DETAILED ACTION
This action is in response to the application filed 06/16/2023. Claims 1-16 are pending and have been examined.
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 Objections
Claims 1-3, 5, and 8-16 are objected to because of the following informalities:
Claims 1-3, and 8-10 do not recite a step (a).
Claims 1, 5, and 11 recite reference characters that are not necessary to the understanding of the claim. The recited reference characters should be omitted.
In Claim 9, “temporal dimensional dimensions” on lines 3-4 should read “temporal dimensions”.
Claims 11-16 do not recite a step (A).
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 9-10 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 9 recites the limitation "said initial and enriched n-dimensional feature maps present said spatial dimensions and as a n-th dimension said semantic depth" in lines 4-5. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to which feature maps “said initial… feature maps” are referring to.
Claim 9 recites the limitation “and as a n+1-th dimension a semantic depth” in line 4. It is unclear as to how this limitation relates and corresponds with “the initial n+1 dimensional feature maps present said spatial and temporal dimensional dimensions”. It is unclear whether the feature maps present a semantic depth as an additional dimension, or the semantic depth is used or presented in a different way.
Claim 9 recites the limitation “said initial and enriched n-dimensional feature maps present said spatial dimensions and as a n-th dimension said semantic depth”. It is unclear as to how many spatial dimension there are. Additionally, it is unclear as to whether the semantic depth is one of these dimensions. If the spatial dimensions are n-1, it is unclear as to how the n-dimensional feature maps are presenting spatial dimensions when the spatial dimensions do not equal n.
Claim 9 recites the limitation “and as a n-th dimension a semantic depth” in line 4. It is unclear as to how this limitation relates and corresponds with “said initial and enriched n-dimensional feature maps present said spatial dimensions”. It is unclear whether the feature maps present a semantic depth as an additional dimension, or the semantic depth is used or presented in a different way.
Claim 10 recites the limitation “wherein said skip connections perform a temporal pooling operation”. It is unclear as to how a skip connection performs a pooling operation as the traditional function of a skip connection is sending data. Examiner notes that pooling operations do not occur within the skip connection in traditional skip connections.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-5, 8, 11, and 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Golden et al. (US 2018/0218502 A1) (hereafter referred to as Golden).
Regarding Claim 1, Golden teaches
A method for processing an inputted perfusion sequence presenting n≥3 dimensions including at least two spatial dimensions and one temporal dimension, by means of a convolutional neural network, CNN (Golden, page 56, paragraph 0070, “The trained CNN model may have been trained on one or more of functional cardiac images, myocardial delayed enhancement images or myocardial perfusion images” where “Receiving learning data may include receiving image data which may include at least one of steady-state free precession (SSFP) magnetic resonance imaging (MRI) data or 4D flow MRI data” (Golden, page 53, paragraph 0042) and where “SSFP cine studies contain of 4 dimensions of data (3 space, 1 time), and 4D Flow studies contain 5 dimensions of data (3 space, 1 time, 4 channels of information)” (Golden, page 64, paragraph 0228). Examiner notes that the space dimensions are the spatial dimensions and the time dimension is the temporal dimension.),
comprising an encoder branch, a decoder branch and skip connections between the encoder branch and the decoder branch (Golden, page 70, paragraph 0285, “FIG. 36 shows a schematic representation of a fully convolutional encoder-decoder architecture with skip connections that utilizes a smaller expanding path than contracting path” and Golden, page 37, Figure 36,
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Examiner notes that the encoder branch is highlighted in FIG. 36 in the left box and the decoder branch is highlighted in the right circle.),
the method comprising the implementation, by a data processor (1lb) of a second server (lb), of steps of (Golden, page 69, paragraph 0275, “The system memory 2508 may also include communications programs 3540, for example a server and/or a Web client or browser for permitting the processor-based device 3504 to access and exchange data with other systems such as user computing systems.”):
