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 .
Response to Arguments
Applicant's arguments below filed 12/30/2025 have been fully considered but they are not persuasive | moot in view of the new grounds of rejection.
The Applicant asserts on page 14 of the Response:
“In the Office Action, Zhong is cited as allegedly disclosing MR data comprising an in-phase image, opposed phase image, water-only image, and fat-only image. Zhong, however does not disclose or suggest MR data that comprises "a plurality of representations of a first examination region within the object after administration of a first dose of a contrast agent, wherein the MR data comprise an in-phase image, an opposed phase image, a water-only image, and a fat-only image," as recited by amended claim 1.”
In response the examiner respectfully asserts that Zhong was not cited in the previous office action as disclosing MR data comprising an in-phase image, opposed phase image, water-only image, and fat-only image. In the previous office action Zhong was cited in claim 2 to teach the MR data comprise one or more of: an in-phase image, an opposed phase image, a water-only image, and a fat-only image. Zhong taught this by disclosing MR data comprising a water-only image and a fat-only image. The amendments to the independent claims require all four images therefore a new ground of rejection has been made. The remainder of applicant’s arguments with respect to claims 1 and 3-16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Drawings
The drawing amendments filed 12/30/2025 have been accepted by the examiner.
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 5, 8, and 9 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.
Regarding claims 5 and 8, lines 1-3 of claim 5 and lines 1-3 of claim 8 recite “wherein the MR data comprise, for each object of the plurality of objects, first MR data, second MR data, and pre-contrast MR data”. However claim 1 defines the MR data as “a plurality of representations of a first examination region within the object after administration of a first dose of a contrast agent”. The limitation of claims 5 and 8 is written as the pre-contrast MR data being part of the MR data however the MR data is defined as MR data after administration of a contrast agent. Therefore it is unclear how the pre-contrast MR data would be both a representation of the examination region after administration of a first dose of a contrast agent and without the contrast agent. For examination purposes claims 5 and 8 will be interpreted as “wherein the MR data comprise, for each object of the plurality of objects, first MR data, and second MR data, the training data set also comprising, for each object of the multitude of objects, pre-contrast MR data”.
Claim 9 is also rejected due to its dependency.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-8, and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Tamir (WO 2021061710) and further in view of Weiss (US 11580626).
Regarding claims 1 and 11-13, Tamir discloses a computer-implemented method ([0009] – “computer-implemented method”) comprising: [claim 1]
a computer system ([0080] – “a computer system 410”) comprising: a processor ([0081] – “The processor”); and a memory storing an application program configured to perform, when executed by the processor, an operation ([0010] – “a non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations”), the operation comprising: [claim 11]
a non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute ([0010] – “a non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations”) the following steps: [claim 12]
a system comprising a magnetic resonance (MR) contrast agent ([0032] – “Gadolinium-based contrast agents, MRI data examples are primarily provided herein”) and a non-transitory computer readable medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute ([0010] – “a non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations”) the following steps: [claim 13]
- receiving a training data set, the training data set comprising, for each object of a plurality of objects, MR data, and target MR data ([0045] – “data for training the model, multiple scans with reduced dose level as well as a full-dose scan may be performed”),
wherein the MR data comprise a plurality of representations of a first examination region within the object after administration of a first dose of a contrast agent ([0057] – “in order to train the deep learning network, pairs of pre-contrast and low-dose images as input and the full-dose image as the ground truth from multiple subjects”, the low-dose images are interpreted to be the MR data), and
wherein the target MR data represent at least a portion of the first examination region within the object after administration of a second dose of the contrast agent, wherein the second dose is different from the first dose ([0057] – “in order to train the deep learning network, pairs of pre-contrast and low-dose images as input and the full-dose image as the ground truth from multiple subjects”, the full-dose images are interpreted to be the target MR data);
- training a machine learning model, thereby obtaining a trained machine learning model ([0055] – “the deep learning model may be trained”), wherein the training comprises:
inputting the MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the MR data and model parameters, predicted MR data ([0047] – “a scan with reduced contrast dose (e.g., similar to scan 2) may be performed. Such input image data may then be processed by the trained model to output a predicted MPR reconstructed image with enhanced contrast”),
receiving from the machine learning model the predicted MR data ([0047] – “output a predicted MPR reconstructed image with enhanced contrast”),
computing a loss value, the loss value quantifying deviations between the predicted MR data and the target MR data ([0073] – “structural similarity index (SSIM) is sensitive to changes in local structure and hence captures the structural aspect of the predicted image with respect to the ground truth”), and
modifying one or more of the model parameters to minimize the loss value ([0061] – “The network may be trained with a combination of L1 (mean absolute error) and structural similarity index (SSIM) losses. Such U-Net style encoder-decoder network architecture may be capable of producing a linear 10x scaling of the contrast uptake between low-dose and zero-dose, without picking up noise along with the enhancement signal.”); and
- outputting the trained machine learning model, and/or storing the machine learning model on a data storage, and/or providing the trained machine learning model for predictive purposes ([0004] – “a generalized deep learning (DL) model is utilized to predict contrast-enhanced images”, [0069] – “The provided model was trained”).
