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 .
Priority
Acknowledgment is made of applicant’s claim for priority under 35 U.S.C. 119(e) to U.S. provisional application, 63/312,690, filed 02/22/2022.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 08/16/2024 and 03/30/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to because:
With respect to Fig. 3, the drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: Fig. 3 depicts the term
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Regarding Fig. 6A, the drawings are objected to as failing to comply with 37 CFR 1.84(l), which requires all drawings be sufficiently reproducible, with every line number and letter being durable, clean, sufficiently dense, dark, uniformly thick, and well-defined. Fig. 6 depicts the following graphical element which is too faded to accurately read or reproduce:
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Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 8 is objected to because of the following informalities:
Claim 8 recites the limitation “corresponding second subsampling”, given applicant later recites “finer than said corresponding first subsampling”, the examiner interprets this limitation to be intended to recite “the/said corresponding second subsampling”.
Claim 8 recites the limitation “different subregions of said, each at a first subsampling”. It is unclear what the intended function or purpose of the underlined claim language is.
Claim 8 recites the limitation “a first subsampling”. The limitation “a first subsampling” is first introduced in claim 7. The examiner understands this to be the same subsampling as that recited previously, and if so, should be defined as “the first subsampling”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation “registering said 2D x-ray image”. The act of “registering said 2D x-ray image” is first recited in claim 1. It is unclear if this new recitation of “registering said 2D x-ray image” is a different process of registering or a distinct act of “registering”. Properly distinguishing or defining the act of “registering” between claim 1 and claim 7 should be made to clarify the scope of this action.
Claim 8 recites the limitation “selecting a subregion a plurality of times for different subregions”. The act of “selecting a subregion” is first recited in claim 7. This subregion, in the context of the claim, is understood to be different from the first subregion recited in claim 7. The definition of this new subregion in claim 8 should be introduced in such a way to clearly define it as distinct from the subregion of claim 7.
Claim 8 recites the limitation “repeating said registering and said repeating said selecting and said registering”. The language of this limitation is unclear and syntactically difficult to comprehend.
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.
Claims 1-4, 9-11, 21, 31 are rejected under 35 U.S.C. 103 as being unpatentable over Navab et al (US 2019/0000564 A1), hereinafter referred to as “Navab”, in view of Siewerdsen et al (US 2017/0238897 A1), hereinafter referred to as “Siewerdsen”.
Regarding claim 1, Navab disclose a system and method for co-registering Cone-Beam Computed Tomography (CBCT) volumes. More specifically, Navab teach A method for registering a two-dimensional (2D) x-ray image to a three- dimensional (3D) x-ray image (a plurality of exemplary methods are provided: Example 1 – Vision-based Intraoperative Cone-Beam CT Stitching for Non-Overlapping Volumes [¶0066-0070], or Example 4 - Automatic Intra-Operative Stitching of Non-Overlapping Cone-Beam CT Acquisitions [¶0235-0236]), comprising:
constructing said 2D x-ray image of an object of interest (a motorized C-arm orbits around a center to acquire a total of 100 2D X-ray images of a subject [¶0079]);
receiving said 3D x-ray image of said object of interest (the algorithm recovers spatial alignment of non-overlapping CBCT volumes, first by estimating the transformation between two volumes by automatic detection and matching of surface features, and then reconstructing projections of the positioning-laser onto an unknown curved surface to recover 3D information of the X-ray [¶0066-67, 80-82; Fig. 3]);
generating a plurality of 2D projection images from said 3D x-ray image of said object of interest for each of a corresponding plurality of different poses of said object of interest (accurate and precise projection matrices are obtained via CBCT volume reconstruction for each projection image [¶0255-261; Figs. 4(a)-(d)]); and registering said 2D x-ray image to one of said 2D projection images, (2D/3D registration is performed for each projection image according to the corresponding projection matrix [¶0255-256]). While Navab does disclose registration using a similarity measure [¶0100], they fail to disclose that this similarity metric is differentially weighted by either low or high-contrast structures.