(b) extracting, using the encoder branch of the CNN, a plurality of initial n+1-dimensional features maps representative of the inputted perfusion sequence at different scales, and projecting, using the skip connections of the CNN, each one of the plurality of initial n+1-dimensional features maps into one of a plurality of initial n-dimensional feature maps (Golden, page 58, paragraph 0126, “The network 600 is configured such that, after every pooling layer 608, the number of feature maps doubles and the spatial resolution is halved. After every upsampling layer 610, the number of feature maps is halved and the spatial resolution is doubled. With the scheme, the number of feature maps for each layer across the network 600 can be fully described by the number (e.g., between 1 and 2000 feature maps) in the first layer” where “ENet utilizes an expanding path that is smaller than its contracting path. ENet also makes use of bottleneck modules, which are convolutions with a small receptive field that are applied in order to project the feature maps into a lower dimensional space” where “subsequent to each upsampling layer, the CNN model may include a concatenation of feature maps from a corresponding layer in the contracting path through a skip connection” (Golden, page 52, paragraph 0028) and “U-Net, originally developed for use in the biomedical community where there are often fewer training images and even finer resolution is required, added the use of skip connections between the contracting and expanding paths to preserve details” (Golden, page 70, paragraph 0286) and Golden, page 37, Figure 36,
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Examiner notes that the plurality of n+1-dimensional features maps are the feature maps within the convolutional layers of the encoder branch shown in the left box. Examiner further notes that the n-dimensional feature maps are the feature maps that are within the convolutional layer that the skip connection is connected or projected to. Examiner additionally notes that the feature maps are projected into a lower dimensional space and thus are n-dimensional feature maps. Examiner further notes that the different scales can be seen in Fig. 36 by the different sizes of layers.);
(c) generating, using said decoder branch of the CNN, a plurality of enriched n- dimensional feature maps also representative of the inputted perfusion sequence at different scales, an enriched n-dimensional feature map at a particular scale incorporating information from the initial n-dimensional feature maps at smaller or equal scale (Golden, page 59, paragraph 0130, “Upsampling the activation volumes back to the original resolution is necessary in a fully convolutional network for pixel-wise segmentation. To increase the resolution of the activation volumes in the network, some systems may use an upsampling operation, then a 2x2 convolution, then a concatenation of feature maps from the corresponding contracting layer through a skip connection, and finally two 3x3 convolutions” where “ENet utilizes an expanding path that is smaller than its contracting path. ENet also makes use of bottleneck modules, which are convolutions with a small receptive field that are applied in order to project the feature maps into a lower dimensional space” and Golden, page 37, Figure 36,
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Examiner notes that the enriched n-dimensional feature maps are the upsampled activation volumes);
(d) generating at least one quantitative map of the inputted perfusion sequence from the enriched n-dimensional feature maps at the largest scale among the different scales (Golden, page 60, paragraph 0175, “Unlike previous models which were only concerned with two classes for a cell discrimination task, foreground and background, the SSFP model disclosed herein attempts to distinguish four classes, namely, background, LV Endocardium, LV Epicardium and RV Endocardium. To accomplish this, the network output may include three probability maps, one for each non-background class” and Golden, page 37, Figure 36,
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Examiner notes that the quantitative map is the probability map as shown in Figure 36.).
Regarding Claim 2, Golden teaches
A method according to claim 1, wherein, for each enriched n- dimensional feature map, an initial n-dimensional feature map of the same scale is provided from the encoder branch to the decoder branch via a dedicated skip connection (Golden, page 58, paragraph 0126, “The network 600 is configured such that, after every pooling layer 608, the number of feature maps doubles and the spatial resolution is halved. After every upsampling layer 610, the number of feature maps is halved and the spatial resolution is doubled. With the scheme, the number of feature maps for each layer across the network 600 can be fully described by the number (e.g., between 1 and 2000 feature maps) in the first layer” where “ENet utilizes an expanding path that is smaller than its contracting path. ENet also makes use of bottleneck modules, which are convolutions with a small receptive field that are applied in order to project the feature maps into a lower dimensional space” and Golden, page 37, Figure 36,
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Examiner notes that the plurality of n+1-dimensional features maps are the feature maps within the convolutional layers of the encoder branch shown in the left box. Examiner further notes that the n-dimensional feature maps are the feature maps that are within the convolutional layer that the skip connection is connected or projected to. Examiner additionally notes that the feature maps are projected into a lower dimensional space and thus are n-dimensional feature maps.).
