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 amendments, filed 01/14/2026, with respect to prior 35 USC 101 rejections of claim 17 have been fully considered and are persuasive. The 35 USC 101 rejection of claim 17 has been withdrawn
Applicant's arguments filed 01/14/2026 have been fully considered but they are not persuasive.
Applicant argues that Gui fails to teach determining an updated RF parameter based on spatial analysis is not persuasive. Gui teaches obtaining spatially resolved B1 amplitude measurements in the form of B1 field map acquired using known MRI techniques such as the Bloch-Siegart method [0115]. Gui further teaches adjusting an RF transmit parameter based on B1 map, specifically adjusting the transmit gain of the RF driver based on the B1 filed map [0099], and acquiring diagnostic MR images using the updated transmit gain [0100]. Accordingly, Gui teaches determining an updated RF parameter based on spatially resolved B1 measurements and using that updated parameter in subsequent MRI sequences.
Applicant further argues that the absence of explicit analysis in Gui precludes determining an RF parameter based on such analysis is also not persuasive, Gui already teaches determining the RF calibration parameter from spatially resolved B1 measurements [0099-100]. The spatial subset analysis recited in the claim merely refines the spatial data used in that determination. Yankeelov teaches performing spatial analysis on subset of MRI data, including identifying regions of interest and applying localized statistical processing to voxel based measurements [0028, 0121].
Applicant further argues that Yankeelov does not determine an updated RF parameter is not persuasive because Yankeelov is not relied upon for teaching RF parameter determination. Gui already teaches determining and applying the RF calibration parameter based on B1 measurements [0099-0100]. Yankeelov is relied upon only for teaching spatial subset analysis techniques applicable to MRI measurement data.
Further argument that the proposed combination changes the principle of operation of the references is also not persuasive. The rejection relies on Yankeelov only for its teaching of spatial statistical analysis of MRI data. Applying such known statistical processing techniques to the spatial B1 measurements used in Gui’s calibration framework would not alter the calibration method disclosed in Gui but instead represents the predictable application of known analysis techniques to improve RF calibration accuracy.
Claim Rejections - 35 USC § 103
4. 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.
Claims 1-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gui (U.S. Publication 20210018583) in view of Yankeelov (WO2023049207).
Regarding claim 1, Gui teaches an image processing method comprising: obtaining spatially-resolved B 1 amplitude measurements obtained using at least one RF parameter (“the MRI system acquires a B.sub.1.sup.+ field map, of the anatomical region of operation 602, the B.sub.1.sup.+ field map is acquired by using the Bloch-Siegert shift method” [0115] such B1 map B1+ maps are spatially resolved amplitude measurements dependent on RF parameter such as transmit gain, flip angle [0099]);
determining, based on the performed spatially-resolved analysis, an updated value of the at least one RF parameter to be used in a series of MRI sequences obtained after determining the updated value
storing the updated value of the at least one RF parameter (“the MRI system adjusts a transmit gain of the RF parameter for use in the series of MRI sequences obtained after the determining the updated value (MRI system stores data associated with image processing and calibration parameters within the image processing system [0105], storing calibration parameters such as transmit gain enables subsequent MRI acquisition using determined RF parameter, therefore teaching obtaining spatially resolved B1 measurements, analyzing those measurements to determine an RF calibration parameter, storing that parameter, and applying the parameter in subsequent MRI sequence.
Gui teaches obtaining spatially resolved B1 field maps and using them to adjust RF transmit gain [0098-99] however, Gui does not explicitly teach performing analysis on a subset of spatially resolved B1 values performing at least one spatially-resolved analysis on a spatially-resolved subset of the obtained spatially-resolved B 1 amplitude measurements
However, Yankeelov teaching quantitative magnetic resonance imaging (MRI) teaches performing at least one spatially-resolved analysis on a spatially-resolved subset of the obtained spatially-resolved B 1 amplitude measurements (“the protocol involves acquisition of five MRI data types at each scan session: 1) DWMRI, 2) 81 field map to correct for radiofrequency inhomogeneity, 3) variable flip angle T1-weighted data for generating a pre-contrast T1 map, 4) dynamic, high-temporal resolution, T1-weighted data before, during” [0062, 0103] “Generate enhanced anatomical images. Enhance contrast by applying a local-statistics-based transfer function to each voxel of a subtraction anatomical image (average pre contrast images minus average post contrast images). Specifically, use the transfer function” [0121]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the spatial subset statistical analysis techniques of Yankeelov into the B1 map calibration framework of Gui as a routine data analysis technique to improve robustness and accuracy of RF calibration derived from spatial measurement.
