum intensity projection is a well-known complementary projection technique used in the same volume rendering systems for highlighting low intensity structures. Benech. Para.225:teaches a second projection and each include a different one of a maximum intensity projection, an average intensity projection, a minimum intensity projection, a sum intensity projection, a median intensity projection, and a standard deviation intensity projection”).
Regarding claim 5, Reynolds in view of Benech teaches The apparatus according to claim 2, wherein when the first and second projections modes are the same, the processing circuitry is configured to project the one or more first samples separately from the one or more second samples (Reynolds, Para. 63-67 and 71-74: describes casting rays through volumetric data. Sampling voxels along the rays and computing projection values based on selected voxel samples. Additionally, Reynold describes identifying specific anatomical structures and manipulating those voxels during rendering) .
Regarding claim 6, Reynolds in view of Benech teaches The apparatus according to claim 2, wherein when the first and second projections modes are the same, the first and second projection modes each comprise an average intensity projection (AveIP) algorithm (Reynolds, Para.63-67: teaches sampling voxel intensities along ray paths through volumetric medical dataset and computing projection values to generate projection images. Average intensity projection is a known projection technique that computes the average intensity of sampled voxels along the ray. It would have been obvious to a person of ordinary skill in the art to use average intensity projection as an alternative projection algorithm when computing projection images.
Regarding claim 7, Reynolds in view of Benech teaches The apparatus according to claim 1, wherein: a voxel value associated with the one or more first samples is higher than a voxel value associated with the one or more second samples; or the voxel value associated with the one or more first samples is lower than the voxel value associated with the one or more second samples (Reynolds, Para 63-67: describes volumetric datasets composed of voxels having intensity values and performing projection rendering by sampling voxel intensities along ray paths. Reynold, Para: 71-74: further disclose Maximum intensity projection techniques in which voxel intensities are compared to determine the projection value. Because voxel intensity values vary across the dataset voxel values associated with some samples are inherently higher or lower than voxel values associated with other samples).
Regarding claim 8, Reynolds in view of Benech teaches The apparatus according to claim 1, wherein the processing circuitry is configured to define at least one of: a threshold of a first measure of the anatomical region of interest; a range of the first measure of the anatomical region of interest; or a value of interest of the first measure of the anatomical region of interest (Reynolds, Para. 92: “ the ribs are found using an intensity threshold”).
Regarding claim 10, Reynolds in view of Benech teaches The apparatus according to claim 8, wherein the first measure of the anatomical region of interest comprises at least one of: a dimension or size of the anatomical region of interest; a vesselness of the anatomical region of interest or vessel branching of the anatomical region of interest; and a textural measure (Reynolds, Para: 46-47 discloses identifying anatomical structure using intensity-based analysis across the structure. Determining anatomical structures within such dataset inherently involves measuring structural characteristics such as size or dimensions.).
Regarding claim 15, Reynolds in view of Benech teaches The apparatus according to claim 1, wherein the processing circuitry is configured to determine whether the one or more first samples of the plurality of samples are part of the anatomical region of interest based on a segmentation mask of at least a part of the anatomical region of interest (Reynolds, Para 47 and 92: describes identifying anatomical structures such as ribs within a medical imaging dataset are segmented using intensity thresholds).
Regarding claim 19, Reynolds in view of Benech teaches The apparatus of claim 8, wherein the anatomical region of interest comprises a space or gap and the first measure comprises a size or dimension of the space or gap (Reynolds, Para. 45-47: determines points corresponding to anatomical structures and analyzes interactions along rays passing through the anatomical structure. Measuring distances between anatomical boundaries inherently identifies spaces or gaps between anatomical structure).
