DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The Response filed 11 May 2026 (hereinafter “the Response”) has been entered and considered. Claims 1-12 and 15 are all the claims pending in the application. Claims 1-10 and 15 remain rejected. Claims 11-12 are objected to. This Action is made final.
Response to Arguments
On pages 8-10 of the Amendment, Applicant argues that the proposed combination of Bergtholdt and Zhang does not teach or suggest “performing a coverage check comprising determining from the image analysis whether the defined target anatomy is fully contained within the first image data”, “generating a data representation of the result of the coverage check”, “controlling a user interface to display an indication of the result of the coverage check”, where “the second FOV of the second scan protocol is determined in dependence on upon the user input”.
Before turning to Applicant’s specific arguments, the Examiner notes that Applicant generally sets forth a piecemeal attack of the references without considering the combination of references. To more clearly understand the combination, the Examiner submits the following:
Bergtholdt is directed to generating volume scan images based on “pre-scan image data”, wherein a “bounding box is automatically created and presented superimposed” over one or more of the pre-scan images which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box” in such a manner as to “ensure the entire tissue of interest (or the entirety of a sub-portion of interest of the tissue of interest) is scanned” in the subsequent volume scan imaging process (Abstract and [0002, 0009-0012, 0027-0031, 0048, and 0061]).
Clearly, Bergtholdt contemplates a manual “coverage check” of the pre-scan images, albeit a visual one, and a manual correction of a field of view by the user before capturing the final diagnostic images.
Since Bergtholdt’s coverage check is performed visually by the user, the reference does not teach the claimed coverage check “from the image analysis”.
However, Zhang discloses a deep learning framework to “check the coverage of LV” from the images so as to “identify images with incomplete coverage” with an intention identical to that of Bergtholdt – namely, “[e]nsuring full coverage of the [tissue of interest]” (Abstract and Section 2).
Clearly, Zhang’s coverage check by way of image analysis is analogous to Bergtholdt’s visual coverage check. Accordingly, as discussed in the previous action, if an ordinarily skilled artisan were to consider the teachings of these two references in combination, it would have been obvious to modify Bergtholdt to perform the coverage check using a deep learning image analysis framework, rather than visually, to determine whether the tissue(s) of interest are completely covered in the pre-scan images and to display the results to the user, as taught by Zhang.
In such a combination, the claimed features would have been taught, contrary to Applicant’s assertions. Specifically, the combination would have taught performing a coverage check comprising determining from the image analysis whether the defined target anatomy is fully contained within the first image data (Abstract and Section 2 of Zhang discloses a deep learning framework called Semi-Coupled-GANs (SCGANs) to “check the coverage of LV” from the images so as to “identify images with incomplete coverage”), generating a data representation of the result of the coverage check (Figure 2 of Zhang shows a display of images that have complete or incomplete coverage, analogous to Bergtholdt’s disclosure of displaying the 2D planning projection images with a “bounding box” superimposed thereover such that the clinician can modify it in response to the visual coverage check), controlling a user interface to display an indication of the result of the coverage check, where the second FOV of the second scan protocol is determined in dependence on upon the user input ([0027-0031] of Bergtholdt discloses that the clinician can “reject and/or modify the bounding box” such that the imaging system 200 captures the volumetric scan according to the field of view defined by the bounding box created by the system and modified by the clinician, wherein such modifying is necessarily in response to the user’s visual coverage check which is analogous to Zhang’s image-analysis-based coverage check).
Turning now to Applicant’s particular arguments:
On pages 8-9, Applicant contends that Bergtholdt does not disclose displaying an indication of a result of the coverage check. In support of this assertion, Applicant argues that Bergtholdt merely disclose the “creation, display, acceptance, rejection or modification of a scan-planning bounding box” (page 8 of the Response). The Examiner respectfully maintains that the applied references do indeed teach the limitation in question.
Initially, the Examiner notes that Applicant’s own characterization of Bergtholdt’s teaches reads on the claimed displaying step. In particular, when the user modifies the bounding box, this is a clear indication that the user does not believe the automatically created bounding box properly covered the area of interest. Since the pre-scan image and the bounding box are displayed to the user while performing the modification, Bergtholdt does teach an indication of the result of the user’s visual coverage check, contrary to Applicant’s assertions.