Siewerdsen, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose volumetric image reconstruction and registration for tomographic imaging systems. Siewerdsen teach wherein said registering uses a similarity metric that is differentially weighted to affect an influence of at least one of low-contrast or high-contrast structures on the registering between said 2D x-ray image and each of said 2D projection images (Siewerdsen: 3D-2D registration of images are conducted using normalized gradient information (NGI), which ignores strong (high-contrast) from one image, or mismatching gradients only present in one image, which is differentially weighted according to the weighting term w [¶0056-60; Eq. 2-6]). Furthermore, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
As for claim 2, Navab in view of Siewerdsen teach The method according to claim 1 (as described above), wherein said similarity metric is based on at least one of image gradients, image intensities, or a statistical distribution thereof (Siewerdsen: NGI is a similarity metric based on the normalized gradient information [¶0056]). Again, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
Considering claim 3, Navab in view of Siewerdsen teach The method according to claim 1 (as described previously), wherein said weighted similarity metric weights at least one of image gradients, intensities, or a statistical distribution thereof according to a magnitude thereof (Siewerdsen: the NGI is weighted according to the weighting term w [¶0056-59; Eq. 2-6]). Again, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
With respect to claim 4, Navab in view of Siewerdsen The method according to claim (as described previously), wherein said similarity metric is a gradient correlation (GC) similarity metric (Siewerdsen: NGI can be substituted with an alternative similarity measure in the form of gradient correlation (GC) [¶0060]). Again, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
With respect to claim 9, Naveb in view of Siewerdsen teach The method according to claim 1 (as described previously), wherein said constructing said 2D x-ray image of an object of interest comprises constructing a radiograph (Naveb: acquiring 100 2D X-ray images – the examiner notes that a 2D X-ray image is inherently a radiograph [¶0079]), and
receiving said 3D x-ray image of said object of interest comprises receiving one of a computed tomography (CT) x-ray image, a cone-beam CT x-ray image, or a fan-beam CT x-ray image (Naveb: the 3D image is acquired using CBCT [¶0066-67]).
Concerning claim 10, Naveb in view of Siewerdsen teach The method according to claim 9 (as described above), further comprising constructing said 3D x- ray image (Naveb: 3D information is reconstructed through the plurality of views obtained around the organ of interest [¶0069]).
Regarding claim 11, Naveb teach A computer-readable medium for registering a two-dimensional (2D) x-ray image to a three-dimensional (3D) x-ray image (Naveb: software for receiving processing and analyzing date may be implemented on a single device, such as a computer system with a processor, RAM and additional memory [¶0061-63]), the computer-readable medium comprising non- transient code which when executed by a computer causes the computer to:
construct said 2D x-ray image of an object of interest (Naveb: a motorized C-arm orbits around a center to acquire a total of 100 2D X-ray images of a subject [¶0079]);
receive said 3D x-ray image of said object of interest (Naveb: the algorithm recovers spatial alignment of non-overlapping CBCT volumes, first by estimating the transformation between two volumes by automatic detection and matching of surface features, and then reconstructing projections of the positioning-laser onto an unknown curved surface to recover 3D information of the X-ray [¶0066-67, 80-82; Fig. 3]);
generate a plurality of 2D projection images from said 3D x-ray image of said object of interest for each of a corresponding plurality of different poses of said object of interest (Naveb: accurate and precise projection matrices are obtained via CBCT volume reconstruction for each projection image [¶0255-261; Figs. 4(a)-(d)]); and
register said 2D x-ray image to one of said 2D projection images, (Naveb: 2D/3D registration is performed for each projection image according to the corresponding projection matrix [¶0255-256]). While Navab does disclose registration using a similarity measure [¶0100], they fail to disclose that this similarity metric is differentially weighted by either low or high-contrast structures.
