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 Amendment
The amendment filed 07/01/2026 has been acknowledged.
Claims 1, 5, 10, 11, and 13 have been amended.
Response to Arguments
Applicant’s argument, see Remarks, filed 07/01/2026, with respect to §112(f) claim interpretation have been fully considered and are persuasive. The previous interpretation of these limitations under 35 U.S.C. 112(f) is therefore withdrawn. The limitations are given their broadest reasonable interpretation in light of the specification.
Applicant's arguments for 35 U.S.C §103 prior art rejections filed 07/01/2026 in regards to claims 1, 2, 13 and 14 have been fully considered but they are not persuasive.
Applicant argues A. The Combination of Wang in view of Andersson and Fatemi- Ardekani Fails to Disclose All the Features of Independent Claims 1, 13, and 14. Applicant alleges The combination of Wang in view of Andersson and Fatemi-Ardekani fails to disclose and/or reasonably suggest at least the following limitations of independent claim 1: (1) a breast micro-calcification image reconstruction module configured to output a breast micro-calcification magnetic resonance image; (2) inputting both gradient echo magnetic resonance imaging data and DIXON magnetic resonance imaging data into the
reconstruction module; (3) the gradient echo magnetic resonance imaging data being a phase image; (4) the DIXON magnetic resonance imaging data and the gradient echo magnetic resonance imaging data being spatially matched and descriptive of the same breast region; the reconstruction module being implemented as a trained breast micro-calcification image reconstruction neural network.
Applicant’s argument is not persuasive because it does not address the references according to the combined teachings for the rejection. Wang is relied upon for the MRI computational system environment and processing of MR signals. Andersson is relied upon for implementing MRI image processing using a trained CNN that operates with multi echo gradient echo MRI information as well as performing DIXON type water/fat processing. Fatemi-Ardekani is relied upon for the known use of phase sensitive gradient echo MRI information to identify and then localize breast calcifications. MPEP 7.37.13“In response to applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).” The cited references teach complimentary portions of the claimed MRI processing architecture. Andersson allows the modified system the known trained neural network MRI mechanism. Fatemi Ardekani supplies the breast specific diagnostic application as well as establishes the significance of gradient echo phase information for pointing out and localizing breast calcification. Accordingly the claimed features must be considered in view of the combined teachings rather than by requiring each reference independently disclose the complete claim limitation arrangement.
Applicant argues B. Andersson Does Not Remedy the Deficiencies of Wang. Applicant alleges Andersson is directed to water/fat signal separation in whole-body gradient echo scans using convolutional neural networks. Andersson trains neural networks to generate fat fraction maps from gradient echo data. The problem addressed by Andersson is water/fat separation, not breast micro- calcification imaging. This distinction is critical because the presently claimed neural network is not merely any neural network used in magnetic resonance imaging. Rather, the claims require a specifically trained breast micro-calcification image reconstruction neural network that receives (i) gradient echo magnetic resonance imaging phase data and (ii) spatially matched DIXON magnetic resonance imaging data and outputs a breast micro-calcification magnetic resonance image descriptive of locations of breast micro-calcifications. Andersson does not disclose such a network architecture. The neural networks disclosed in Andersson are trained to generate fat fraction maps. Andersson repeatedly explains that the desired network output is a fat fraction map and that training is performed using reference fat fraction maps. Thus, the target output used to train the Andersson network is fundamentally different from the output required by the claims. The claims are directed to locating breast micro-calcifications. Andersson is directed to estimating water and fat composition. These are distinct imaging targets derived from different physical characteristics and requiring different training objectives. Nothing in Andersson teaches or suggests training a neural network using breast micro-calcification ground truth to produce a breast micro- calcification image. The Office Action appears to rely on Andersson for the general proposition that neural networks may be used in magnetic resonance image processing. However, the claims do not merely recite use of a neural network in the abstract. Instead, the claims recite a particular neural-network reconstruction architecture having: (1) a particular combination of MRI inputs, namely gradient echo phase image data and spatially matched DIXON MRI data; and (2) a particular output, namely a breast micro-calcification magnetic resonance image. Andersson does not teach either feature. Accordingly, Andersson does not cure the deficiencies of Wang.