Conversely Tamir does not teach wherein the MR data comprise an in-phase image, an opposed phase image, a water-only image, and a fat-only image, with some or all images acquired according to one or more of: a DIXON-type sequence protocol, a T1-weighted DIXON-type sequence protocol, and a T1-VIBE-DIXON-type sequence protocol.
However Weiss discloses wherein the MR data comprise an in-phase image, an opposed phase image, a water-only image, and a fat-only image, with some or all images acquired according to one or more of: a DIXON-type sequence protocol, a T1-weighted DIXON-type sequence protocol, and a T1-VIBE-DIXON-type sequence protocol (Col. 1 lines 23-25 – “in-phase (IP) and opposed-phase (OP) images and derive water (W) and fat (F) images”, Col. 2 lines 46-49 – “FIG. 1A shows a series of traditional gray-scale MRI images of the head and neck of a person where the OP (12) and IP (14) images were acquired using a Dixon sequence and the W (16) and F (18) were derived from the OP (12) and IP (14) images”).
The disclosure of Weiss is an analogous art considering it is in the field of machine learning trained with MRI images.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamir to incorporate the Dixon sequence to obtain fat only, water only, in-phase, and opposed-phase images of Weiss to achieve the same results. One would have motivation to combine because “Radiologists will often examine and/or compare several of the four images to make a diagnosis” (Weiss Col. 1 lines 28-30).
Regarding claim 3, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the machine learning model is configured to generate the predicted MR data without making use of MR data acquired prior to the administration of the first dose of the contrast agent ([0047] – “only one scan without contrast agent (e.g., similar to scan 1), or a scan with reduced contrast dose (e.g., similar to scan 2) may be performed”).
Regarding claim 4, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the MR data comprise, for each object of the plurality of objects, first MR data and second MR data (Fig. 12 shows MR data [low-dose] from six different scanners),
wherein the first MR data represent the first examination region within the object after administration of the first dose of the contrast agent (Fig. 12 shows MR data [low-dose] from six different scanners), wherein the first MR data were acquired according to a first MR sequence protocol (The sites and scanners shown in Fig. 12 are the same sites and scanners listed in Fig. 10, [0071] – “The study retrospectively identified 640 patients (323 females; 52 ± 16 years), undergoing clinical brain MRI exams from three institutions, three scanner manufacturers and eight scanner models using different institutional scan protocols” therefore there is a first MR sequence protocol),
wherein, the second MR data represent the first examination region within the object after the administration of the first dose of the contrast agent (Fig. 12 shows MR data [low-dose] from six different scanners), wherein the second MR data were acquired according to a second MR sequence protocol, wherein the second MR sequence protocol differs from the first MR sequence protocol (The sites and scanners shown in Fig. 12 are the same sites and scanners listed in Fig. 10, [0071] – “The study retrospectively identified 640 patients (323 females; 52 ± 16 years), undergoing clinical brain MRI exams from three institutions, three scanner manufacturers and eight scanner models using different institutional scan protocols” therefore there is at least a first and a second MR sequence protocols that are different from each other), and
wherein the training of the machine learning model comprises:
inputting the first MR data, and the second MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the first MR data, the second MR data, and model parameters, predicted MR data ([0071] – “The study retrospectively identified 640 patients”, [0072] – “Out of 640 cases, the model as shown in FIG. 11 was trained with 56 cases, and 13 validation cases were used to fine-tune the hyper-parameters and empirically find the optimal combination of loss weights. To ensure that the model generalizes well across sites and vendors, the train and validation sets consisted of approximately an equal number of studies from all the institutions and scanner manufacturers (refer FIG. 10).”, Fig. 12 shows the synthesized MR image for each site and scanner).