Siewerdsen, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose volumetric image reconstruction and registration for tomographic imaging systems. Siewerdsen teach wherein said registering uses a similarity metric that is differentially weighted to affect an influence of at least one of low-contrast or high-contrast structures on the registering between said 2D x-ray image and each of said 2D projection images (Siewerdsen: 3D-2D registration of images are conducted using normalized gradient information (NGI), which ignores strong (high-contrast) from one image, or mismatching gradients only present in one image, which is differentially weighted according to the weighting term w [¶0056-60; Eq. 2-6]). Furthermore, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
As for claim 21, Naveb teach A system for registering a two-dimensional (2D) x-ray image to a three- dimensional (3D) x-ray image (Naveb: the system of Fig. 2, according to examples 1 & 4 [¶0035]), said system comprising a processor (Naveb: computer system comprising a processor [¶0062]) and a memory (Naveb: and a random access memory, and includes additional memory devices or information storage systems [¶0062]), said memory comprising non-transient code which when executed by said processor causes the processor to:
construct said 2D x-ray image of an object of interest (Naveb: a motorized C-arm orbits around a center to acquire a total of 100 2D X-ray images of a subject [¶0079]);
receive said 3D x-ray image of said object of interest (Naveb: the algorithm recovers spatial alignment of non-overlapping CBCT volumes, first by estimating the transformation between two volumes by automatic detection and matching of surface features, and then reconstructing projections of the positioning-laser onto an unknown curved surface to recover 3D information of the X-ray [¶0066-67, 80-82; Fig. 3]);
generate a plurality of 2D projection images from said 3D x-ray image of said object of interest for each of a corresponding plurality of different poses of said object of interest (Naveb: accurate and precise projection matrices are obtained via CBCT volume reconstruction for each projection image [¶0255-261; Figs. 4(a)-(d)]); and
register said 2D x-ray image to one of said 2D projection images, (Naveb: 2D/3D registration is performed for each projection image according to the corresponding projection matrix [¶0255-256]). While Navab does disclose registration using a similarity measure [¶0100], they fail to disclose that this similarity metric is differentially weighted by either low or high-contrast structures.
Siewerdsen, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose volumetric image reconstruction and registration for tomographic imaging systems. Siewerdsen teach wherein said registering uses a similarity metric that is differentially weighted to affect an influence of at least one of low-contrast or high-contrast structures on the registering between said 2D x-ray image and each of said 2D projection images (Siewerdsen: 3D-2D registration of images are conducted using normalized gradient information (NGI), which ignores strong (high-contrast) from one image, or mismatching gradients only present in one image, which is differentially weighted according to the weighting term w [¶0056-60; Eq. 2-6]). Furthermore, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
Considering claim 31, Naveb teach A two-dimensional (2D) x-ray system, comprising:
an x-ray illumination system constructed to illuminate an object of interest with an x-ray beam (Naveb: the X-ray capture system of Figs. 2 & 7 illuminates a patient from an X-ray source [¶0035, 73, 0195]);
a detection system arranged to receive at least a portion of said x-ray beam after said x-ray beam after passing through at least a portion of said object of interest (Naveb: cameras and mirrors capture X-ray beams passing through the patient [¶0035]); and
a system for registering a two-dimensional (2D) x-ray image to a three-dimensional (3D) x- ray image (Naveb: the system of Fig. 2, according to examples 1 & 4 [¶0035]), said system comprising a processor (Naveb: computer system comprising a processor [¶0062]) and a memory (Naveb: and a random access memory, and includes additional memory devices or information storage systems [¶0062]),
wherein said memory comprises non-transient code which when executed by said processor causes the processor to:
construct said 2D x-ray image of an object of interest (Naveb: a motorized C-arm orbits around a center to acquire a total of 100 2D X-ray images of a subject [¶0079]);
receive said 3D x-ray image of said object of interest (Naveb: the algorithm recovers spatial alignment of non-overlapping CBCT volumes, first by estimating the transformation between two volumes by automatic detection and matching of surface features, and then reconstructing projections of the positioning-laser onto an unknown curved surface to recover 3D information of the X-ray [¶0066-67, 80-82; Fig. 3]);
generate a plurality of 2D projection images from said 3D x-ray image of said object of interest for each of a corresponding plurality of different poses of said object of interest (Naveb: accurate and precise projection matrices are obtained via CBCT volume reconstruction for each projection image [¶0255-261; Figs. 4(a)-(d)]); and
register said 2D x-ray image to one of said 2D projection images, (Naveb: 2D/3D registration is performed for each projection image according to the corresponding projection matrix [¶0255-256]). While Navab does disclose registration using a similarity measure [¶0100], they fail to disclose that this similarity metric is differentially weighted by either low or high-contrast structures.