Regarding claim 20, Reynolds teaches A medical image processing method comprising: receiving a slab comprising a plurality of samples determined by a camera model (Para: 19, and 21-22: Reynold describes a medical image processing system including processing circuitry configured to process volumetric medical imaging data): receive a slab comprising a plurality of samples determined by a camera model ( Para: 29, 63-67, 71: Reynold describes receiving a three dimensional medical imaging dataset comprising voxels and sampling points along ray paths); determine whether one or more first samples of the plurality of samples are part of an anatomical region of interest (Para 38: 96-97: Reynolds performs segmentation and identification of anatomical structures such as ribs or spine within the medical image volume); project the slab along a view direction onto an image plane to form an image( Para 63,69, and 74: Reynold describes casting rays along ray paths through volume dataset and computing image values through projection techniques such as maximum intensity projection) wherein in response to a determination that the one or more first samples are part of the anatomical region of interest, the processing circuitry is configured to (Para 38, and 96-97).
Reynold fails to teach project the one or more first samples using a first projection mode; and project one or more second samples of the plurality of samples that are not part of the anatomical region of interest using a second projection mode.
Benech teaches the project the one or more first samples using a first projection mode ( Para.225: teaches a first projection); and project one or more second samples of the plurality of samples that are not part of the anatomical region of interest using a second projection mode (Para.225:teaches a second projection and each of them include a different intensity projection. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the volume rendering technique of Reynolds to incorporate the first and second projection approach based rendering approach taught by Benech in order to improve visualization efficiency and emphasize clinically relevant anatomical structures ).
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Reynolds (US 20170262978 A1) in view of in view of Benech (US 20250299824 A1)in further view of Boucneau (US-20250138709-A1)
Regarding claim 9, Reynolds in view of Benech teaches The apparatus according to claim 8, wherein the threshold, but fails to teach the range or the value of interest of the first measure is defined based on a selected window width and/or window level to be applied to the image.
Boucneau teaches the range or the value of interest of the first measure is defined based on a selected window width and/or window level to be applied to the image (Para.64: discloses” a region of interest can be displayed with… a different window width or window level”. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the system of Reynolds is view of Benech to incorporate the adjusting visualization of a region using selected window width and window level parameters. This combination would improve visualization and interpretation of regions of interest within medica imaging datasets).
Claim(s) 18 is rejected under 35 U.S.C. 103 as being unpatentable over Reynolds (US 20170262978 A1) in view of in view of Benech (US 20250299824 A1)in further view of Yerebakan (US-20250285284-A1).
Regarding claim 18, Reynolds in view of Benech teaches The apparatus of claim 1, wherein one or more pixels of the image are associated with the one or more first samples but fails to teach wherein the one or more pixels define a mask for use in a masking process of another image or for overlaying on another image .
Yerebakan teaches wherein the one or more pixels define a mask for use in a masking process of another image or for overlaying on another image ( Para.65: discloses outputting a segmentation mask and visualizing the segmentation mask over a source image as an overlay. therefore, the segmentation mask may define pixels used for masking or overlaying another image. It would have been obvious to apply the overlay/visualization techniques of Yerebakan to the segmented anatomical regions identified in Reynolds system in order to improve visualization of anatomical structures in medical images).
Allowable Subject Matter
Claims 11 and its dependents 12 - 14, and 16-17 are objected to as allowable subject matter. The following is a statement of reasons for the indication of allowable subject matter: None of the prior art teaches such limitations “wherein the processing circuitry is configured to at least one of: determine a second measure of a candidate anatomical region, the candidate anatomical region comprising the one or more first samples, or receive a predetermined second measure of the candidate anatomical region; and determine whether the second measure is below the threshold of the first measure, is within the range of the first measure or corresponds to the value of interest of the first measure”.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Piccand (S. Piccand, R. Noumeir and E. Paquette, "Region of Interest and Multiresolution for Volume Rendering," in IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 5, pp. 561-568, Sept. 2008, doi: 10.1109/TITB.2007.907986.) Discloses a similar method of projection by using a high full rendered ROI and another projection of context around the roi in a lower resolution.
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/LATRELL ANTHONY CREARY/Examiner, Art Unit 2613
/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613