Moreover, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The Examiner did not rely on Bergtholdt to teach the claim limitation in question. Rather, Zhang discloses a deep learning framework to “check the coverage of LV” from the images so as to “identify images with incomplete coverage” (Abstract and Section 2), wherein the results of the complete or incomplete coverage are displayed (Fig. 2).
For all the reasons discussed above, the combination of Bergtholdt and Zhang teaches the displaying step.
On pages 8-9, Applicant asserts that Bergtholdt does not teach or suggest receiving user input following display of that coverage-check result. In support of this assertion, Applicant argues that Bergtholdt does not describe any determination that the defined target anatomy is fully contained within the first image data, thus concluding that the bounding box modification is not the result of the coverage check. The Examiner respectfully disagrees.
Initially, why would the user decide to modify the bounding box if they had not first determined that the initial bounding box does not produce the correct coverage? Clearly, the modification of the bounding box, as taught by Bergtholdt, implicitly discloses that a coverage check has been performed (in this case, visually). This is also explicitly disclosed by the reference since the whole point of the bounding box modification is to “ensure the entire tissue of interest (or the entirety of a sub-portion of interest of the tissue of interest) is scanned” in the subsequent volume scan imaging process ([0048]). Thus, Bergtholdt alone teaches the limitation in question here.
Even so, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The Examiner did not rely on Bergtholdt to teach the claim limitation in question. Rather, Zhang discloses a deep learning framework to “check the coverage of LV” from the images so as to “identify images with incomplete coverage” with an intention identical to that of Bergtholdt – namely, “[e]nsuring full coverage of the [tissue of interest]” (Abstract and Section 2). Clearly, Zhang’s coverage check by way of image analysis is analogous to Bergtholdt’s visual coverage check. Thus, in the proposed combination, the user’s input would be in response to the result of the coverage check (i.e., modifying the bounding box to correct an inadequately covered tissue of interest).
On page 9 of the Response, Applicant appears to acknowledge “the user’s visual identification of tissue boundaries”, but contends that the reference is deficient because such a check is not determined “from anatomical image analysis”.
However, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The Examiner did not rely on Bergtholdt to teach the use of image analysis for the coverage check. Rather, Zhang discloses a deep learning framework to “check the coverage of LV” from the images so as to “identify images with incomplete coverage” (Abstract and Section 2). Since Zhang’s coverage check by way of image analysis is clearly analogous to Bergtholdt’s visual coverage check, the combination of references teaches the limitation in question here.
On pages 9-10, Applicant asserts that Zhang does not teach or suggest “determining a second scan protocol or second FOV” or “receiving user input after displaying a coverage-check result” or “determining a second FOV in dependence upon that post-display user input”.
However, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The Applicant has merely listed all the features already taught by the primary reference Bergtholdt. The rejection did not rely on Zhang to teach these features because they are indeed taught by Bergtholdt. In particular, Bergtholdt discloses determining a second scan protocol defining a second set of scan parameters for the imaging apparatus, and wherein the second scan protocol is for acquiring image data which covers a second FOV, wherein the second FOV is the same as or different to the first FOV ([0027-0031] discloses that the imaging system 200 also captures a “volumetric scan…with scan setting…which result in an image quality at which the image data can be used for diagnostic purposes”, wherein the diagnostic image data also covers a “field of view”; see Figs. 1 and 4), receiving a user input from the user interface following display, and wherein the second FOV of the second scan protocol is determined in dependence upon the user input ([0027-0031] discloses that a “bounding box is automatically created and presented superimposed over one or more 2D planning projection images” which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box”, wherein the bounding box “identifies at least a start position of the volumetric scan and a stop location or a length of the volumetric scan” and these “start and end locations define a field of view” which “represents the sub-portion of the object or subject that will be scanned during the volumetric scan”).
On page 10 of the Response, Applicant asserts that Zhang does not teach or suggest the claimed display of the coverage check since Zhang’s “probability values and classification examples are not a displayed indication in an acquisition-planning interface that prompts user input for determining a second FOV”.
However, the features upon which applicant relies (i.e., user prompt) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, Applicant again fails to consider Zhang’s teachings in the context of Bergtholdt’s clear disclosure of an acquisition-planning interface that prompts user input for determining a second FOV ([0027-0031] discloses that a “bounding box is automatically created and presented superimposed over one or more 2D planning projection images” which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box”). For all the reasons discussed above, the combination of Bergtholdt and Zhang teaches the limitation in question.