Regarding claim 2, Gui as modified further teaches performing the series of MRI sequences with the updated value of the at least one RF parameter (“the MRI system acquires a diagnostic MR image of the anatomical region using the transmit gain obtained at 408” [0100]).
Regarding claim 3, Gui as modified further teaches wherein the at least one RF parameter is an RF transmitter gain level (“the MRI system adjusts a transmit gain of the RF driver (e.g., RF drive unit 22 in FIG. 1) based on the predicted B.sub.1.sup.+ field map”[0099]).
Regarding claim 4, Gui does not explicitly teach selecting the spatially-resolved subset using a histogram.
However, Yankeelov teaching quantitative magnetic resonance imaging (MRI) teaches selecting the spatially-resolved subset using a histogram (“The data processing step includes deriving values from each of the MRI data sets to identify the region of interest” [0028], “Generate enhanced anatomical images. Enhance contrast by applying a local-statistics-based transfer function to each voxel of a subtraction anatomical image” [0121] a histogram is a standard statistical tool to visualize and select ranges of values within a distribution, selecting subsets based on percentile threshold which is mathematically equivalent to identify bins in a histogram and choosing values within a bin).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the teaching statistical subset method of Yankeelov in Gui’s framework to predictively improve robustness and accuracy of RF calibration.
Regarding claim 5, Gui as modified further teaches wherein performing at least one spatially- resolved analysis on the spatially-resolved subset of the obtained spatially-resolved B 1 amplitude measurements comprises selecting the spatially-resolved subset using a region of interest (“generate, apply mask to suppress background corresponds to ROI” [0099, 0119]).
Regarding claim 6, Gui teaches obtaining spatially resolved B1 amplitude measurement [0115], performing analysis on such maps, and adjusting transmit gain based on B1 values [0099], However Gui does not explicitly teach selecting a lower percentage of the B1 amplitude or averaging that subset to determine the adjustment factor based on a ratio.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Gui by selecting a lowest percentage subset of B1 amplitude values for calibration as Gui acknowledges that measured B1 maps contain noise, discontinuities and variations [0019]. Using a lowest portion of values us a routine statistical technique to mitigate outliers, reduce bias from extreme high values, and ensure conservative calibration that maintains sufficiently excitation in under flipped regions. Also computing a ratio of a target value to the average of the lowest percentage is a predictable mathematical variation of Gui’s disclosed adjustment of transmit gain based on B1 maps, which represents no more than routine optimization.
Regarding claim 7, Gui teaches obtaining spatially resolved B1 amplitude measurement [0115], performing analysis on such maps, and adjusting transmit gain based on B1 values [0099], However Gui does not explicitly teach selecting both a low and high percentage of B1 amplitude values and averaging those subsets to calculate an updated RF parameter.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify Gui by incorporating both low and high value subsets of the B1 measurements, as Gui in [0024] acknowledges that transmitting B1 inhomogeneity presents in the form of insufficient and excessive excitation that produce shading artifacts “result in intensity inhomogeneities (also referred to as shading)”, analyzing both extremes of the measure B1 distributions provides a more balanced calibration that addresses under and over excited regions. Averaging values from both subsets is a predictable statistical technique to capture the range of inhomogeneity and constitutes a predictable variation of Gui’s disclosed gain adjustment technique.
Regarding claim 8, Gui as modified further teaches wherein obtaining the spatially-resolved B1 amplitude measurements comprises using a Bloch-Siegert shift sequence to obtain the spatially- resolved B 1 amplitude measurements (“the ground truth B.sub.1.sup.+ field map is obtained by Bloch-Siegert shift based method” [0105]).