Siewerdsen, on the other hand, is analogous art pertinent to the field of endeavor of the present application and disclose volumetric image reconstruction and registration for tomographic imaging systems. Siewerdsen teach wherein said registering uses a similarity metric that is differentially weighted to affect an influence of at least one of low-contrast or high-contrast structures on the registering between said 2D x-ray image and each of said 2D projection images (Siewerdsen: 3D-2D registration of images are conducted using normalized gradient information (NGI), which ignores strong (high-contrast) from one image, or mismatching gradients only present in one image, which is differentially weighted according to the weighting term w [¶0056-60; Eq. 2-6]). Furthermore, Siewerdsen disclose that alternative similarity measures can be used, such as gradient correlation (GC), however NGI-based measures were the most robust against mismatch and noise [¶0060], and that their self-calibrating system enabled sufficient detail and accuracy even with simulated system perturbations [¶0061].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the NGI-based metric of Siewerdsen with the 3D-2D x-ray image registration system and method of Navab to arrive at the invention of the instant application. The motivation for doing so would be to allow for 3D-2D x-ray image registration that can accurately align image regions with high-contrast or noisy inputs, and resist external perturbations during imaging [¶0060-61].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Navab et al (US 2019/0000564 A1), hereinafter referred to as “Navab”, in view of Siewerdsen et al (US 2017/0238897 A1), hereinafter referred to as “Siewerdsen”, further in view of Helm et al (US 2020/0315553 A1), hereinafter referred to as “Helm”.
Concerning claim 5, Navab in view of Siewerdsen teach The method according to claim 1 (as described previously), however, they fail to teach using a gradient orientation as a similarity metric.
Helm, however, is analogous art pertinent to the field of endeavor of the present application and disclose a method for registering 2D and 3D CT images. Helm teach wherein said similarity metric is a gradient orientation (GO) similarity metric (Helm: The similarity metric of block 530 may include a gradient orientation metric [¶0096; Fig. 6]). Helm further disclose that selection of gradient orientation over gradient correlation is optimized for maximizing the similarity metric [¶0097; Eq. 3].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present application to incorporate the gradient orientation taught by Helm as a possible similarity metric in the base system of Naveb in view of Siewerdsen to arrive at the invention of the instant application. The motivation for doing so would be to allow for maximizing the similarity metric depending on the context of 2D-3D registration [¶0097; Eq. 3].
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Navab et al (US 2019/0000564 A1), hereinafter referred to as “Navab”, in view of Siewerdsen et al (US 2017/0238897 A1), hereinafter referred to as “Siewerdsen”, further in view of Wang et al (US 2014/0140479 A1), hereinafter referred to as “Wang”.
Regarding claim 6, Naveb in view of Siewerdsen teach The method according to claim 1 (as described previously), but fails to explicitly teach removing high contrast gradients prior to registration.
Wang, on the other hand, disclose a hybrid dual energy (DE) x-ray image processing method wherein bone contrast is suppressed to better visual internal chest structures. Wang further teaches further comprising, prior to said registering, preprocessing said 2D x-ray image to remove high contrast gradient regions (Wang: rib structures (high contrast bone) in x-ray images are suppressed via a rib structure segmentation (S120) and suppression (S130) process to yield a rib-suppressed region [¶0041-45; Figs. 2 & 3]). Additionally, Wang disclose their method takes advantage of dual-energy imaging, which acquires images at lower energy doses to better capture lower-contrast soft-tissue [¶0008].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to utilize the bone suppression techniques of Wang with the method of Naveb in view of Siewerdsen to arrive at the invention of the instant application. One of ordinary skill would be incentivized to do so in order to better visualize soft tissue in chest x-ray images [¶0008].
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Navab et al (US 2019/0000564 A1), hereinafter referred to as “Navab”, in view of Siewerdsen et al (US 2017/0238897 A1), hereinafter referred to as “Siewerdsen”, further in view of Ketcha (US 2017/0178349 A1), hereinafter referred to as “Ketcha”.
As for claim 7, Naveb in view of Siewerdsen The method according to claim 1 (as described previously), but do not clearly describe a process of downsampling regions in an x-ray image to for registering a deformable structure.