On page 10 of the Response, Applicant argues that the asserted combination lacks the “claimed closed sequence tying the coverage-check result to the subsequent user-dependent second-FOV determination”. In support of this argument, Applicant submits that Bergtholdt provides a scan-planning bounding box, while Zhang provides a CMR quality-assessment classifier. The Examiner disagrees with Applicant’s oversimplification of the references and maintains that the combination of references does tie the coverage check result to the user-modification.
Bergtholdt discloses generating volume scan images based on “pre-scan image data”, wherein a “bounding box is automatically created and presented superimposed” over one or more of the pre-scan images which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box” in such a manner as to “ensure the entire tissue of interest (or the entirety of a sub-portion of interest of the tissue of interest) is scanned” in the subsequent volume scan imaging process (Abstract and [0002, 0009-0012, 0027-0031, 0048, and 0061]).
Why would the user modify the bounding box for the subsequent volume scan imaging process if they had not performed a visual coverage check? Applicant appears to acknowledge this visual check on page 9 of the Response. Clearly, the only reason the user would modify the bounding box to “ensure the entire tissue of interest…is scanned” in the subsequent volume scan imaging process is that the user had first determined (via a coverage check) that the tissue of interest is not properly covered within the bounding box. Thus, Applicant’s assertion that the references lack a sequence tying the coverage check result to the subsequent user-input second FOV determination is incorrect.
Finally, Applicant asserts that the rejection’s rationale of preserving diagnostic accuracy is insufficient (pages 10-11 of the Response). In support of this argument, Applicant contends that the proposed modification would have required more than just adding Zhang’s classifier to Bergtholdt, arguing that it would require “converting Bergtholdt’s bounding-box scan-planning workflow into a workflow that generates and displays a coverage-check result and then determines the second FOV based on user input received after that displayed result”. The Examiner respectfully disagrees.
As discussed at length above, Bergtholdt alone discloses a workflow that generates a coverage-check result and then determines the second FOV based on user input received after that result (“bounding box is automatically created and presented superimposed” over one or more of the pre-scan images which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box” in such a manner as to “ensure the entire tissue of interest (or the entirety of a sub-portion of interest of the tissue of interest) is scanned” in the subsequent volume scan imaging process, wherein a visual check must be performed by the user prior to modifying the bounding box).
While Bergtholdt does not expressly disclose the visual check is performed by image analysis, a result of which is displayed, Zhang teaches these features (Abstract and Section 2 discloses a deep learning framework called Semi-Coupled-GANs (SCGANs) to “check the coverage of LV” from the images so as to “identify images with incomplete coverage”, and Figure 2 of Zhang shows a display of images that have complete or incomplete coverage).
As discussed in the prior action, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bergtholdt to perform a coverage check using a deep learning image analysis framework to determine whether the tissue(s) of interest are completely covered in the pre-scan images and to display the results to the user, as taught by Zhang, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. In particular, Bergtholdt’s visual coverage check as modified by Zhang’s image-analysis-based coverage check can yield a predictable result of ensuring complete coverage of the tissue(s) of interest since both reference desire the same ([0002, 0048] of Bergtholdt and Abstract of Zhang).
Thus, a person of ordinary skill would have appreciated including, in Bergtholdt’s workflow of pre-scan planning and user-corrected FOV for subsequent scanning, the ability to automatically perform the coverage check by way of image analysis since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Indeed, it is predictable that a deep-learning-based coverage check would be an improvement over a visual one, thus leading to improved diagnostic accuracy, as stated in the prior action.
Applicant’s remaining arguments on page 11 rely on the assertion that Bergtholdt and Zhang fail to teach the features of the independent claims. These arguments have each been addressed above.
For all the foregoing reasons, the prior art rejections are maintained, as reproduced below.
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.
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, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2016/0287201 to Bergtholdt et al. (hereinafter “Bergtholdt”) in view of “Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets” by Zhang et al. (cited in the IDS filed 3/27/24; hereinafter “Zhang”).