Regarding Claim 3, Golden teaches,
The method according to one of claim 1,wherein, at step (c), the enriched n-dimensional feature map at the smallest scale among the different scales is generated from the initial n+1 -dimensional feature map at the smallest scale among the different scales, and each enriched n-dimensional feature map at another scale than the smallest scale is generated from the initial n-dimensional feature map at the same another scale and a enriched n-dimensional feature map at a smaller scale than the another scale (Golden, page 37, Figure 36,
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Examiner notes that the circled layer is the enriched n-dimensional feature map at the smallest scale and the boxed layer is the initial n+1-dimensional feature map at the smallest scale. Examiner further notes that the other upsample layers or enriched feature maps are generated from the convolution layer or n-dimensional feature map at the same scale as well as upsample layers of a smaller scale.).
Regarding Claim 4, Golden teaches
The method according to claim 1,further comprising a previous step (a) of obtaining the perfusion sequence by stacking a plurality of successive images of a perfusion (Golden, page 73, paragraph 0314, “Perfusion imaging using gadolinium-based con trast is used to identify biomarkers of coronary stenosis. Late gadolinium enhancement imaging, also using gadolinium based contrast, is used to assess myocardial infarction. In all of these imaging protocols, and in others, the anatomical orientations and the need for contouring tend to be similar. Images are typically acquired both in short axis orientations, in which the imaging plane is parallel to the short axis of the left ventricle, and in long axis orientations, in which the imaging plane is parallel to the long axis of the left ventricle” where “The short axis (SAX) view, which consists of a series of slices along the long axis of the left ventricle. Each slice is in the plane of the short axis of the left ventricle, which is orthogonal to the ventricle's long axis;” (Golden, page 49, paragraph 0003) and where “Example LV endocardium contours are shown as images 100a-100k in FIG. 1, which shows the contours at a single time point over a full SAX stack. From 100a to 100k, the slices proceed from the apex of the left ventricle to the base of the left ventricle” (Golden, page 49, paragraph 0008). Examiner notes that the perfusion imaging uses SAX views which are stacks of successive images. ).
Regarding Claim 5, Golden teaches
The method according to claim 4, wherein said successive images of a perfusion are acquired by a medical imaging device (10) connected to the second server (la) (Golden, page 61, paragraph 0185, “Inference is the process of utilizing a trained model for prediction on new data. In at least some implementations, a web application (or "web app") may be used for inference. FIG. 9 displays an example pipeline or process 900 by which predictions may be made on new SSFP studies. At 902, after a user has loaded a study in the web application, the user may invoke the inference service (e.g., by clicking a "generate missing contours" icon), which automatically generates any missing (not yet created) contours. Such contours may include LV Endo, LV Epi, or RV Endo, for example. In at least some implementations, inference may be invoked automatically when the study is either loaded by the user in the application or when the study is first uploaded by the user to a server. If inference is performed at upload time, the predictions may be stored in a nontransitory processor-readable storage medium at that time but not displayed until the user opens the study” where “Depending on the type of acquisition, these views may be captured directly in the scanner (e.g., steady-state free precession (SSFP) MRI) or may be created via multi planar reconstructions (MPRs) of a volume aligned in a different orientation (such as the axial, sagittal or coronal planes, e.g., 4D Flow MRI). The SAX view has multiple spatial slices, usually covering the entire volume of the heart, but the 2CH, 3CH and 4CH views often only have a single spatial slice. All series are cine, and have multiple time points encompassing a complete cardiac cycle” (Golden, page 49, paragraph 0004). Examiner notes that an MRI acquires the successive images and is connected to a server through inferencing.).
Regarding Claim 8, Golden teaches
The method according to claim 1,wherein said CNN is fully convolutional (Golden, page 51, paragraph 0028, “A machine learning system may be summarized as including at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data; and at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, the at least one processor: … trains a fully convolutional neural network (CNN) model to segment at least one part of the anatomical structure utilizing tl1e received learning data”).