Applicants argument is not persuasive because the rejection does not reply upon Andersson as expressly disclosing a neural network already trained for breast calcification localization. Andersson establishes that trained CNN including a U-Net system can perform image to image MRI processing using multi echo gradient echo MRI information for Dixon water/fat evaluation and processing. Fatemi Ardekani is used in the modified system for the different but complimentary teaching that phase sensitive gradient echo MRI data is diagnostically useful for identifying and localizing breast calcification.
The fact that Andersson’s network uses a fat fraction training objective doesn’t automatically negate the teaching of using a trained neural network to process the relevant MRI information. This rejection proposes adapting the known neural network MRI processing technique taught by Andersson to the known breast calcification imagining objective taught by Fatemi-Ardekani. The Applicant’s distinction between Andersson’s particular fat fraction output and the claimed breast micro calcification output addresses Andersson individually rather than the modification presented. MPEP 7.37.13“In response to applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).”
Applicant argues C. Fatemi-Ardekani Does Not Remedy the Deficiencies of Wang and Andersson. Applicant acknowledges that Fatemi Ardekani teaches using susceptibility weighted imagining and corrected phase images to identify breast calcification. Applicant argues that Fatemi Ardekani does not teach spatially matched DIXON and GRE phase data being supplied to a trained neural network.
Applicant’s argument is not persuasive because Fatemi Ardekani is not relied upon for teaching the entire neural network input architecture. Fatemi Ardekani is relied upon for the breast specific diagnostic teaching missing from Andersson. Namely that corrected phase information obtained using gradient echo susceptibility sensitive MRI enables identification and localization of breast calcification.
When this is considered with Andersson’s teaching of trained neural network processing of multi echo gradient echo MRI data which is associated with Dixon water/fat processing, Fatemi Ardekani provides a reason for directing the MRI processing toward the phase sensitive breast calcification imagining goal. The rejection relies on the references complementary teachings rather than necessitating Fatemi Ardekani indecently disclosing Andersson’s neural network architecture. MPEP 7.37.13“In response to applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).”
Applicant argues D The Proposed Combination Requires Hindsight Reconstruction The rejection effectively extracts isolated concepts from three unrelated references: susceptibility reconstruction from Wang, neural-network generation of fat fraction maps from Andersson, and calcification visualization using corrected phase images from Fatemi-Ardekani. The Office Action then reconstructs Applicants' invention by selecting elements from the references and assigning them a purpose that none of the references teaches. This is impermissible hindsight reconstruction. (See MPEP §2145(X)(A)).
The cited references identify neither the claimed inputs, nor the claimed network objective, nor the claimed network output. The only apparent path from the cited references to the presently claimed architecture comes from knowledge of Applicants' disclosure. Such reconstruction is improper hindsight. (MPEP §2145(X)(A)). Even using impermissible hindsight, the resulting combination still does not disclose or suggest: (1) receiving gradient echo phase image data and DIXON magnetic resonance imaging data as separate spatially matched inputs; (2) inputting those inputs into a trained breast micro-calcification image reconstruction neural network; and (3) producing a breast micro-calcification magnetic resonance image as the output of that neural network.
