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 .
Claim Status
Claims 1-18 are currently pending in the application filed 09/23/2024.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 on 12/06/2024
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 9/23/2024 have been considered by the Examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recatal of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier, as explained in MPEP § 2181, subsection I (note that the list of generic placeholders below is not exhaustive, and other generic placeholders may invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph):
A. The Claim Limitation Uses the Term "Means" or "Step" or a Generic Placeholder (A Term That Is Simply A Substitute for "Means")
With respect to the first prong of this analysis, a claim element that does not include the term "means" or "step" triggers a rebuttable presumption that 35 U.S.C. 112(f) does not apply. When the claim limitation does not use the term "means," examiners should determine whether the presumption that 35 U.S.C. 112(f) does not apply is overcome. The presumption may be overcome if the claim limitation uses a generic placeholder (a term that is simply a substitute for the term "means"). The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation, and likewise there is no fixed list of words that always avoid 35 U.S.C. 112(f) interpretation. Every case will turn on its own unique set of facts.
Such claim limitation(s) is/are:
"Input interface", “first segmentation unit”, “second segmentation unit”, and a “mask unit” in claims 12 and dependent claims described in paragraph [00100] ( “In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuity such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.”)
"Scanning Unit", and a “control device” in claims 13 and dependent claims described in paragraph [00100] ( “In addition, or alternative, to that discussed above, units and/or devices according to one or more example embodiments may be implemented using hardware, software, and/or a combination thereof. For example, hardware devices may be implemented using processing circuity such as, but not limited to, a processor, Central Processing Unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a System-on-Chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. Portions of the example embodiments and corresponding detailed description may be presented in terms of software, or algorithms and symbolic representations of operation on data bits within a computer memory. These descriptions and representations are the ones by which those of ordinary skill in the art effectively convey the substance of their work to others of ordinary skill in the art. An algorithm, as the term is used here, and as it is used generally, is conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of optical, electrical, or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.”)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 10, and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features), and Jahne (Novel Techniques for Automatically Enhanced Visualization of Coronary Arteries in MSCT Data and for Drawing Direct Comparisons to Conventional Angiography)
Regarding claim 1,
Zheng teaches:
A method for generating segmented, masked 4D image data of a heart, the method comprising:
(Zheng, [Abstract]; “an automatic system that segments the four heart chambers from cardiac CT volumes”)
providing a 4D image of a heart of a patient, the 4D image having 4D image data of the heart of the patient;
(Zheng, [Abstract]; “the heart from cardiac computed tomography (CT) volumes”, Fig 2)
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generating first segmented 4D image data based on the 4D image data, wherein a heart wall of a ventricle (Zheng, [Abstract]; “important landmarks (such as the valves and the ventricular septum cusps)”) is segmented; (Zheng, [Abstract]; “heart modeling and automatic model fitting to an unseen volume. …anatomically accurate, allow manual editing, and provide sufficient information to guide automatic detection and segmentation. … automatic heart chamber segmentation in 3-D CT volumes.”)
generating second segmented 4D image data (Zheng, [Abstract]; “boundary delineation”) based on the first segmented 4D image data (Zheng, [Abstract], “After determining the pose of the heart chambers”), wherein an epicardium and an endocardium of the ventricle are segmented (Zheng, [Page 1670]; “Both the endo- and epi-cardiums are delineated”);
Zheng fails to teach:
generating the segmented, masked 4D image data based on the second segmented 4D image data, wherein an interior of the ventricle is covered.
Jahne teaches:
generating the segmented, masked 4D image data based on the second segmented 4D image data, wherein an interior of the ventricle is covered.
(Jahne, [Abstract]; “an advanced masking method for eliminating large cardiac cavities to obtain a better visibility of the coronary arteries in the rendered CT data”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng with Jahne. The motivation for the combination is to generate masked image data. (Jahne, [Abstract]; “advanced masking7 method”)
Regarding claim 2,
The combination of Zheng and Jahne teaches:
wherein the ventricle comprises a left ventricle. (Zheng, Fig 1)
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Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to apply the method to the left ventricle, whose function is clinically most significant, so that its wall motion can be analyzed (Zheng, [Page 1669]; "Since the LV is clinically more important than other chambers").