Regarding claim 5, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the MR data comprise, for each object of the plurality of objects, first MR data, second MR data, and pre-contrast MR data (Fig. 12 shows MR data [low-dose] and pre-contrast MR data from six different scanners),
wherein the first MR data represent the first examination region within the object after administration of the first dose of the contrast agent (Fig. 12 shows MR data [low-dose] from six different scanners), wherein the first MR data were acquired according to a first MR sequence protocol (The sites and scanners shown in Fig. 12 are the same sites and scanners listed in Fig. 10, [0071] – “The study retrospectively identified 640 patients (323 females; 52 ± 16 years), undergoing clinical brain MRI exams from three institutions, three scanner manufacturers and eight scanner models using different institutional scan protocols” therefore there is a first MR sequence protocol),
wherein, the second MR data represent the first examination region within the object after the administration of the first dose of the contrast agent (Fig. 12 shows MR data [low-dose] from six different scanners), wherein the second MR data were acquired according to a second MR sequence protocol, wherein the second MR sequence protocol differs from the first MR sequence protocol (The sites and scanners shown in Fig. 12 are the same sites and scanners listed in Fig. 10, [0071] – “The study retrospectively identified 640 patients (323 females; 52 ± 16 years), undergoing clinical brain MRI exams from three institutions, three scanner manufacturers and eight scanner models using different institutional scan protocols” therefore there is at least a first and a second MR sequence protocols that are different from each other),
wherein, the pre-contrast MR data represent the first examination region within the object without the contrast agent (Fig. 12 shows pre-contrast MR data from six different scanners), wherein the pre-contrast MR data were acquired according to the first MR sequence protocol, the second MR sequence protocol, or a third MR sequence protocol, wherein the third MR sequence protocol differs from the first MR sequence protocol and/or from the second MR sequence protocol (The sites and scanners shown in Fig. 12 are the same sites and scanners listed in Fig. 10, [0071] – “The study retrospectively identified 640 patients (323 females; 52 ± 16 years), undergoing clinical brain MRI exams from three institutions, three scanner manufacturers and eight scanner models using different institutional scan protocols” therefore the pre-contrast MR data could be from the one of the different scanners and protocols used for the first MR data and the second MR data or it could be from a scanner and protocol different from the first MR data and the second MR data), and
wherein the training of the machine learning model comprises:
inputting the first MR data, the second MR data, and the pre-contrast MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the first MR data, the second MR data, the pre-contrast MR data and model parameters, predicted MR data ([0071] – “The study retrospectively identified 640 patients”, [0072] – “Out of 640 cases, the model as shown in FIG. 11 was trained with 56 cases, and 13 validation cases were used to fine-tune the hyper-parameters and empirically find the optimal combination of loss weights. To ensure that the model generalizes well across sites and vendors, the train and validation sets consisted of approximately an equal number of studies from all the institutions and scanner manufacturers (refer FIG. 10).”, Fig. 12 shows the synthesized MR image for each site and scanner).