Applicant’s arguments is not persuasive because the rationale for combination arises from the prior art itself rather than the applicant’s disclosure. Andersson independently establishes the use of trained CNN fort processing multi echo gradient exho MRI information in connection with Dixon water/fat processing. Fatemi-Ardekani independently establishes that phase sensitive gradient echo MRI information provides diagnostically useful contrast for identifying and localizing breast calcifications. Wang supplies the computer implemented MRI system framework for receiving and processing MRI information and generating diagnostic images. Fatemi Ardekani themselves provides the breast specific reason for directing phase sensitive gradient echo MRI processing to a calcification identification and localization. Meanwhile Andersson supplies the known trained neural network processing mechanism. The proposed combination follows from complementary teachings within the cited art and does not depend on the applicant’s disclosure for its rationale. Furthermore, in response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant argues E. The Proposed Modification Cannot Change the Principle Operation of Reference. That “The proposed combination set forth in the Office Action changes the principle of operation and purpose of the cited teachings. Andersson's neural network is designed for water/fat separation. Fatemi-Ardekani's method is based on susceptibility-weighted phase processing. Wang's method is based on susceptibility inversion using additional information. The claims do not merely substitute one known image processing tool for another. Rather, the claims define a specific reconstruction architecture that uses spatially matched DIXON and gradient echo phase data to generate a breast micro-calcification image. The Office Action has not identified any teaching in the cited art that would have led a person of ordinary skill to make that particular selection and integration.
Applicant’s argument is not persuasive because the proposed modification does not require changing the fundamental principle by which Andersson’s trained neural network operates. The neural network architecture continues to receive MRI image information. The neural network architecture still continues computationally process that information according to learned parameters and generate an image domain output. The modification concerns the MRI data utilized, the training objective, and the diagnostic output toward which the known neural network process is directed. The rejection does not propose converting chemical shift water/fat separation into susceptibility processing or treating the two as if they are the same. Andersson demonstrates the known use of a CNN for procession gradient echo MRI information while Fatemi Ardekani establishes the known diagnostic use of gradient echo phase information for breast calcification identification and subsequent localization. By applying the known neural network processing technique to the diagnostic objective taught by Fatemi Ardekani does not destroy the neural network pipeline of Andersson nor does it leave it inoperable. Applicant has not actually established that the proposed modification changes the principle of operation in the manner of In re Ratti, 270 F.2d 810, 813, 123 USPQ 349, 352 (CCPA 1959).
Applicant argues F The Cited Art Does Not Teach the Claimed Use of DIXON Data. Applicant claims that claim 1 requires GRE phase image data and DIXON MRI data as separate spatially matched datasets. That they describe the same breast region and that both datasets must be jointly supplied to the trained breast micro calcification neural network. Applicant alleges that Andersson merely performs water/fat separation from GRTE data and does not use DIXON MRI data as an input to a breast micro calcification reconstruction network.
Applicant’s argument is not persuasive because applicant’s characterization of the claimed data as a distinct “multimodal” MRI input structure imposes requirements not recited by claim 1. Claim 1 requires receiving GRE MRI data wherein the GRE MRI data is a phase image and receiving DIXON MRI data descriptive of the same breast region where the two are spatially matched. Claim 1 does not require that these data originate from separate MRI acquisitions, different imagining modalities or independently acquired datasets. Adnersson teaches processing multi echo gradient echo MRI data for DIXON type water/fat separation using a CNN. The acquired gradient echo information includes real and imaginary signal components. Therefore, it provides the complex MRI information from which phase and DIXON water/fat information are obtained. Since the DIXON information corresponds to the same multi echo gradient echo acquisition, the resulting DIXON data and corresponding gradient echo data describe corresponding spatial locations of the same imaged anatomy. The claimed “spatially matched” relationship does not require independently acquired images that are then registered.
Fatemi Ardekani further establishes why the phase component of gradient echo information would be utilized for the claimed breast specific objective. They teach that corrected phase information obtained from gradient echo susceptibility weighted imagining permits identification and localization of breast calcification. Andersson teaches the trained neural network processing framework and the relationship between gradient echo information and DIXON processing while Fatemi Ardekani supplies the reason to utilize the corresponding phase information for breast calcification localization.
A person of ordinary skill in the art would have found it obvious to use the spatially corresponding DIXON information and gradient echo phase information within the trained MRI processing architecture taught by Andersson and direct that processing toward the breast calcification identification and localization objective taught by Fatemi Ardekani. Applicant’s argument that Andersson does not itself disclose the completed breast micro calcification neural network again addresses Andersson individually rather than the combined teachings forming the basis of the rejection. MPEP 7.37.13“In response to applicant’s arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).” MPEP 2141 Examination Guidelines for Determining Obviousness Under 35 U.S.C. 103 [R-01.2024]
To that end the previous office actions 35 USC §103 prior art rejections of claims 1, 2, 13 and 14 will be upheld.