Regarding claim 3,
the combination of Zheng, and Jahne teaches:
wherein the generating the first segmented 4D image data includes producing a mask (“Jahne, [Page 291, Material and Methods]; “an advanced masking of large cardiac cavities”) of the heart wall of the ventricle (Zheng, [Abstract]; “important landmarks (such as the valves and the ventricular septum cusps)”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to mark the region relevant to the analysis by producing a mask of the ventricle wall, so that the heart walls and surfaces can be registered more reliably (Jahne, [Page 291]; "an advanced masking of large cardiac cavities").
Regarding claim 4,
the combination of Zheng, and Jahne teaches:
wherein the producing produces the mask by applying a four-chamber segmentation algorithm to the 4D image data. (Zheng, [Abstract]; “an automatic system that segments the four heart chambers from cardiac CT volumes”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to produce the mask by segmenting the left ventricle from the other chambers using a four-chamber segmentation algorithm, so that the chamber of interest is isolated for the masking step (Zheng, [Abstract]; "an automatic system that segments the four heart chambers from cardiac CT volumes").
Regarding claim 10,
the combination of Zheng, and Jahne teaches:
wherein at least one of the generating the second segmented 4D image data generates the second segmented 4D image data by highlighting a contrast of the epicardium and endocardium of the ventricle, or the generating the segmented, masked 4D image data includes producing and inverting an intraventricular mask.
(Jahne, [page 293], "the resulting binary mask eliminates large structures correlating with the cardiac cavities");
(Jahne, [page 291], "binary mask (3D) to eliminate cardiac cavities")
Examiner Note: Producing and inverting an intraventricular mask" is read in light of Spec. [0061], which states the step is "the production and inversion of a mask for the intraventricular blood lumen," and that the mask "is inverted so that the entire blood-filled volume or blood lumen is covered." Jahne produces a binary mask correlating with the intraventricular cardiac cavities and applies it so the cavities are eliminated from the output (Jahne, [page 293], "Our approach for masking out spurious cardiac cavities"). Applying a cavity mask so the cavity region is removed is the inverted use of that mask, accomplishing the same coverage of the intraventricular volume described at Spec. [0061].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to cover the contrast-enhanced intraventricular cavity by producing a mask of that cavity and applying it so the cavity region is removed from the resulting image data (Jahne, [Page 293], "By applying the generated mask to the CT data after extracting the heart").
Regarding Claim 12
Zheng teaches:
an input interface configured to receive 4D image data of a heart of a patient (Zheng, [page 13], "By performing heart segmentation frame by frame, the heart motion is tracked");
Examiner Note: Zheng discloses a computer-implemented segmentation system that takes a cardiac CT volume as its input (Zheng, [page 1], "automatic four-chamber heart segmentation system"), and that input is 4D/time-resolved because the system is applied frame by frame across the cardiac cycle (Zheng, [page 13], "By performing heart segmentation frame by frame, the heart motion is tracked"). Receiving the cardiac CT volume into Zheng's system is therefore the receiving of 4D image data of a heart by an input interface, corresponding to the input interface 51 described at Spec. [0075].
a first segmentation unit configured to generate first segmented 4D image data based on the 4D image data, wherein a heart wall of a ventricle of the heart is segmented (Zheng, [page 1], "automatic heart chamber segmentation in 3D CT volumes").
Examiner Note: Zheng segments the heart chambers in cardiac CT, including the left ventricle bounded by its delineated epicardium and endocardium (Zheng, [page 1], "automatic heart chamber segmentation in 3D CT volumes"; [page 4], "Both the endo- and epicardiums are delineated for the LV"). The region between the delineated epicardium and endocardium is the ventricle wall (the myocardium), so Zheng's segmentation of the LV chamber segments the heart wall of the ventricle, corresponding to the first segmentation that produces the wall region described at Spec. [0023] and [0051].
a second segmentation unit configured to generate second segmented 4D image data based on the first segmented 4D image data, wherein an epicardium and an endocardium of the ventricle are segmented
(Zheng, [page 4], "Both the endo- and epicardiums are delineated for the LV
(Zheng, [page 2], "green for the left ventricle (LV) endocardium, magenta for the LV epicardium").