As to independent claim 1, Bergtholdt discloses a computer implemented method (Abstract and [0009-0012, 0061] discloses that Bergtholdt is directed to generating “volume scan planning images” based on “pre-scan image data”, wherein the disclosed method is implemented by a “computer processor”) comprising: receiving first image data of a patient anatomy from a medical imaging apparatus, wherein the first image data is 2D or 3D image data, and wherein the first image data is data acquired in accordance with a first scan protocol comprising a first set of scan parameters for the medical imaging apparatus, wherein the first scan protocol is for acquisition of image data which covers a first FOV ([0024-0026] discloses “an imaging system 200, such as a computed tomography (CT) scanner” which acquires 2D or 3D pre-scans which may be “a 2D scout (also referred to as a pilot or surview) scan”, wherein the pre-scans have “lower image quality (e.g., lower contrast resolution)” compared to subsequently captured diagnostic scan (first set of scan parameters), and wherein the pre-scans cover a particular “field of view”; see Figs. 1 and 3); applying anatomical image analysis to the first image data to detect at least a portion of a defined target anatomy in the image data ([0037-0041] discloses that “tissue(s) of interest detector 504 detects the one or more tissues of interest in the 3D pre-scan image data” using “registration algorithms”); determining a second scan protocol defining a second set of scan parameters for the imaging apparatus, and wherein the second scan protocol is for acquiring image data which covers a second FOV, wherein the second FOV is the same as or different to the first FOV ([0027-0031] discloses that the imaging system 200 also captures a “volumetric scan…with scan setting…which result in an image quality at which the image data can be used for diagnostic purposes”, wherein the diagnostic image data also covers a “field of view”; see Figs. 1 and 4); controlling a user interface to display an indication; receiving a user input from the user interface following display, and wherein the second FOV of the second scan protocol is determined in dependence upon the user input ([0027-0031] discloses that a “bounding box is automatically created and presented superimposed over one or more 2D planning projection images” which “can be displayed” so that a “clinician can accept, reject, and/or modify the bounding box”, wherein the bounding box “identifies at least a start position of the volumetric scan and a stop location or a length of the volumetric scan” and these “start and end locations define a field of view” which “represents the sub-portion of the object or subject that will be scanned during the volumetric scan”); and acquiring second image data of the patient anatomy in accordance with the second scan protocol, the second image data covering the second FOV, wherein the second image data is 2D or 3D image data ([0027-0031] discloses that the imaging system 200 captures the volumetric scan according to the field of view defined by the bounding box created by the system and modified by the clinician).
Although Bergtholdt discloses that the bounding box should “ensure the entire tissue of interest (or the entirety of a sub-portion of interest of the tissue of interest) is scanned” ([0002, 0048]), Bergtholdt does not expressly disclose that the system analyzes the image to do so. That it, Bergtholdt does not expressly disclose performing a coverage check comprising determining from the image analysis whether the defined target anatomy is fully contained within the first image data; generating a data representation of the result of the coverage check or that the indication displayed to the user on the user interface is of the result of the coverage check.
Zhang, like Bergtholdt, is directed to “diagnostic imaging” in which “[e]nsuring full coverage of the [tissue of interest] is a basic criteria” (Abstract). Zhang discloses a deep learning framework called Semi-Coupled-GANs (SCGANs) to “check the coverage of LV” from the images so as to “identify images with incomplete coverage” (Abstract and Section 2). Figure 2 of Zhang shows a display of images that have complete or incomplete coverage.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Bergtholdt to perform a coverage check using a deep learning image analysis framework to determine whether the tissue(s) of interest are completely covered in the pre-scan images and to display the results to the user, as taught by Zhang, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have preserved “diagnostic accuracy” (Section 1 of Zhang), particularly since both Bergtholdt and Zhang contemplate the desire to ensure complete coverage of the tissue(s) of interest ([0002, 0048] of Bergtholdt and Abstract of Zhang).
As to claim 9, Bergtholdt as modified above further teaches that the scan parameters of the first and second scan protocols include boundaries of a scan range along at least one scan axis of the medical imaging apparatus, and wherein the first FOV and second FOV are each defined at least in part by the boundaries of said scan range ([0024-0026, 0030] of Bergtholdt discloses that the field of view is defined by “start and end locations” as an “extent at least along the z-axis” or longitudinal axis, wherein the pre-scan image data and the volumetric scan image data may cover a “same field of view”).
Independent claim 15 recites a system, comprising: a medical imaging apparatus; and a processor ([0024, 0032] discloses that the system 201 includes an imaging system 200 and a scan planner 220 implemented via one or more computer processors) configured to perform the steps recited in independent claim 1. Accordingly, claim 15 is rejected for reasons analogous to those discussed above in conjunction with claim 1.