Regarding Claim 11, Golden teaches,
A method for training a convolution neural network, CNN, for processing an inputted perfusion sequence presenting n≥3 dimensions including at least two spatial dimensions and one temporal dimension, (Golden, page 56, paragraph 0070, “The trained CNN model may have been trained on one or more of functional cardiac images, myocardial delayed enhancement images or myocardial perfusion images” where “Receiving learning data may include receiving image data which may include at least one of steady-state free precession (SSFP) magnetic resonance imaging (MRI) data or 4D flow MRI data” (Golden, page 53, paragraph 0042) and where “SSFP cine studies contain of 4 dimensions of data (3 space, 1 time), and 4D Flow studies contain 5 dimensions of data (3 space, 1 time, 4 channels of information)” (Golden, page 64, paragraph 0228). Examiner notes that the space dimensions are the spatial dimensions and the time dimension is the temporal dimension.),
Wherein the CNN comprises an encoder branch, a decoder branch and skip connections between the encoder branch and the decoder branch (Golden, page 70, paragraph 0285, “FIG. 36 shows a schematic representation of a fully convolutional encoder-decoder architecture with skip connections that utilizes a smaller expanding path than contracting path” and Golden, page 37, Figure 36,
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Examiner notes that the encoder branch is highlighted in FIG. 36 in the left box and the decoder branch is highlighted in the right circle.),
Wherein the method comprises the implementation, by a data processor (11a) of a first server (1a), for each of a plurality of training perfusion sequence from a base of training perfusion sequences each associated to an expected quantitative map of the perfusion, of steps of (Golden, page 74, paragraph 0327 “In some implementations, the data on which the CNN model 4604 has been trained is data from functional cardiac magnetic resonance imaging (e.g., via a contrast-free SSFP imaging sequence) and the cardiac image data is data from a cardiac perfusion or myocardial delayed enhancement study” where “The system memory 2508 may also include communications programs 3540, for example a server and/or a Web client or browser for permitting the processor-based device 3504 to access and exchange data with other systems such as user computing systems” (Golden, page 69, paragraph 0275) where “the output of the network is eighteen scalars corresponding to three coordinates for each of the six landmarks in the input image. Such an architecture may be trained in a similar fashion to the previously described landmark detector. The only update needed is the re-formulation of the loss to account for the change in the network output format (a spatial point in this implementation, as opposed to the probability map used in the first implementation). One reasonable loss function may be the L2 (squared) distance between the output of the network and the real landmark coordinate, but other loss functions may be used as well, as long as the loss functions are related to the quantity of interest, namely the distance error” (Golden, page 68, paragraph 0263) and “Typically, around 20 3D volumetric images are acquired throughout a single cardiac cycle, each corresponding to one snapshot of the heartbeat. The initial database thus corresponds to the 3D images of different patients at different time steps. Each 3D MRI presents a number of landmark annotations, from zero landmark to six landmarks, placed by the user of the web application. The landmark annotations, if present, are stored as vectors of coordinates (x, y, z, t) indicating the position (x, y, z) of the landmark in the 3D MRI corresponding to the time point t” (Golden, page 65, paragraph 0237). Examiner notes that the real landmark coordinate is the expected quantitative map.)