Examiner Note: Zheng delineates both the epicardium and the endocardium of the left ventricle in cardiac CT (Zheng, [page 4], "Both the endo- and epicardiums are delineated for the LV"; [page 2], "green for the left ventricle (LV) endocardium, magenta for the LV epicardium"). This delineation is performed on the localized/segmented heart from the prior step, so Zheng's separate delineation of the LV endocardium and epicardium is the generating of second segmented data in which the epicardium and endocardium of the ventricle are segmented, corresponding to the second segmented 4D image data described at Spec. [0026].
Zheng fails to teach
and a mask unit configured to generate segmented, masked 4D image data based on the second segmented 4D image data, wherein an interior of the ventricle is covered.
Jahne teaches:
a mask unit configured to generate segmented, masked 4D image data based on the second segmented 4D image data, wherein an interior of the ventricle is covered
(Jahne, [page 293], "the resulting binary mask eliminates large structures correlating with the cardiac cavities");
(Jahne, [page 291], "binary mask (3D) to eliminate cardiac cavities").
Examiner Note: Jahne produces a binary mask of the intraventricular cardiac cavities and applies it so those cavities are removed from the image data (Jahne, [page 293], "the resulting binary mask eliminates large structures correlating with the cardiac cavities"; [page 291], "binary mask (3D) to eliminate cardiac cavities"). Removing the cavity region in this way covers the interior of the ventricle, corresponding to the masking that covers the intraventricular volume described at Spec. [0061] and [0074].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng with Jahne. The motivation for the combination is to provide a mask unit that covers the interior of the ventricle by producing a mask of the contrast-enhanced cavity and applying it so the cavity region is removed from the resulting image data (Jahne, [Page 293], "By applying the generated mask to the CT data after extracting the heart”).
Regarding claim 13,
the combination of Zheng, and Jahne teaches:
A medical imaging system, comprising: a scanning unit to capture measurement data of the patient; (Jahne, [page 291], "acquired by 16-slice and 64-slice CT scanners").
Examiner Note: Jahne acquires the cardiac data on a CT scanner, which necessarily includes a scanning unit that captures the X-ray measurement data from the patient, corresponding to the scanning unit 82 described at Spec. [0089].
a control device to control [the] scanning unit and to generate the 4D image data based on the measurement data; and (Jahne, [page 291], "The gray level values of the CT data are normalized values of the computed X-ray attenuation coefficients").
Examiner Note: The CT image data of Jahne is computed from the measured X-ray attenuation, so the CT scanner inherently includes a control device that controls the scan and generates (reconstructs) the image data from that measurement data, corresponding to the control device 81 and reconstruction unit 86 described at Spec. [0089].
the image data generating device of claim 12. (Zheng, [page 1], "automatic four-chamber heart segmentation system"); see also (Jahne, [page 293], "the resulting binary mask eliminates large structures correlating with the cardiac cavities").
Examiner Note: The combination of Zheng and Jahne teaches the image data generating device of claim 12, with Zheng providing the input interface and the segmentation units and Jahne providing the mask unit.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to provide the segmentation and masking device as part of the CT imaging system that captures the measurement data and reconstructs the image data, so that the acquired cardiac CT data is processed by the device within a single imaging system (Jahne, [Page 291], "acquired by 16-slice and 64-slice CT scanners").
Regarding claim 14,
the combination of Zheng, and Jahne teaches:
A non-transitory computer program product with a computer program [that], when executed by a medical imaging system, cause[s] the medical imaging system to perform the method of claim 1. (Zheng, [Page 11]; “After code optimization and using multithreading techniques, we achieved an average speed of 4.0 s for the automatic segmentation of all four chambers on a computer with a dual-core 3.2-GHz processor and 3-GB memory.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to implement the segmentation and masking method as a computer program executed on the imaging system's processor, so that the method of claim 1 is performed automatically and efficiently (Zheng, [Page 11]; "we achieved an average speed of 4.0 s for the automatic segmentation of all four chambers").
Regarding claim 15,
the combination of Zheng, and Jahne teaches:
A non-transitory computer-readable medium on which are stored program sections [that, when] executed by a medical imaging system, cause the medical imaging system to perform the method of claim 1. (Zheng [Page 11]; “After code optimization and using multithreading techniques, we achieved an average speed of 4.0 s for the automatic seg mentation of all four chambers on a computer with a dual-core 3.2-GHz processor and 3-GB memory.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne. The motivation for the combination is to store the segmentation and masking program on a computer-readable medium so that, when executed on the imaging system's processor, it performs the method of claim 1 automatically and efficiently (Zheng, [Page 11]; "we achieved an average speed of 4.0 s for the automatic segmentation of all four chambers").