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.
Claim 13 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 13 recites “machine executable instructions stored on a non-transitory computer readable execution” It is unclear what constitutes a “non-transitory computer readable execution.” And how machine executable instructions are “stored on” an execution. Although a “non-transitory computer readable medium” is terminology having a recognized meaning in the computer arts, claim 13 does not recite a medium. Accordingly, the metes and bounds of the recited storage limitation cannot be determined with reasonable certainty.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 13 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does/do not fall within at least one of the four categories of patent eligible subject matter because Claim 13 recites “A computer program comprising machine executable instructions”. Although applicant amended claim 13 to recite “stored on anon-transitory computer readable execution”, the amended language does not recite a computer readable medium memory, storage device or other tangible structure. A computer program without the computer readable medium or other physical structure needed to realize the computer program’s functionality, constitutes non-statutory functional descriptive material. The recitation “non-transitory computer readable execution” does not clearly limit the claimed computer program to a statutory manufacture. Accordingly, the amendment does not overcome the rejection under 35 U.S.C. 101
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 ,2, 13 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (Wang hereinafter US 8781197 B2) in view of Andersson et al (Andersson hereinafter SEPARATION OF WATER AND FAT SIGNAL IN WHOLE‐BODY GRADIENT ECHO SCANS USING CONVOLUTIONAL NEURAL NETWORKS) in further view of Ardekani et al (Ardekani hereinafter IDENTIFICATION OF BREAST CALCIFICATION USING MAGNETIC RESONANCE IMAGING).
As per claim 1, Wang teaches a medical system comprising: a memory configured to store machine executable instructions (Figure 1) and a computational system configured for controlling the medical system that can receive the gradient echo MRI data (Column 5 line 10 “the computer executable code includes instructions for collecting magnetic resonance signals“ and Column 12 line 9 “The invention also includes the followings: (63) 1. Developing data acquisition sequence. A fast multiple gradient echo imaging sequence is developed for effective high-resolution mapping…”) as well as receive the DIXON MRI data (Column 5 line 10 “the computer executable code includes instructions for collecting magnetic resonance signals“ and column 47 line 13 “We propose extending IDEAL…The signal at a given voxel can be modeled as…(Equation 7-7)…w and f are the water and fat magnetization at the echo center…Maps of the three parameters water w, fat f and field map … can be estimated by fitting the signal model to the measured multiple echo data at each voxel…” The Dixon technique is a way to separate fat and water signals in MRI. IDEAL is a modern high-performance variant of the 3-point DIXON technique. Thus, Wang shows a system capable of receiving DIXON imaging data.) Wangs system also has the framework to generate calcification MR images using the received data (column 9 line 56 “information has been used to generate high contrast-to-noise ratio high resolution images at high field strength; positive phase is used to identify the presence of calcification. The use of phase information in field inversion as proposed in this research, will lead to accurate susceptibility quantification.” The system also shows the ability to hold the “ instructions for collecting magnetic resonance signals emitted by an object and for generating an image of the object from the magnetic resonance signals” column 5 line 10. )
Wang is not relied upon for the objective of breast microcalcification localization, the reconstruction module , the output of breast micro calcification localization MRI, the phase image nature of the GRE data, nor the reconstruction module being a neural network. Although Wang speaks on using this system to track calcification and its genetic roots in breast tissue (as well as other types mineralization in other organs) through MRI (Column 49 line 34-67) Wang is used mainly for the medical system’s general architecture and capability.