(b) extracting, using the encoder branch of the CNN, a plurality of initial n+1-dimensional features maps representative of the training perfusion sequence at n≥3 different scales, and projecting, using the skip connections of the CNN, each one of the plurality of initial n+1-dimensional features maps into one of a plurality of initial n-dimensional feature maps (Golden, page 58, paragraph 0126, “The network 600 is configured such that, after every pooling layer 608, the number of feature maps doubles and the spatial resolution is halved. After every upsampling layer 610, the number of feature maps is halved and the spatial resolution is doubled. With the scheme, the number of feature maps for each layer across the network 600 can be fully described by the number (e.g., between 1 and 2000 feature maps) in the first layer” where “ENet utilizes an expanding path that is smaller than its contracting path. ENet also makes use of bottleneck modules, which are convolutions with a small receptive field that are applied in order to project the feature maps into a lower dimensional space” where “subsequent to each upsampling layer, the CNN model may include a concatenation of feature maps from a corresponding layer in the contracting path through a skip connection” (Golden, page 52, paragraph 0028) and “U-Net, originally developed for use in the biomedical community where there are often fewer training images and even finer resolution is required, added the use of skip connections between the contracting and expanding paths to preserve details” (Golden, page 70, paragraph 0286) and Golden, page 37, Figure 36,
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Examiner notes that the plurality of n+1-dimensional features maps are the feature maps within the convolutional layers of the encoder branch shown in the left box. Examiner further notes that the n-dimensional feature maps are the feature maps that are within the convolutional layer that the skip connection is connected or projected to. Examiner additionally notes that the feature maps are projected into a lower dimensional space and thus are n-dimensional feature maps. Examiner further notes that the different scales can be seen in Fig. 36 by the different sizes of layers.);
(c) generating, using said decoder branch of the CNN, a plurality of enriched n- dimensional feature maps also representative of the inputted perfusion sequence at different scales, an enriched n-dimensional feature map at a particular scale incorporating information from the initial n-dimensional feature maps at smaller or equal scale (Golden, page 59, paragraph 0130, “Upsampling the activation volumes back to the original resolution is necessary in a fully convolutional network for pixel-wise segmentation. To increase the resolution of the activation volumes in the network, some systems may use an upsampling operation, then a 2x2 convolution, then a concatenation of feature maps from the corresponding contracting layer through a skip connection, and finally two 3x3 convolutions” where “ENet utilizes an expanding path that is smaller than its contracting path. ENet also makes use of bottleneck modules, which are convolutions with a small receptive field that are applied in order to project the feature maps into a lower dimensional space” and Golden, page 37, Figure 36,
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Examiner notes that the enriched n-dimensional feature maps are the upsampled activation volumes);
(d) generating at least one quantitative map of the inputted perfusion sequence from the enriched n-dimensional feature maps at the largest scale among the different scales (Golden, page 60, paragraph 0175, “Unlike previous models which were only concerned with two classes for a cell discrimination task, foreground and background, the SSFP model disclosed herein attempts to distinguish four classes, namely, background, LV Endocardium, LV Epicardium and RV Endocardium. To accomplish this, the network output may include three probability maps, one for each non-background class” and Golden, page 37, Figure 36,
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Examiner notes that the quantitative map is the probability map as shown in Figure 36.).
And minimizing a distance with the expected quantitative map of the perfusion (Golden, page 68, paragraph 0263, “the output of the network is eighteen scalars corresponding to three coordinates for each of the six landmarks in the input image. Such an architecture may be trained in a similar fashion to the previously described landmark detector. The only update needed is the re-formulation of the loss to account for the change in the network output format (a spatial point in this implementation, as opposed to the probability map used in the first implementation). One reasonable loss function may be the L2 (squared) distance between the output of the network and the real landmark coordinate, but other loss functions may be used as well, as long as the loss functions are related to the quantity of interest, namely the distance error” where “Beginning with random noise as a model "input" and a real segmentation mask as the target, we perform backpropagation to update the pixel values in the input image such that the loss is minimized” (Golden, page 72, paragraph 0301). Examiner notes that the loss function computes the distance with the real landmark coordinate and since the loss is minimized, the distance that the loss function is computing is also minimized.).
Regarding Claim 15, Golden teaches
A non-transitory computer-readable medium comprising code instructions that, when executed by a computer, cause the computer to execute a method according to claim 1 for processing an inputted perfusion sequence (Golden, page 54, paragraph 0058, “A machine learning system may be summarized as including at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data; and at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, the at least one processor: receives a plurality of sets of 3D MRJ images, the images in each of the plurality of sets represent an anatomical structure of a patient; receives a plurality of annotations for the plurality of sets of 3D MRI images, each annotation indicative of a landmark of an anatomical structure of a patient depicted in a corresponding image; trains a convolutional neural network (CNN) model to predict the locations of the plurality of landmarks utilizing the 3D MRI images; and stores the trained CNN model in the at least one nontransitory processor- readable storage medium of the machine learning system.”).