Claims 5-9, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng, Jahne further in view of NIH (MIPAV User’s Guide)
Regarding claim 5,
the combination of Zheng, and Jahne fails to teach:
generating a contrast-rich mask image, wherein
voxels within a mask are determined,
gray values within the mask deviate from a mean value by no more than a predetermined value, and the
gray values are mapped onto contrast-rich gray values, the contrast-rich gray
values are between the gray value 0 and a sum of the mean value and the predetermined value.
NIH teaches:
generating a contrast-rich mask image, wherein (NIH, [Page 56]; “Generates a mask, with a specified intensity, of the region inside or outside the contoured VOIs that are delineated on an image”)
voxels within a mask are determined, (NIH, [Page 175]; “voxels are 0 for background, 1 for surface, and 2 for interior”)
gray values within the mask deviate from a mean (NIH, [Page 702]; “average pixel intensity”) value by no more than a predetermined value, and the (NIH, [Page 702]; “Average pixel intensity equals Sum pixel intensities divided to Number of pixels”)
Examiner Note: A gray value is the single intensity number assigned to a voxel in a grayscale image.
gray values are mapped (NIH, [Page 389]; “For the adjusted image, drag the image intensity line for the red channel up in the middle.”) onto contrast-rich gray values,
(NIH, [Page 358]; “when the image is divided into more regions, the brightest pixel in the region gets remapped as one of the brightest pixels in the image regardless of its absolute intensity in the image. Likewise, the darkest pixel in the region is remapped as one of the darkest in the image regardless of its absolute intensity.”)
(NIH, [Page 348]: “Also, areas of low intensity and low contrast in the source image were correspondingly remapped in the reference image to areas of higher contrast and generally overall brighter values.”)
the contrast-rich gray values are between the gray value 0 (NIH, [Page 712]; “Lower limit – end of threshold range;”) and a sum of the mean value (NIH, [Page 702]; "Average pixel intensity equals Sum pixel intensities divided to Number of pixels") and the predetermined value (NIH, [Page 700]; “Standard Deviation Threshold)
(NIH, [Page 712]; “Upper Limit – beginning of threshold range;”).
(NIH, [Page 700]; “Standard Deviation Threshold… such as a number of standard deviations and/or values outside the range”)
Examiner Note: The "sum" is just the top of the output range, found by adding the predetermined value to the mean. NIH discloses both pieces, the mean as its average pixel intensity (NIH, [Page 702]) and the predetermined value as its number of standard deviations (NIH, [Page 700]), and remaps the in-mask gray values into a range with an upper limit set from the mean and the standard deviation (NIH, [Page 712]).
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne with NIH. The motivation for the combination is to better utilize the scale of brightness values within the mask area so that textures and contrasts are made clearer (NIH, [Page 358]; "the brightest pixel in the region gets remapped as one of the brightest pixels in the image ... the darkest pixel in the region is remapped as one of the darkest").
Regarding claim 6,
the combination of Zheng, Jahne, and NIH teaches:
the predetermined value is two standard deviations from the mean value. (NIH, [Page 701]; “Number of standard deviations, … the Number of standard deviations parameter.”)
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng, Jahne, and NIH. The motivation for the combination is to define the in-mask value range using two standard deviations about the mean, since that range encompasses approximately 95 percent of the values of a normal distribution (NIH, [Page 701]; "Number of standard deviations").
Regarding claim 7,
the combination of Zheng, Jahne and NIH teaches:
wherein the generating the first segmented 4D image data generates the first segmented 4D image data by combining the contrast-rich mask image with the 4D image data using weighting (Jahne, [Page 292]; "By applying the generated mask to the original CT data").
Examiner Note: Jahne combines the mask with the image data by applying the generated mask to the original CT data to produce masked CT data, the mask values weighting the image data on a voxel basis, which reads on combining the contrast-rich mask image with the 4D image data using weighting under the broadest reasonable interpretation, corresponding to Spec. [0055] and [0073].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng, Jahne and NIH. The motivation for the combination is to retain the original image information while improving the contrast within the mask area, by combining the contrast-rich mask image with the original 4D image data (Jahne, [Page 292]; "By applying the generated mask to the original CT data").