Regarding claim 6, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the MR data comprise, for each object of the plurality of objects, first MR data, and second MR data ([0036] – “multiple adjacent slices are input to a network”),
wherein the first MR data represent the first examination region within the object after administration of the first dose of the contrast agent, wherein the first MR data were acquired according to a first MR sequence protocol ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, the central slice in the stack of slices is interpreted as the first MR data which would be acquired according to a first MR sequence protocol),
wherein, the second MR data represent a second examination region within the object after the administration of the first dose of the contrast agent ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, one of the other slices in the stack of slices aside from the central slice is interpreted as the second MR data), […]
wherein the target MR data represent the first examination region within the object after administration of the second dose of the contrast agent, wherein the second dose is different from the first dose ([0057] – “in order to train the deep learning network, pairs of pre-contrast and low-dose images as input and the full-dose image as the ground truth from multiple subjects”, the full-dose images are interpreted to be the target MR data, as shown in Fig. 12 the full-dose image is the same slice position as the low-dose image), and
wherein the training of the machine learning model comprises:
inputting the first MR data, and the second MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the first MR data, the second MR data, and model parameters, predicted MR data ([0062] – “seven slices each of pre-contrast and low-dose images are stacked channel-wise to create a 14-channel input volumetric data for training the model to predict the central full-dose slices 803”, [0082] – “trained model (e.g., hyper parameters)”).
Conversely Tamir does not explicitly teach wherein the second MR data were acquired according to the first MR sequence protocol or a second MR sequence protocol. However one with ordinary skill in the art would find it obvious that the slices adjacent to the central slice would either have the same MR sequence protocol as the central slice or a different [second] MR sequence protocol.
Regarding claim 7, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the MR data comprise, for each object of the plurality of objects, first MR data, and second MR data ([0036] – “multiple adjacent slices are input to a network”),
wherein the first MR data represent the first examination region within the object after administration of the first dose of the contrast agent, wherein the first MR data were acquired according to a first MR sequence protocol ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, the central slice in the stack of slices is interpreted as the first MR data which would be acquired according to a first MR sequence protocol),
wherein the second MR data represent a number of additional examination regions within the object after the administration of the first dose of the contrast agent, wherein the additional examination regions together with the first examination region form a stack of adjacent and/or partially overlapping slices ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, one of the other slices in the stack of slices aside from the central slice is interpreted as the second MR data), […]
wherein the training of the machine learning model comprises:
inputting the first MR data, and the second MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the first MR data, the second MR data, and model parameters, predicted MR data ([0062] – “seven slices each of pre-contrast and low-dose images are stacked channel-wise to create a 14-channel input volumetric data for training the model to predict the central full-dose slices 803”, [0082] – “trained model (e.g., hyper parameters)”).
Conversely Tamir does not explicitly teach wherein the second MR data were acquired according to the first MR sequence protocol or a second MR sequence protocol. However one with ordinary skill in the art would find it obvious that the slices adjacent to the central slice would either have the same MR sequence protocol as the central slice or a different [second] MR sequence protocol.
Regarding claim 8, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the MR data comprise, for each object of the plurality of objects, first MR data, second MR data, and pre-contrast MR data ([0036] – “multiple adjacent slices are input to a network”, Fig. 12 shows pre-contrast MR data from six different scanners),
wherein the first MR data represent the first examination region within the object after administration of the first dose of the contrast agent, wherein the first MR data were acquired according to a first MR sequence protocol, wherein the first MR data preferably comprise more than one representation of the first examination region ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, [0040] – “the input 2.5D volumetric image may be reformatted into multiple axes such as principal axes (e.g., sagittal, coronal, and axial) to generate multiple reformatted volumetric images 121”, therefore one with ordinary skill in the art would recognize a plurality of slices in the axial direction for example can be interpreted as the first MR data and the first MR data would be acquired according to a first MR sequence protocol),
wherein the second MR data represent a number of additional examination regions within the object after the administration of the first dose of the contrast agent, wherein the additional examination regions together with the first examination region form a stack of adjacent and/or partially overlapping slices, […], wherein the second MR data comprise more than one representation of at least some of the additional examination regions ([0036] – “multiple adjacent slices are input to a network”, [0039] – “a selected number of input slices of the pre-contrast or low-dose images 110 may be stacked channel-wise to create a 2.5D volumetric input image”, [0040] – “the input 2.5D volumetric image may be reformatted into multiple axes such as principal axes (e.g., sagittal, coronal, and axial) to generate multiple reformatted volumetric images 121”, therefore one with ordinary skill in the art would recognize a plurality of slices in the sagittal or coronal direction can be interpreted as the second MR data and each slice would be partially overlapping the slices in the axial direction of the first MR data),
wherein the pre-contrast MR data represent the first examination region and/or the second examination region without the contrast agent, wherein the pre-contrast MR data were acquired according to the first MR sequence protocol or the second MR sequence protocol or a third sequence protocol ([0045] – “two 3D T1-weighted images were obtained: pre-contrast and post-10% dose contrast (0.01 mmol/kg)”, Fig. 12 shows pre-contrast images that are at the same slice location as the low-dose image for each scanner, additionally images formed by the same scanner are acquired according to the same MR sequence protocol as recited in [0046] – “experiments shown in FIGs. 2, 3, and 10-12. In the illustrated scan protocol, each patient underwent three scans in a single imaging session.”), and
wherein the training of the machine learning model comprises:
inputting the first MR data, the second MR data, and optionally the pre-contrast MR data into the machine learning model, wherein the machine learning model is configured to generate, at least partially on the basis of the first MR data, the second MR data, optionally the third MR data and model parameters, predicted MR data ([0041] – “each of the three reformatted volumetric images 121 (e.g., sagittal, coronal, and axial) may be rotated”, [0042] – “The plurality of rotated volumetric 2.5D images 122 may then be fed to the 2.5D trained model 131”, [0062] – “seven slices each of pre-contrast and low-dose images are stacked channel-wise to create a 14-channel input volumetric data for training the model to predict the central full-dose slices 803”).
