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 .
Claim Objections
Claim(s) 1, 6, 18 and 20 are objected to because of the following informalities:
Claim 1 recites a phrase “timeframes of the k-space data” in line 7. Examiner suggests amending the phrase to recite “the timeframes of the k-space data” to restore clarity.
Claim 6 recites a phrase “wherein the image is reconstructed from the outer volume removed k-space data using the computer system comprises …” in the first line. Examiner suggests amending the phrase to recite “wherein reconstructing the image from the outer volume removed k-space data using the computer system comprises …” to restore clarity.
Claim 18 recites a phrase “the outer volume removed data” in the last line. Examiner suggests amending the phrase to recite “the outer volume removed calibration data” to restore clarity.
Claim 20 recites a phrase “a pulse sequence” in line 7. Examiner suggests amending the phrase to recite “the pulse sequence” to restore clarity.
Appropriate correction is required.
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 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) 1-2, 6-7, 10, 13 and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Goel et al. (US 20250314728; hereinafter Goel).
Regarding claim 1, Goel discloses in figure(s) 1-13 a method for reconstructing an image of a subject from k-space data acquired with a magnetic resonance imaging (MRI) system (MRI system 100; fig. 1), the method comprising:
accessing k-space data (214; fig. 2) with a computer system (processor 204), wherein the k-space data were acquired from a subject using an MRI system (step 238; fig. 3; 250; fig. 4), wherein the k-space data comprise timeframes of k-space data acquired using time-interleaved undersampling patterns in k-space (para. 29 - there is enough system dead time in each shot, the extra line is easily accommodated without increasing scan time);
generating composite image data from the k-space data using the computer system to combine timeframes of the k-space data (para. 44 - Control and analysis circuit 168 is coupled to a memory circuit 170 to store programming code for operation of the MRI system 100 and to store the processed image data for later reconstruction; fig. 1; steps 240, 242; fig. 3);
accessing a machine learning model (reconstruction model 261; fig. 4) with the computer system, wherein the machine learning model has been trained (training module 212; fig. 2; para. 51 - Training module 212 may include training datasets for the one or more ML models of motion detection module 208 and/or motion correction module 210) on training data to extract ghosting artifact signal (para. 5 - ghosting or blurring-like artifacts) components from a magnetic resonance image (para. 7 - utilizing, via the processor, a deep learning-based reconstruction model on motion-corrupted k-space data);
generating a ghosting artifact image by inputting the composite image data to the machine learning model using the computer system (para. 50 - motion-corrupted image is inputted into the deep learning-based reconstruction model), generating the ghosting artifact image as an output (para. 7 - modify the motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image), wherein the ghosting artifact image depicts ghosting artifacts (para. 5 - ghosting or blurring-like artifacts) extracted from the composite image data (step 244; fig. 3; para. 48 - motion detection module 208 is configured to utilize a trained machine learning algorithm (e.g., support vector machine) to detect navigator like echoes with different random Gaussian noises belonging to the same or different poses);
estimating outer volume signals (para. 29 - All kinds of motion will appear as outliers in the shots) using the computer system to subtract the ghosting artifact image from the composite image data (para. 48 - the clustering of the additional navigator like echoes provides position information to remove outlier position shots);
generating outer volume removed k-space data using the computer system to remove the outer volume signals from the k-space data (para. 49 - Motion correction module 210 is configured to generate dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data including only shots of the plurality of shots not affected by movement of the subject during acquisition); and
reconstructing an image from the outer volume removed k-space data using the computer system (para. 50 - reconstruction model is utilized on the motion-corrupted k-space data to modify the motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed (e.g., motion artifact free) MR image; step 246; fig. 3).
Regarding claim 2, Goel discloses in figure(s) 1-13 the method of claim 1, wherein the k-space data were acquired from a spatial region in the subject, wherein the spatial region contains a smaller region-of-interest containing an anatomical target (para. 37 - a patient 126 e.g., subject to be positioned therein for imaging selected anatomy within the patient).
Regarding claim 6, Goel discloses in figure(s) 1-13 the method of claim 1, wherein the image is reconstructed from the outer volume removed k-space data using a physics-driven deep learning (PD-DL) model (para. 29 - motion correction unit perform reconstruction in a physics driven artificial intelligence framework) by: accessing the PD-DL model using the computer system; and inputting the outer volume removed k-space data to the PD-DL model, generating the reconstructed image as an output (para. 50 - motion correction module 210 includes a reconstruction model, which may be a ML/DL model …inputting both the motion-corrupted image and the contrast image into the deep learning-based reconstruction model, wherein the dominant pose k-space data is utilized for application of soft data consistency in an unrolled framework. The actions even further include outputting from the deep learning-based reconstruction model the reconstructed image based on both the motion-corrupted image and the contrast image).