Regarding claim 8,
the combination of Zheng, Jahne, and NIH teaches:
wherein the weighted combination is effected at a predetermined percentage (NIH, [Page 376]; “both images blended in proportion 50/50”)
Examiner Note: The weighted combination is effected at a predetermined percentage" is read in light of Applicant Spec. [0073] ("the weighting factor indicates the percentages at which the increased-contrast data and the original 4D image data should be combined"). NIH’s AlphaBlending combines two images at a set proportion, by default "in proportion 50/50," meaning (NIH, [page 387];"50 percent of each image"), which is a weighted combination of two images effected at a predetermined percentage, corresponding to applicant's specification.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng, Jahne, and NIH. The motivation for the combination is to include a controlled proportion of each image in the result by combining the contrast-rich mask image and the original image data at a predetermined percentage (NIH, [Page 377]; "the AlphaBlending image slider to change the displaying proportion")
Regarding claim 9,
the combination of Zheng, Jahne and NIH renders obvious that:
wherein the percentage between 20 and 40 percent for the contrast-rich mask image is around 30 percent relative to the first segmented 4D image data.
(NIH, page 377; "use the AlphaBlending image slider to change the displaying proportion")
(NIH, [page 387] ;"50 percent of each image")
Examiner Note: The percentage of claim 9 is read in light of Spec. [0057], which describes the value as a preference, stating that the percentage "is preferably around 30 percent" and that at that value "the contrast-rich mask image is weighted at 30 percent and the original 4D image data at 70 percent." NIH teaches that the percentage at which two images are combined is a user-selectable parameter set by the AlphaBlending slider across the full range, with a default of 50 percent of each image.
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng, Jahne and NIH, and to set the contrast-rich mask image weighting at a value between 20 and 40 percent, around 30 percent. The motivation for the combination is that the blend percentage is a result-effective variable that controls how much of the contrast-rich mask image versus the original image data is included in the result, and discovering the optimum or workable value of such a variable involves only routine skill in the art. Applicant has not established that the claimed value is critical or produces unexpected results, describing it only as preferred (NIH, [Page 377]; "use the AlphaBlending image slider to change the displaying proportion").
Regarding claim 16,
the combination of Zheng, and Jahne teaches:
generating a contrast-rich mask image, wherein voxels within a mask are determined
(Jahne, [page 291], "gray level values of the CT data are normalized values of the computed X-ray attenuation coefficients, expressed in Hounsfield Units (HU)");
(Jahne, [page 292], "Otsu's method of automatic threshold selection from gray level histogram for image segmentation ... voxels of the original CT data are dichotomized into three gray level classes").
Examiner Note: Jahne expresses the CT gray values in Hounsfield Units and uses Otsu's method to sort the voxels of the CT data into gray-level classes, which determines the voxels within a mask by gray value, corresponding to the determination of in-mask voxels at Spec. [0053].
The combination of Zheng and Jahne fails to teach:
gray values within the mask deviate from a mean value by no more than a predetermined value, and the gray values are mapped onto contrast-rich gray values, the contrast-rich gray values are between the gray value 0 and a sum of the mean value and the predetermined value.
NIH teaches:
gray values within the mask deviate from a mean value by no more than a predetermined value
(NIH, [page 702]; "Average pixel intensity equals Sum pixel intensities divided to Number of pixels"); (NIH, [page 700]; "Standard Deviation Threshold ... such as a number of standard deviations and/or values outside the range").
Examiner Note: NIH determines the in-mask average pixel intensity (the mean) and limits the values by a standard-deviation threshold, so the gray values are constrained to deviate from the mean by no more than a predetermined value, corresponding to Spec. [0053].
the gray values are mapped onto contrast-rich gray values (NIH, [page 358]; "the brightest pixel in the region gets remapped as one of the brightest pixels in the image ... the darkest pixel in the region is remapped as one of the darkest"), the contrast-rich gray values are between the gray value 0 and a sum of the mean value (NIH, [Page 702]; "Average pixel intensity equals Sum pixel intensities divided to Number of pixels") and the predetermined value (NIH, [Page 700]; “Standard Deviation Threshold)
(NIH, [page 712]; "Lower limit – end of threshold range");
(NIH, [page 712]; "Upper Limit – beginning of threshold range").