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.
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golden in view of Hess et al. (“Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep Learning”)(hereafter referred to as Hess).
Regarding Claim 6, Golden teaches,
The method according to claim 5, wherein said medical imaging device is a Magnetic Resonance Imaging, MRI, scanner (Golden, page 49, paragraph 0004, “Depending on the type of acquisition, these views may be captured directly in the scanner (e.g., steady-state free precession (SSFP) MRI) or may be created via multi planar reconstructions (MPRs) of a volume aligned in a different orientation (such as the axial, sagittal or coronal planes, e.g., 4D Flow MRI). The SAX view has multiple spatial slices, usually covering the entire volume of the heart, but the 2CH, 3CH and 4CH views often only have a single spatial slice. All series are cine, and have multiple time points encompassing a complete cardiac cycle”).
Golden does not teach, but Hess does teach
and the perfusion sequence is a Dynamic susceptibility Contrast, DSC, or a Dynamic Contrast Enhanced, DCE, perfusion sequence (Hess, page 1, abstract, “In this work, we present a novel convolutional neural network based method for perfusion map generation in dynamic susceptibility contrast-enhanced perfusion imaging”).
Golden and Hess are considered analogous to the claimed invention because they both process perfusion sequences in CNNs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Golden to use the DSC perfusion sequences from Golden. Doing so is a simple substitution of one known element (perfusion sequences from Golden) for another (DSC perfusion sequences from Hess) to obtain predictable results (processing perfusion sequences).
Regarding Claim 7, Golden teaches the method of claim 4. Golden does not teach, but Hess does teach
wherein previous step (a) comprises extracting patches of a predetermined size from the perfusion sequence, steps (b) to (d) being performed for each extracted patch (Hess, page 3, 1st paragraph, “Guided by the fact that those characteristics are captured best by large blood vessels entering the brain, we select the input to the BCS to be a patch sequence from the perfusion sequence, located at the transition between the basilar artery and the posterior cerebral artery. The location of this patch is globally fixed, i.e., it is not fine-tuned to the individual volume. Therefore, it may happen that this patch does not contain the desired blood vessels for specific instances in our data. The BCS processes the supplied patch sequence via two 3D convolutional layers, encoding each patch into a vector of size 16. The sequence of encoded patches is forwarded to the sequence encoder.” Examiner notes that the patches are from the perfusion sequence and thus is extracted. Examiner further notes that the patches are a predetermined size of 16 before being forwarded on for processing.).
Golden and Hess are considered analogous to the claimed invention because they both process perfusion sequences in CNNs. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Golden to extract patches like in Hess prior to steps b to d. Doing so is advantageous because “our method generates perfusion maps that are comparable to the target maps used for clinical routine, while being model-free, fast, and less noisy” (Hess, page 1, abstract).
Claim(s) 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golden in view of Ramon et al. (“Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks”)(hereafter referred to as Ramon).
Regarding claim 12, Golden teaches the method according to claim 11. Golden does not teach, but Ramon does teach
previously comprising generating at least one degraded version of at least one original training perfusion sequence of the training base (Ramon, page 2, 2nd column, 2nd paragraph, “Let vector x denote a reconstructed image volume from a low-dose SPECT-MPI acquisition, and vector y the corresponding image reconstructed from a standard full-dose acquisition of a given subject. Our goal is to determine a mapping from x to y such that y
≈
f
(
x
)
. (1) In (1), the reconstructed image x, with lower data counts, represents a noisy version of image y (with full-dose data counts)” where “Specifically, assume a total of T such training image pairs are available. Then the training dataset is formed by input-output pairs as : {(x(i), y(i)), i=1,…,T} (2) where x(i) denotes the low-dose image from patient i, and y(i) the corresponding full-dose image” “Specifically, assume a total of T such training image pairs are available. Then the training dataset is formed by input-output pairs as : {(x(i), y(i)), i=1,…,T} (2) where x(i) denotes the low-dose image from patient i, and y(i) the corresponding full-dose image” (Ramon, page 2, 2nd column, 3rd paragraph). Examiner notes that the degraded version is x and the at least one original training perfusion sequence of the training base is y.),
associating to said degraded version the expected quantitative map of the perfusion associated with the original training perfusion sequence (Ramon, page 2, 3rd paragraph, “Once optimized,
f
^
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y
~
=
f
^
(
x
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and enriching the training base by adding said degraded version (Ramon, page 2, 2nd column, 3rd paragraph, “Specifically, assume a total of T such training image pairs are available. Then the training dataset is formed by input-output pairs as : {(x(i), y(i)), i=1,…,T} (2) where x(i) denotes the low-dose image from patient i, and y(i) the corresponding full-dose image.” Examiner notes that the low-dose image is the degraded version. ).