Regarding claim 7, Goel discloses in figure(s) 1-13 the method of claim 6, wherein reconstructing the image using the PD-DL model further comprises accessing masked coil sensitivity maps with the computer system and inputting the masked coil sensitivity maps to the PD-DL model as an additional input (para. 33 - a k-space mask from the k-space data utilizing only the shots not affected by movement of the subject during acquisition; para. 49 - motion correction module 210 is configured to apply the k-space mask to the k-space data to generate the dominant pose k-space data).
Regarding claim 10, Goel discloses in figure(s) 1-13 the method of claim 1, wherein the k-space data comprise timeframes of uniformly undersampled k-space (para. 3 - human tissue is subjected to a uniform magnetic field (polarizing field B.sub.0); para. 63 - obtaining k-space data of a subject acquired with a magnetic resonance imaging MRI scanner utilizing a multi-shot pulse sequence e.g., fast spin echo sequence).
Regarding claim 13, Goel discloses in figure(s) 1-13 the method of claim 1, wherein reconstructing the image comprises using an image reconstruction that receives coil sensitivity maps as an additional input (para. 59 - additional navigator like echoes comprises clustering the respective additional navigator like echoes (e.g., for a given slice for a given coil for a given NEX).
Regarding claim 18, Goel discloses in figure(s) 1-13 a method for generating outer volume removed calibration data for use in magnetic resonance imaging (MRI) (MRI system 100; fig. 1), the method comprising:
accessing k-space data (214; fig. 2) with a computer system (processor 204), wherein the k-space data were acquired from a subject using an MRI system (step 238; fig. 3; 250; fig. 4), wherein the k-space data comprise timeframes of k-space data acquired using time-interleaved undersampling patterns in k-space (para. 29 - there is enough system dead time in each shot, the extra line is easily accommodated without increasing scan time);
generating composite image data from the k-space data using the computer system to combine timeframes of the k-space data (para. 44 - Control and analysis circuit 168 is coupled to a memory circuit 170 to store programming code for operation of the MRI system 100 and to store the processed image data for later reconstruction; fig. 1; steps 240, 242; fig. 3);
estimating outer volume signals (para. 29 - All kinds of motion will appear as outliers in the shots) from the composite image data (para. 48 - the clustering of the additional navigator like echoes provides position information to remove outlier position shots);
generating outer volume removed calibration data (para. 29 - additional navigator like echoes which serve as guiding lines are used to detect the shots which are in a consistent e.g., dominant pose) using the computer system to remove the outer volume signals from the composite image data (para. 29 - Outlier shots i.e., of non-dominant poses of the subject where the subject moved during data acquisition or the scan are dropped and reconstructed); and
storing the outer volume removed data with the computer system (store reconstructed image step 266; fig. 4).
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 of this title, 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.
Claim(s) 3-5, 8-9, 14-17 and 19-23 are rejected under 35 U.S.C. 103 as being unpatentable over Goel in view of Li et al. (US 20180140216).
Regarding claim 3, Goel teaches in figure(s) 1-13 the method of claim 2,
Goel does not teach explicitly wherein the anatomical target is a heart.
However, Li teaches in figure(s) 1-7 wherein the anatomical target is a heart (para. 37 - ROI includes an entire heart of a subject).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having wherein the anatomical target is a heart as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 4, Goel in view of Li teaches the method of claim 3,
Li additionally teaches in figure(s) 1-7 wherein the k-space data comprise timeframes of k-space data acquired during times in a cardiac cycle when the heart is moving (para. 10 - deformations due to cardiac and respiratory motion are incorporated into an L1 regularized iterative reconstruction framework; para. 20 - continuously acquired 3D projection reconstruction k-space data, first the dataset is segmented into multiple cardiac/respiratory phases ... a 16-phase cardiac cycle is reconstructed.).
Regarding claim 5, Goel in view of Li teaches the method of claim 3,
Li additionally teaches in figure(s) 1-7 wherein the k-space data comprise timeframes of k-space data acquired during times in a cardiac cycle when the heart is not moving (para. 20 - quiescent phases are identified and combined with or without motion correction; para. 18 - 4D coronary MRA acquisition: the coronary artery stayed relatively still during phases 1, 2, and 16).
Regarding claim 8, Goel teaches the method of claim 7,
Goel does not teach explicitly wherein accessing the masked coil sensitivity maps with the computer system comprises: accessing coil sensitivity maps with the computer system; accessing an outer volume mask data with the computer system; and generating the masked coil sensitivity maps by applying the outer volume mask data to the coil sensitivity maps.