Andersson teaches a image reconstruction module (Abstract “water-fat signal separation of whole-body gradient echo scans using convolutional neural networks” Andersson’s CNN is a computational reconstruction module that processes MRI data to reconstruct and output MRI images. This satisfies the “image reconstruction module” ), inputting gradient echo MRI data and DIXON MRI data (“A 3D spoiled gradient echo sequence was used. A total of 5 bipolar echoes were collected. The following parameters were used: voxel size = 2.07 × 2.07 × 8 mm3- and “As input to the networks 1 2D axial slice with 2 channels for each echo, 1 for the real and 1 for the imaginary component, was used” To clarify Andersson processes gradient echo MRI data to perform Dixon type water fat separation. This means the input dataset inherently includes Dixon MRI signal components (water, fat, in-phase, opposed-phased) derived from the GRE acquisition) , wherein the gradient echo MRI data is a phase image (Andersson’s is doing chemical -shift based separation on gradient echo scans. Chemical shift (Dixon) separation relies on phase differences across gradient echo signals. The GRE data implicitly includes phase information Andersson further states in the Neural Network section that “The networks were trained using different configurations of the available echoes as input. Networks were either trained with echoes of both polarities or only echoes of 1 polarity (i.e., only odd or only even numbered echoes). For the 3 different sets of echoes used, all possible configurations using the first available echo and different numbers of consecutive echoes were used.” sTating “all possible configurations” are used includes phase image), wherein the DIXON MRI data and the gradient echo MRI data are spatially matched (Andersson is doing a “whole‐body gradient echo scans” Dixon signal components are acquired from the same GRE acquisition and are implicitly spatially matched.), wherein the module is implemented as a trained neural network (Anderson is using a U-net as seen in figure 1)
Ardekani teaches using SWI which is described in Optimizing imagining parameters section as “a 3D fast gradient echo sequence” to identify and localize breast calcifications. Ardekani explains that “Corrected phase and magnitude images acquired using SWI allowed identification and correlation of all calcifications” and that “As the approach is a 3D technique, it could potentially allow for more accurate localization and biopsy” For the claim limitation that the output image is “a breast micro-calcification magnetic resonance image” Ardekani teaches MRI output images tailored to breast calcification depictions because it discloses “Corrected phase and magnitude images acquired using SWI” that are used for calcification identification. For the limitation that the output image is “descriptive of a location of breast micro-calcifications” “Ardekani teaches both identification and localization of calcifications. Ardekani states that the corrected SWI images “identification and correlation of all calcifications” and that “3D technique, it could 1potentially allow for more accurate localization”. Ardekani supplies the clinical imagining objective. It should also be noted that the SWI images are generally phase images to help differentiate materials like iron or calcium.
It should be noted for clarification that the fact that Andersson and Wang do not expressly mention breast calcification does not circumvent the purpose of an obviousness analysis. This is because they are not being used for the clinical target; they are being used for the technical mechanism and system environment that a person of ordinary skill in the art would have applied to that target being breast calcification image. Andersson supplies the core reconstruction technology i.e. a neural network operating on GRE echo/DIXON data, with GRE phase and DIXON data used as an input and MRI image that is the output offered by the neural network. Wang supplies the surrounding MRI system framework and the understanding that MR signals are collected, processed and used to generate diagnostic images and detect features such as a calcified deposit. This keeps the combination in the same MRI detection and susceptibility imagining realm. Ardekani provides the missing specific problem endpoint: in breast imaging, susceptibility weighted imaging ( high resolution 3D MRI that uses magnitude and phase image information that is sensitive to calcification) permits identification of calcifications in the breast and a more accurate localization. In essence Andersson shows how to implement a neural MRI reconstruction on the relevant data through his neural network architecture, Wang teaches the MRI system context in which signals are collected and images are generated which include calcification related diagnostics and Ardekani teaches the reasoning a person of ordinary skill in the art would direct this pipeline towards breast microcalcification localization MRI output.