Claims 2-8 are rejected under 35 U.S.C. 103 as being unpatentable over Bergtholdt in view of Zhang and further in view of U.S. Patent Application Publication No. 2011/0288407 to Brinks et al. (hereinafter “Brinks”).
As to claim 2, Bergtholdt as modified above further teaches that the data representation of the result of the coverage check is communicated to a datastore for storage of the result of the coverage check (Section 3 of Zhang discloses that the deep learning framework is implemented on GPUs which are necessarily part of a computer, and Fig. 2 shows the results of the coverage check which are necessarily stored in memory); and wherein the second image data is stored in the same or a different datastore ([0032] of Bergtholdt discloses that the scanned images are stored in “physical memory”). Bergtholdt as modified above does not expressly disclose that the second image data is stored associated with the result of the coverage check.
Brinks, like Bergtholdt, is directed to a medical imaging system which acquires surview (scout) data prior to main scan data, wherein the system determines whether the organ(s) of interest are outside the field of view (FOV), similar to Zhang (Abstract and [0021-0023]). Brinks discloses generating an extended FOV to be used when generating the main PET image representation ([0023]). Brinks further discloses that the images and the corrected FOV PET data which is the result of the coverage check are stored in a memory unit ([0021-0023]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Bergtholdt and Zhang to store the main scan image and result of the coverage check in association, as taught by Brinks, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have provided access to the data at a later date/time.
As to claim 3, Bergtholdt as modified above does not expressly disclose that the method further comprises: responsive to a negative result of the coverage check, determining a proposed adjustment to the first scan protocol so as to acquire an extended FOV, wherein the proposed adjustment is based on the anatomical image analysis; and either setting the second scan protocol in accordance with the proposed adjusted first scan protocol, such that the second FOV is set as the extended FOV; or communicating the proposed adjustment to the first scan protocol and/or the proposed extended FOV to the user interface.
Brinks, like Bergtholdt, is directed to a medical imaging system which acquires surview (scout) data prior to main scan data, wherein the system determines whether the organ(s) of interest are outside the field of view (FOV), similar to Zhang (Abstract and [0021-0023]). Brinks discloses generating an extended FOV including the entire organ of interest to be used when generating the main PET image representation ([0023]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Bergtholdt and Zhang to generate an extended FOV to be used when generating the main scan responsive to determining that the organ of interest is outside the FOV, as taught by Brinks, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have ensured complete coverage of the tissue(s) of interest ([0002, 0048] of Bergtholdt and Abstract of Zhang).
As to claim 4, Bergtholdt as modified above does not expressly disclose applying the anatomical image analysis to estimate a spatial extension of the target anatomy beyond at least one boundary of the FOV, and further comprising controlling a user interface to display a visual representation of the spatial extension.
Brinks, like Bergtholdt, is directed to a medical imaging system which acquires surview (scout) data prior to main scan data, wherein the system determines whether the organ(s) of interest are outside the field of view (FOV), similar to Zhang (Abstract and [0021-0023]). Brinks discloses generating an extended FOV including the entire organ of interest to be used when generating the main PET image representation ([0023]). Brinks further discloses a graphic user interface that displays the pre-corrected and post-corrected images for verification and/or further manual correction ([0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Bergtholdt and Zhang to generate an extended FOV to be used when generating the main scan and to display the corrected images on a user interface, as taught by Brinks, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have ensured complete coverage of the tissue(s) of interest ([0002, 0048] of Bergtholdt and Abstract of Zhang).
As to claim 5, the proposed combination of Bergtholdt, Zhang and Brinks further teaches that the coverage check comprises determining, based on the spatial extension of the target anatomy beyond the FOV, a proposed adjustment to the first scan protocol for acquiring an extended FOV which fully covers the target anatomy, and wherein the second scan protocol is determined such that the second FOV is set as the extended FOV ([0023] of Brinks discloses generating an extended FOV including the entire organ of interest to be used when generating the main PET image representation; the reasons for combining the references are the same as those discussed above in conjunction with claim 4).