In contrast, Ketcha disclose a method of 3D-2D CT image registration to accurately capture deformable structures. More particularly, Ketcha teach further comprising:
selecting a subregion of said 2D x-ray image at a first subsampling (Ketcha: a multistage registration process is performed wherein subsections of the 2D image are selected at a starting resolution, exemplary figures of subsections illustrated in Figs. 1(a-b) [¶0029; 38-40]);
registering said 2D x-ray image to one of said 2D projection images based on said selected subregion (Ketcha: the multistage registration process aligns 2D projections to the 2D x-ray image [¶0029; 38-40; Figs. 1(a-b) & 2]); and
repeating said selecting and said registering for a second subsampling that is finer than said first subsampling such that registering said 2D x-ray image to one of said 2D projection images so as to register a deformable structure (Ketcha: this multistage registration process iterates multiple times with increasing resolution via downsampling of image p for globally deformable 3D-2D registration [¶0033, 38-40]). Ketcha explains that this multi-stage facilitates finer registration accuracy, capturing increasingly fine details of anatomical structures [¶0040].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present invention to utilize the downsampling system for capturing deformable structures taught by Ketcha with the base registration system disclosed by Naveb in view of Siewerdsen to arrive at the invention of the instant application. The motivation to do so would be accurately capture increasingly finer details in every stage of 3D-2D registration [¶0040].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Navab et al (US 2019/0000564 A1), hereinafter referred to as “Navab”, in view of Siewerdsen et al (US 2017/0238897 A1), hereinafter referred to as “Siewerdsen”, further in view of Ketcha et al (US 2017/0178349 A1), hereinafter referred to as “Ketcha”, further in view of Chou et al (US 2016/0012592 A1), hereinafter referred to as “Chou”.
Considering claim 8, Naveb in view of Siewerdsen, further in view of Ketcha teach The method according to claim 7 (as described above), further comprising:
repeating said selecting a subregion a plurality of times for different subregions of said, each at a first subsampling (Ketcha: the multistage registration method is performed across a plurality of different subregions, see Figs. 1(a-b) & 2, illustrating four distinct subregions being selected [¶0029-40]); and
repeating said registering and said repeating said selecting and said registering for corresponding second subsampling that is finer than said corresponding first subsampling such that registering said 2D x-ray image to one of said 2D projection images for each selected subregion so as to register a deformable structure (Ketcha: the multistage registration iterates by decreasing the downsampling of each selected image region p to capture deformable structures [¶0029-40]). Ketcha explains that this multi-stage facilitates finer registration accuracy, capturing increasingly fine details of anatomical structures [¶0040].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present invention to utilize the downsampling system for capturing deformable structures taught by Ketcha with the base registration system disclosed by Naveb in view of Siewerdsen to arrive at the invention of the instant application. The motivation to do so would be accurately capture increasingly finer details in every stage of 3D-2D registration [¶0040]. Ketcha, however, discloses that their method does not create a deformation field, but rather captures deformation in smaller localized regions.
Chou, per contra, disclose a method for 2D-3D deformable registration using weighted deformation parameters to output a heatmap of deformation vectors. Specifically, Chou teach according to a deformation map across a region of said 2D x- ray image (Chou: a heatmap of deformation basis vectors are illustrated for CT images of the lungs [¶0073; Fig. 8]). Chou further discloses that these deformations are learned via distance calculation metrics to optimize the deformation basis vector alignment [¶0073].
Therefore, it would have been obvious to one of ordinary skill before the effective filing date of the present invention to incorporate the deformation maps taught by Chau to the downsampling-based 2D-3D image registration method of Naveb in view of Siewerdsen, further in view of Ketcha to arrive at the invention of the instant application. The motivation for doing so would be to more accurately capture deformable structure and motion in CT images of the chest cavity [¶0073].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al (US 2013/0195338 A1) disclose a 2D-3D registration system tubular structures of the chest airways utilizing similarity score metrics for accurate matching of projections.
Khamene et al (US 2009/0052757 A1) teach a method of deformable registration by determining a vector field of deformation to map 2D projections from a 3D volume onto a 2D image of an object of interest.
Schreiber; Bernd (US 2020/0367837 A1) describe a method for increasing the quality of X-ray tomographs via low and high-pass filters that aid in removing high-contrast regions.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael M. Sofroniou whose telephone number is (571)272-0287. The examiner can normally be reached M-F: 8:30 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M. Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MICHAEL M SOFRONIOU/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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