However, Li teaches in figure(s) 1-7 wherein accessing the masked coil sensitivity maps with the computer system comprises: accessing coil sensitivity maps with the computer system (para. 10 - a sensitivity encoding operation is performed by using a gridding/re-gridding approach); accessing an outer volume mask data with the computer system (para. 32 - a heart mask is automatically generated using a multiatlas method and serves as the region of interest (ROI) of the registration algorithm); and generating the masked coil sensitivity maps by applying the outer volume mask data to the coil sensitivity maps (para. 30 - excitation pulses are slab-selective for suppressing outer volume signal).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having wherein accessing the masked coil sensitivity maps with the computer system comprises: accessing coil sensitivity maps with the computer system; accessing an outer volume mask data with the computer system; and generating the masked coil sensitivity maps by applying the outer volume mask data to the coil sensitivity maps as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 9, Goel in view of Li teaches the method of claim 8,
Goel additionally teaches in figure(s) 1-13 wherein the outer volume mask data are generated from the estimated outer volume signals (clm. 4 - excitation pulses are slab-selective for suppressing outer volume signal; para. 32 - all selected frames are also cropped to the bounding box of the heart mask).
Regarding claim 14, Goel teaches in figure(s) 1-13 the method of claim 13,
Goel does not teach explicitly wherein the coil sensitivity maps are generated from outer volume removed calibration data in which outer volume regions have been removed.
However, Li teaches in figure(s) 1-7 wherein the coil sensitivity maps are generated from outer volume removed calibration data in which outer volume regions have been removed (para. 58 - self-calibrated sensitivity map … which combined all included k-space data to yield an image in the reference cardiac phase).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having wherein the coil sensitivity maps are generated from outer volume removed calibration data in which outer volume regions have been removed as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 15, Goel in view of Li teaches the method of claim 14,
Li additionally teaches in figure(s) 1-7 wherein the outer volume removed calibration data are generated by: generating an outer volume mask from the estimated outer volume signals; and applying the outer volume mask to the calibration data (para. 32 - all selected frames are also cropped to the bounding box of the heart mask; para. 6 - excitation pulses can be slab-selective for suppressing outer volume signal).
Regarding claim 16, Goel in view of Li teaches the method of claim 14,
Goel additionally teaches in figure(s) 1-13 wherein the calibration data comprise the composite image data (para. 16 - utilizing motion-corrupted image without contrast image).
Regarding claim 17, Goel teaches in figure(s) 1-13 the method of claim 1,
Goel does not teach explicitly wherein reconstructing the image comprises reconstructing a time-series of images.
However, Li teaches in figure(s) 1-7 wherein reconstructing the image comprises reconstructing a time-series of images (para. 31 - During reconstruction, the cardiac and respiratory motion signals are first extracted automatically from the multichannel SG projection time series using principal component analysis and prior knowledge of cardiac and respiratory frequencies).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having wherein reconstructing the image comprises reconstructing a time-series of images as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 19, Goel teaches the method of claim 18,
Goel does not teach explicitly further comprising generating coil sensitivity maps from the outer volume removed calibration data.
However, Li teaches in figure(s) 1-7 further comprising generating coil sensitivity maps (para. 58 - self-calibrated sensitivity map) from the outer volume removed calibration data (para. 30 - slab-selective for suppressing outer volume signal).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having further comprising generating coil sensitivity maps from the outer volume removed calibration data as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 20, Goel teaches in figure(s) 1-13 a method for reconstructing an image of a subject from k-space data acquired with a magnetic resonance imaging (MRI) system (MRI system 100; fig. 1), the method comprising:
accessing k-space data (214; fig. 2) with a computer system (processor 204), wherein the k-space data were acquired from a subject with an MRI system using a pulse sequence (para. 29 - a pulse sequence diagram e.g., of a multi-shot sequence e.g., fast spin echo sequence or a single shot sequence with multiple number of excitations NEX is modified to acquire an additional navigator like echo e.g., a near-center within 10 percent phase encode line), wherein the k- space data comprise at least one timeframe of k-space data that samples a central region of k-space and at least one timeframe of k-space data that samples peripheral regions of k-space during dead time (para. 29 - there is enough system dead time in each shot, the extra line is easily accommodated without increasing scan time) of a pulse sequence;
reconstructing a first image from the k-space data using a deep learning image reconstruction (para. 7 - a deep learning-based reconstruction model) that is trained on training data (training of a reconstruction model; fig. 7) to remove image artifacts (para. 5 - ghosting or blurring-like artifacts to manifest in the reconstructed image);
generating composite image data (para. 7 - motion-corrupted k-space data) from the k-space data using the computer system to combine timeframes of the k-space data (para. 44 - Control and analysis circuit 168 is coupled to a memory circuit 170 to store programming code for operation of the MRI system 100 and to store the processed image data for later reconstruction; fig. 1; steps 240, 242; fig. 3);
estimating outer volume signals signals (para. 29 - All kinds of motion will appear as outliers in the shots) using the computer system to subtract the first image from the composite image data (para. 48 - clustering of the additional navigator like echoes provides position information to remove outlier position shots);
generating outer volume removed k-space data using the computer system to remove the outer volume signals from the k-space data (para. 49 - Motion correction module 210 is configured to generate dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data including only shots of the plurality of shots not affected by movement of the subject during acquisition); and
reconstructing a second image from the outer volume removed k-space data using the computer system (para. 50 - reconstruction model is utilized on the motion-corrupted k-space data to modify the motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed (e.g., motion artifact free) MR image; step 246; fig. 3).