Examiner Note: NIH remaps the in-mask gray values so the darkest maps toward 0 and the brightest toward the upper limit of the range, the range being bounded by a lower limit and an upper limit set from the mean and the standard-deviation threshold, which reads on mapping the gray values onto contrast-rich values between 0 and the sum of the mean and the predetermined value, corresponding to Spec. [0053].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne with NIH. The motivation for the combination is to better utilize the scale of brightness values within the mask area so that textures and contrasts are made clearer (NIH, [page 358]; "the brightest pixel in the region gets remapped as one of the brightest pixels in the image ... the darkest pixel in the region is remapped as one of the darkest").
Regarding claim 17,
the combination of Zheng, Jahne and NIH teaches:
wherein the generating the first segmented 4D image data generates the first segmented 4D image data by combining the contrast-rich mask image with the 4D image data using weighting
(Jahne, [page 292], "By applying the generated mask to the original CT data");
(Jahne, [page 291], "combined mask" applied to produce "masked CT data" in Fig. 1).
Examiner Note: Jahne combines the mask with the image data by applying the generated mask to the original CT data to produce masked CT data, the mask values weighting the image data on a voxel basis, which reads on combining the contrast-rich mask image with the 4D image data using weighting under the broadest reasonable interpretation, corresponding to Spec. [0055] and [0073].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng, Jahne and NIH. The motivation for the combination is to retain the original image information while improving the contrast within the mask area, by combining the contrast-rich mask image with the original 4D image data (Jahne, [Page 292], "By applying the generated mask to the original CT data").
Regarding claim 18,
The combination of Zheng, Jahne and NIH teaches
The method of claim 5, wherein at least one of the generating the second segmented 4D image data generates the second segmented 4D image data by highlighting a contrast of the epicardium and endocardium of the ventricle, or the generating the segmented, masked 4D image data includes producing and inverting an intraventricular mask
(Jahne, [page 290], "an advanced masking method for eliminating large cardiac cavities to obtain a better visibility of the coronary arteries");
(Jahne, [page 291], "binary mask (3D) to eliminate cardiac cavities").
Examiner Note: The claim requires only one of the two alternatives. Jahne produces a binary mask of the cardiac cavities (including the ventricles) and applies it to eliminate those cavities from the image data, which meets the second alternative of producing and inverting an intraventricular mask, corresponding to the masking that covers the intraventricular volume at Spec. [0061].
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne with NIH. The motivation for the combination is to cover the contrast-enhanced intraventricular cavity by producing a mask of that cavity and applying it so the cavity region is eliminated from the resulting image data (Jahne, [Page 293], "By applying the generated mask to the CT data after extracting the heart").
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zheng, and Jahne further in view of Peled (Automated 4-dimensional regional myocardial strain evaluation using cardiac computed tomography)
Regarding claim 11,
The combination of Zheng and Jahne fails to teach:
determining a deformation field based on the segmented, masked 4D image data;
determining an intrinsic myocardial motion trajectory based on the determined deformation field;
and performing a myocardial deformation analysis of the left ventricle based on the determined intrinsic myocardial motion trajectory.
Peled teaches:
determining a deformation field based on the segmented, masked 4D image data;
(Peled, [Page 149]; “utilizes a finite element based tracking algorithm through the cardiac cycle")
(Peled, [Page 149]; “Circumferential (CS), longitudinal (LS) and radial (RS) strains were calculated for each of 16 myocardial segments")
determining an intrinsic myocardial motion trajectory based on the determined deformation field;
(Peled, [Page 149];"Peak strains for akinetic segments were generally post-systolic, peaking at 50 ± 17% of the RR interval compared to 43 ± 9% for normokinetic segments")
performing a myocardial deformation analysis of the left ventricle based on the determined intrinsic myocardial motion trajectory.
(Peled, [Page 149]; “Automated 4D regional strain analysis of CT datasets shows a good correspondence to visual analysis and successfully differentiates between normal and abnormal segments, thus providing an objective quantifiable map of myocardial regional function")
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Zheng and Jahne with Peled. The motivation for the combination is to determine deformation fields and intrinsic myocardial motion trajectories from the segmented, masked 4D image data and thereby perform a myocardial deformation analysis of the left ventricle, providing an objective quantifiable map of myocardial regional function (Peled, [Page 149]; "providing an objective quantifiable map of myocardial regional function").
Conclusion
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/SHIVANGI SARKAR/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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