Golden and Ramon are considered analogous to the claimed invention because they both use encoders and decoders to process perfusion images. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Golden to create a degraded image and add it to the training base. Doing so is advantageous because it “can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data” (Ramon, page 10, Conclusions, 2nd paragraph).
Regarding claim 13, Golden in view of Ramon teaches the method according to claim 12. Golden in view of Ramon further teaches,
wherein said original training perfusion sequence is associated to a contrast product dose, said degraded version of the original training perfusion sequence simulating a lower contrast product dose (Ramon, page 5, 1st column, 1st paragraph “In the first approach, we train the 3D CAE/CNN to learn the mapping from a specific reduced-dose level (i.e., 1/2, 1/4, 1/8, or 1/16 dose) to full dose. That is, the denoising network is obtained specifically for a given dose level” where “Specifically, assume a total of T such training image pairs are available. Then the training dataset is formed by input-output pairs as : {(x(i), y(i)), i=1,…,T} (2) where x(i) denotes the low-dose image from patient i, and y(i) the corresponding full-dose image” “Specifically, assume a total of T such training image pairs are available. Then the training dataset is formed by input-output pairs as : {(x(i), y(i)), i=1,…,T} (2) where x(i) denotes the low-dose image from patient i, and y(i) the corresponding full-dose image” (Ramon, page 2, 2nd column, 3rd paragraph). Examiner notes that the reduced dose level is simulated with with a low-dose image, and the full-dose is associated to the full-dose image. ).
Golden and Ramon are considered analogous to the claimed invention because they both use encoders and decoders to process perfusion images. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Golden to create a degraded image and add it to the training base. Doing so is advantageous because it “can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data” (Ramon, page 10, Conclusions, 2nd paragraph).
Allowable Subject Matter
Claims 14 and 16 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.
Specifically, regarding claim 14, “wherein the degraded version of the original training perfusion sequence simulating a lower contrast product dose is generated by calculating, for each voxel of the original training perfusion sequence, from a temporal signal S(t) of said voxel a degraded temporal signal Sd(t) using the formula Sd(t) = S(t) - (1 - d) -[
S
(
t
)
-
- S(0)], wherein
S
(
t
)
-
is a local average of the temporal signal S(t), and d is a dose reduction factor” in conjunction with the other limitations of the claims are not taught by the prior art of record.
The closest prior art is Ramon and Wu et al. (“Physiological Modulations in Arterial Spin Labeling Perfusion Magnetic Resonance Imaging”)(hereafter referred to as Wu).
Ramon discloses generating the degraded version of the original training perfusion sequence simulating a lower contrast product does using signals (Ramon, page 5, Section III A, 2nd paragraph). Ramon also discloses using voxels of the original training perfusion sequence (Ramon, page 2, 1st column, 3rd paragraph). Ramon does not disclose calculating for each voxel, from a temporal signal. Ramon also does not disclose the formula.
Wu discloses an average of temporal signals (Wu, page 3, equation 3), but does not disclose a degraded version of the original training perfusion sequence.
Therefore, the prior art of record, individually, or in combination, does not disclose claim 14 as a whole. Claim 16 would be allowable at least due to its dependencies on claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kim et al. (“Improving resolution of MR images with an adversarial network incorporating images with different contrast”) uses encoders and decoders to generate higher resolution images from low resolution images. Kim et al. also uses a discriminator CNN to determine which images are generated and which are not.
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/K.R.H./Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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