Conversely Tamir does not explicitly teach wherein the second MR data were acquired according to the first MR sequence protocol or a second MR sequence protocol. However one with ordinary skill in the art would find it obvious that the slices adjacent to the central slice would either have the same MR sequence protocol as the central slice or a different [second] MR sequence protocol.
Regarding claim 10, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 1.
Tamir further discloses wherein the second dose is higher than the first dose ([0045] – “two 3D T1-weighted images were obtained: pre-contrast and post-10% dose contrast (0.01 mmol/kg). For training and clinical validation, the remaining 90% of the standard contrast dose (full-dose equivalent, 100%-dose) was administrated and a third 3D 71-weighted image (100%-dose) was obtained”),
wherein the first dose is a dose which is equal to or less than a dose which is recommended by the manufacturer or distributor of the contrast agent and/or the first dose is equal to or less than a standard dose approved by an authority for an MR examination ([0045] – “two 3D T1-weighted images were obtained: pre-contrast and post-10% dose contrast (0.01 mmol/kg). For training and clinical validation, the remaining 90% of the standard contrast dose (full-dose equivalent, 100%-dose) was administrated and a third 3D 71-weighted image (100%-dose) was obtained”), and
wherein the second dose is the dose which is recommended by the manufacturer or distributor of the contrast agent and/or a dose which is mentioned in the product label of the contrast agent and/or the dose approved by an authority for an MR examination (also referred to as standard dose), or the second dose is a dose that is required to obtain a defined appearance of the first examination region ([0045] – “two 3D T1-weighted images were obtained: pre-contrast and post-10% dose contrast (0.01 mmol/kg). For training and clinical validation, the remaining 90% of the standard contrast dose (full-dose equivalent, 100%-dose) was administrated and a third 3D 71-weighted image (100%-dose) was obtained”).
Regarding claim 14, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 13.
Tamir further discloses wherein the magnetic resonance contrast agent comprises one or more of: gadolinium chelates, gadobenate dimeglumine, gadoteric acid, gadodiamide, gadoteridol, and gadobutrol (Fig. 10 shows data distribution from different sites using different scanners and different contrast agents including gadobenate dimeglumine and gadobutrol).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Tamir (WO 2021061710) and Weiss (US 11580626) as applied to claim 5 above, and further in view of Zhong (US20210302522).
Regarding claim 9, Tamir and Weiss disclose all the elements of the claimed invention as cited in claims 1 and 5.
As cited above Tamir discloses the second MR data from a second scanner with a second protocol.
Conversely Tamir does not teach wherein the first MR data comprise one or more of: an in-phase MR image, an opposed phase MR image, a water-only MR image, and a fat-only MR image, and/or
wherein the […] MR data comprise one or more of: an opposed phase MR image, a water-only MR image, and a fat-only MR image, and/or
wherein the pre-contrast MR data comprise a water-only MR image, and/or
wherein the first MR sequence protocol comprises one or more of: a T1-weighted DIXON-type sequence protocol and a T1-VIBE-DIXON-type sequence protocol, and/or
wherein the second MR sequence protocol comprises one or more of: a T1-weighted DIXON-type sequence protocol and a T1-VIBE-DIXON-type sequence protocol.