As to claim 6, the proposed combination of Bergtholdt, Zhang and Brinks further teaches controlling a user interface to display a representation of the proposed adjustment to the first scan protocol and/or proposed extended FOV; generating a prompt on the user interface requesting user approval; receiving a user input from the user interface indicative of approval or non-approval, and acquiring second image data in accordance with the proposed adjusted scan protocol, spanning the extended FOV, only responsive to receipt of a user input indicative of approval ([0030-0031] of Bergtholdt discloses presenting a bounding box to a clinician who can accept, reject, and/or modify the bounding box, wherein such verification and/or manual correction necessarily involves a prompt for and receipt of user input, and wherein the bounding box “identifies at least a start position of the volumetric scan and a stop location or a length of the volumetric scan” and these “start and end locations define a field of view” which “represents the sub-portion of the object or subject that will be scanned during the volumetric scan”; [0023-0025] of Brinks discloses generating an extended FOV including the entire organ of interest to be used when generating the main PET image representation and that a graphic user interface that displays the pre-corrected and post-corrected images for verification and/or further manual correction, wherein such verification and/or manual correction necessarily involves a prompt for and receipt of user input; the reasons for combining the references are the same as those discussed above in conjunction with claim 4).
As to claim 7, the proposed combination of Bergtholdt, Zhang and Brinks further teaches controlling a user interface to display a visual representation of the proposed extended FOV relative to a rendered view of the first image data; controlling the user interface to generate a prompt for a user input indicative of approval of the proposed extended FOV, or indicative of an amendment to the proposed extended FOV, via operation of a user control; and determining the second scan protocol such that the second FOV is set as the extended FOV, responsive to receipt of user approval from the user-interface; OR determining the second scan protocol such that the second FOV is set as a user- amended FOV, wherein the user-amended FOV is defined based on a received user input indicative of an amendment to the extended FOV ([0030-0031] of Bergtholdt discloses presenting a bounding box to a clinician who can accept, reject, and/or modify the bounding box, wherein such verification and/or manual correction necessarily involves a prompt for and receipt of user input, and wherein the bounding box “identifies at least a start position of the volumetric scan and a stop location or a length of the volumetric scan” and these “start and end locations define a field of view” which “represents the sub-portion of the object or subject that will be scanned during the volumetric scan”; [0023-0025] of Brinks discloses generating an extended FOV including the entire organ of interest to be used when generating the main PET image representation and that a graphic user interface that displays the images for verification and/or further manual correction, wherein such verification and/or manual correction necessarily involves a prompt for and receipt of user input; the reasons for combining the references are the same as those discussed above in conjunction with claim 4).
As to claim 8, the proposed combination of Bergtholdt, Zhang and Brinks further teaches that estimating the spatial extension of the target anatomy outside of the FOV comprises estimating an outline of a boundary of at least the portion of the target anatomy which lies outside of the FOV, and wherein the method further comprises generating a visual depiction of said outline relative to the first image data on a display of a user interface ([0023-0025] of Brinks discloses generating an extended FOV including the entire organ of interest by aligning, registering, or fusing the attenuation correction PET image representation and an anatomy map and extrapolating PET image intensity values to the extracted organs, outside of the actual PET FOV to generate a theoretical extension of the PET FOV, and that a graphic user interface that displays the images for verification and/or further manual correction; the reasons for combining the references are the same as those discussed above in conjunction with claim 4).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Bergtholdt in view of Zhang and further in view of U.S. Patent Application Publication No. 2019/0244353 to Buelow et al. (hereinafter “Buelow”).
As to claim 10, Bergtholdt as modified above further teaches that the data representation is communicated to a datastore for storage of the result of the coverage check (Section 3 of Zhang discloses that the deep learning framework is implemented on GPUs which are necessarily part of a computer, and Fig. 2 shows the results of the coverage check which are necessarily stored in memory). Bergtholdt as modified above does not expressly disclose performing a quality assessment comprising deriving a quality indicator for the acquired second image data based on the result of the coverage check associated with the image data. However, Buelow discloses performing “quality assessment of medical image datasets” including comparing the FOV of an examined object with a reference FOV (Abstract). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the proposed combination of Bergtholdt and Zhang to perform a quality assessment of Bergtholdt’s FOV volumetric image (which is generated based on Zhang’s coverage check in the above combination), as taught by Buelow, to arrive at the claimed invention discussed above. Such a modification is the result of combining prior art elements according to known methods to yield predictable results. It is predictable that the proposed modification would have “identif[ied] potential errors or deviations that may be avoided in future imaging operations”, as taught by Buelow (Abstract).
Allowable Subject Matter
Claims 11-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SEAN M CONNER/Primary Examiner, Art Unit 2663
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