Goel does not teach explicitly k-space data were acquired from a heart of a subject; pulse sequence during which the heart is moving.
However, Li teaches in figure(s) 1-7 k-space data were acquired from a heart of a subject (para. 49 - the object may be a liver, heart, womb, brain, breast, abdomen, or blood vessel.); pulse sequence during which the heart is moving (para. 77 - observing a movement or position of the object 10, a respiration measurer for measuring the respiration of the object 10, an ECG measurer for measuring ECG of the object 10).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having k-space data were acquired from a heart of a subject; pulse sequence during which the heart is moving as taught by Li in order to provide "Addressing these issues … residual cardiac respiratory motion artifacts remain the major challenge for coronary MRA, and lead to considerable diagnostic inaccuracy and scan failures… improved systems and methods for coronary MRA" (paras. 3-4).
Regarding claim 21, Goel in view of Li teaches the method of claim 20,
Goel additionally teaches in figure(s) 1-13 wherein the pulse sequence is an ECG-triggered pulse sequence that acquires auxiliary k-space data during an auxiliary window occurring after a primary imaging window during which the k-space data are acquired (para. 48 - a magnetic resonance imaging (MRI) scanner utilizing a single-shot or multi-shot pulse sequence … a plurality of navigator like echoes of the k-space data (representing the plurality of echoes received in a same repetition after an excitation pulse for a respective shot) including an additional navigator like echo are acquired for each shot or a group of shots of a plurality of shots for a respective slice).
Regarding claim 22, Goel in view of Li teaches the method of claim 21,
Goel additionally teaches in figure(s) 1-13 wherein the auxiliary k-space data sample peripheral k- space lines that are interleaved with the k-space data (para. 50 - transforming centrally located phase encoded lines e.g., obtained from both motion-corrupted and uncorrupted shots from the k-space data into a contrast image).
Regarding claim 23, Goel in view of Li teaches the method of claim 21,
Li additionally teaches in figure(s) 1-7 wherein the auxiliary data are further used to estimate the outer volume signals (para. 30 - water-selective excitation pulses are used to suppress fat signal, the excitation pulses are slab-selective for suppressing outer volume signal).
Claim(s) 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Goel in view of GULAKA et al. (US 20150168522).
Regarding claim 11, Goel teaches in figure(s) 1-13 the method of claim 1,
Goel does not teach explicitly wherein the k-space data comprise a higher density of k- space sampling in a first region of k-space and a lower density of k-space sampling in a second region of k-space.
However, GULAKA teaches in figure(s) 1-13 wherein the k-space data comprise a higher density of k- space sampling in a first region of k-space and a lower density of k-space sampling in a second region of k-space (para. 106 - a density of acquired line data of the second region may be smaller than a density of acquired line data of the first region.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Goel by having wherein the k-space data comprise a higher density of k- space sampling in a first region of k-space and a lower density of k-space sampling in a second region of k-space as taught by GULAKA in order to provide "MRI systems may acquire images having high soft tissue contrast, neurological images, intravascular images, musculoskeletal images, and oncologic images that are important in detecting abnormal tissues." (para. 6).
Regarding claim 12, Goel in view of GULAKA teaches the method of claim 11,
Goel additionally teaches in figure(s) 1-13 wherein the ghosting artifacts comprise noise-like ghosting (para. 48 - echoes with different random Gaussian noises belonging to the same or different poses) caused by differences between the k-space data acquired from the first region of k- space and the k-space data acquired from the second region of k-space (para. 50 - motion-corrupted k-space data to modify the motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gao et al. (US 20220392035) discloses "nonuniformity correction systems and methods of diffusion-weighted magnetic resonance images".
Edelman et. al. (US 20100134103) discloses "System And Method For Ghost Magnetic Resonance Imaging".
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AKM ZAKARIA whose telephone number is (571)270-0664. The examiner can normally be reached on 8-5 PM (PST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Judy Nguyen can be reached on (571) 272-2258. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AKM ZAKARIA/
Primary Examiner, Art Unit 2858
Prosecution Insights