Accordingly, a person of ordinary skill in the art at the time this invention was effectively filed would have found it obvious to implement Andersson’s CNN MRI processing within the MRI system framework of Wang and to direct the resulting MRI output toward the breast calcification identification and localization objective taught by Ardekani. This is because the references address complementary parts of the same phase sensitive MRI flow. Andersson teaches using CNN networks for water-fat signal separation of whole-body gradient echoes cans i.e. a trained model that processes gradient echo, Dixon MRI data to generate MRI domain outputs. Wang enables the deployable MRI system context and Ardekani teaches why a person of ordinary skill in the art would target that processing to breast calcifications namely that corrected phase and magnitude images acquired by GRE derived SWI allows for identification and correlation to calcifications. Essentially, they work together as follows: Andersson supplies the image reconstruction/model mechanism, Wang supplies the computer implemented MRI system architecture that receives MR data and generates images and Ardekani supplies the breast specific diagnostic target for the output image. Ardekani teaches that phase sensitive MRI processing such as SWI corrected phase and magnitude imagining enhances the visibility of breast micro calcifications and enables their identification and localization because calcifications produce susceptibility induced phase effects in MRI data. Both Andersson and Ardekani rely on extracting diagnostically relevant information from phase sensitive GRE MRI signals and the modification simply directs a known reconstructions technique toward a known diagnostic objective using the same underlying data. A person of ordinary skill in the art would have recognized that the same phase sensitive information used in Andersson’s U-net is the same class of information used in Fatemi to visualize calcifications, namely, signal variations arising from magnetic susceptibility and chemical shift effects in gradient echo MRI data. The advantage of this combination is a system that uses phase sensitive GRE/Dixon MRI data in a trained computational pipeline to produce an MRI image tailored for breast micro calcification with improved suppression of confounding tissue components and improved utility for diagnosis. This allows for breast microcalcification diagnosis imagining to be MRI based rather than CT based which eliminates the patients need for X-ray exposure of a traditional Mammogram.
As per claim 2
Wang, Jafari and Ardekani cover claim 1’s limitations in claim 1’s 103 rejection. Please see claim 1’s 103 rejection.
Ardekani calculates a filtered breast micro-calcification image (132) by applying a high-pass spatial filter (130) to the breast micro-calcification magnetic resonance image. (SWI image processing section: A high-pass filter to remove low-spatial frequency components, caused by background field effects, was applied.29 Following filtering corrected phase images were calculated)
Wang show additional support for the capabilities of the system. Column 33 line 9 “ A single image was acquired, and phase unwrapping and high pass filtering were performed. The…filtering algorithms were those described in…The imaged resolution was 93.75 .mu.m isotropic, and the image was acquired using an echo time of 10 ms.”, column 37 line 52 “A method is used to remove motion artifacts in MRI by forcing data consistency…a novel POCS algorithm is developed as an automatic iterative method to remove motion artifacts using high pass phase filtering and convex projections in both k-space and image space.” Column 75 line 14 “ High Pass Filtering: When images from a reference phantom are not available, a high pass filter can remove background field inhomogeneity. A simple high pass filter, described by…was taken to be zero outside of the imaged volume.”)
Accordingly, s person of ordinary skill in the art at the time this invention was effectively filed would have been motivated to apply a high pass filter to the breast micro calcification MRI image because both Ardekani and Wang within the Wang/Jafari/Ardekani system both teach high pass filtering removes unwanted low spatial frequency and background field effects from MRI phase data. This improves the diagnostically useful image contrast of the MRI image. Ardekani applies the high pass filter then corrected phase images are calculated in the breast calcification SWI workflow. Wang similarly teaches high pass filtration on MRI image data. A person of ordinary skill in the art would have found it obvious after generating the breast calcification MRI image to further calculate a filtered image by applying a high pass filter. Doing so predictably suppresses background effects and enhance local phase contrast associated with the targeted calcifications thereby improving calcification identification and localization in the breast MRI image.
As per Claim 13
Claim 13 is the machine executable instructions embodied in a non transitory computer readablethat dictate claim 1 and will be rejected under the same premise.
As per claim 14
Claim 14 is the parallel method claim of claim 1 and will be rejected under the same premise.
Allowable Subject Matter
Claim 3-12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/SHANE WRENSFORD CODRINGTON/Examiner, Art Unit 2667 /MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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