However Zhong discloses wherein the first MR data comprise one or more of: an in-phase MR image, an opposed phase MR image, a water-only MR image, and a fat-only MR image ([0009] – “a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including a plurality of water images, a plurality of fat images, and a plurality of effective R*2 maps from the plurality of multi-echo images”), and/or
wherein the […] MR data comprise one or more of: an opposed phase MR image, a water-only MR image, and a fat-only MR image (as cited above Tamir discloses the second MR data from a second scanner with a second protocol, [0009] – “a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including a plurality of water images, a plurality of fat images, and a plurality of effective R*2 maps from the plurality of multi-echo images”), and/or
wherein the pre-contrast MR data comprise a water-only MR image ([0009] – “A first subset of the multi-echo images is acquired prior to application of contrast to the anatomical area of interest…Next, data is generated including a plurality of water images, a plurality of fat images, and a plurality of effective R*2 maps from the plurality of multi-echo images”), and/or
wherein the first MR sequence protocol comprises one or more of: a T1-weighted DIXON-type sequence protocol and a T1-VIBE-DIXON-type sequence protocol ([0020] – “multi-echo Dixon techniques…These techniques are described herein with respect to the VIBE sequence…T1-weighted GRE image”), and/or
wherein the second MR sequence protocol comprises one or more of: a T1-weighted DIXON-type sequence protocol and a T1-VIBE-DIXON-type sequence protocol ([0020] – “multi-echo Dixon techniques…These techniques are described herein with respect to the VIBE sequence…T1-weighted GRE image”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamir to incorporate the Dixon sequence to obtain fat only and water only images of Zhong to achieve the same results. One would have motivation to combine because water only and fat only images would provide additional contrast enhancement.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Tamir (WO 2021061710) and Weiss (US 11580626) as applied to claim 13 above, and further in view of Erfindernennung liegt noch nicht vor (EP3644086A1) hereinafter Erfindernennung.
Regarding claim 15, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 13.
Conversely Tamir does not teach wherein the magnetic resonance contrast agent comprises a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance.
However Erfindernennung discloses wherein the magnetic resonance contrast agent comprises a substance or a substance mixture with gadoxetic acid or a gadoxetic acid salt as contrast-enhancing active substance ([0036] – “the contrast agent is a substance or a mixture of substances containing gadoxetic acid or a salt of gadoxetic acid as a contrast-enhancing agent. The most preferred substance is the disodium salt of gadoxetic acid”).
The disclosure of Erfindernennung is an analogous art considering it is in the field of MRI imaging of contrast enhanced tissue.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamir to incorporate the use of gadoxetic acid or a salt of gadoxetic acid as a contrast-enhancing agent of Erfindernennung to achieve the same results. One would have motivation to combine because “Gd-EOB-DTPA [disodium salt of gadoxetic acid] exhibits a significantly higher molar relaxivity in liver cells (hepatocytes) than, for example, in plasma” (Erfindernennung [0036]).
Regarding claim 16, Tamir and Weiss disclose all the elements of the claimed invention as cited in claim 13.
Conversely Tamir does not teach wherein the magnetic resonance contrast agent comprises the disodium salt of gadoxetic acid.
However Erfindernennung discloses wherein the magnetic resonance contrast agent comprises the disodium salt of gadoxetic acid ([0036] – “the contrast agent is a substance or a mixture of substances containing gadoxetic acid or a salt of gadoxetic acid as a contrast-enhancing agent. The most preferred substance is the disodium salt of gadoxetic acid”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Tamir to incorporate the use of the disodium salt of gadoxetic acid as a contrast-enhancing agent of Erfindernennung to achieve the same results. One would have motivation to combine because “Gd-EOB-DTPA [disodium salt of gadoxetic acid] exhibits a significantly higher molar relaxivity in liver cells (hepatocytes) than, for example, in plasma” (Erfindernennung [0036]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/R.C.L./Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797