Regarding claim 9, Gui as modified further teaches wherein obtaining the spatially-resolved B 1 amplitude measurements comprises using Actual Flip Angle imaging to obtain the spatially- resolved B 1 amplitude measurements (“other methods known in the art of MRI to measure the B.sub.1.sup.+ field, such as double angle, AFI, and phase sensitivity based approaches, can be used as well”[0116]).
Regarding claim 10, Gui as modified further teaches wherein obtaining the spatially-resolved B1 amplitude measurements comprises obtaining a 1D projection (calibration scans with gradient encoding and RF pulses inherently generated 1D data [0115]).
Regarding claim 11-12, Gui as modified further teaches wherein obtaining the spatially-resolved B1 amplitude measurements comprises obtaining a 2D slice (“The display device 33 may display a two-dimensional (2D) slice image or three-dimensional (3D) image of the subject 16 generated by the image processing system” [0035]).
Regarding claim 13, Gui does not explicitly teach performing filtering on the spatially-resolved subset of the obtained spatially-resolved B1 amplitude measurements; and performing the at least one spatially-resolved analysis on the filtered spatially-resolved B 1 amplitude measurements.
However, Yankeelov teaching quantitative magnetic resonance imaging (MRI) teaches performing filtering on the spatially-resolved subset of the obtained spatially-resolved B1 amplitude measurements; and performing the at least one spatially-resolved analysis on the filtered spatially-resolved B 1 amplitude measurements (“at operation 614, the foreground (the unmasked region of the B.sub.1.sup.+ field map) may be smoothed (filtered) by fitting a pre-determined function to the B.sub.1.sup.+ field intensity values in the foreground of the B.sub.1.sup.+ field map” [0099, 0119]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the teaching statistical subset method of Yankeelov in Gui’s framework to predictively improve robustness and accuracy of RF calibration.
Regarding claim 14, Gui as modified further teaches wherein the at least one RF parameter is a flip angle (“the transmit gain for the transmit RF coil is adjusted to achieve a desired flip angle in the region of interest” [0099]).
Regarding claim 15, Gui as modified further teaches wherein the at least one RF parameter is a shim setting (“calibration scan is part of the pre-scan which may include quick higher-order shimming” [0018]).
Regarding claim 16, the structure recited is intrinsic to the method recited in claim 1, as disclosed Gui (U.S. Publication 20210018583) in view of Yankeelov (WO2023049207) as the recited structure will be used during the normal operation of the method, as discussed above with regard to claim 1. Gui as modified further teaches a Magnetic Resonance Imaging (MRI) system comprising: a gantry (fig. 1 (10) with 12-15 on both sides); at least one RF transmitter coil (fig. 1 (14)); and an RF transmitter coil controller (“the RF coil unit 15 transmits, RF driver unit 22, controller unit 25” [0023]).
Regarding claim 17, the method recited is intrinsic to the apparatus recited in claim 16, as disclosed by Gui (U.S. Publication 20210018583) in view of Yankeelov (WO2023049207) as the recited method steps will be performed during the normal operation of the apparatus, as discussed above with regard to claim 16. Gui as modified further teaches a computer program product comprising: a computer readable storage medium configured to be communicatively coupled to a computer processor, wherein the computer readable storage medium includes computer instructions which, when executed by the computer processor, cause the computer processor to perform the steps of (“Non-transitory memory 206 may store deep neural network module 208, training module 212, and MR image data 214” [0039]).
Regarding claim 19, Gui as modified further teaches obtaining the series of MRI sequences using the updated value of the at least one RF parameter obtained after the determining the updated value (where the MRI system adjusts the transmit gain of the RF driver based on the B1 field map (the MRI system adjust a transmit gain of the RF driver… based on the predicted B1+field map [0099], and subsequently acquires MR images using the updated transmit gain, the MRI system acquires a diagnostic MR image… using the transmit gain obtained at 408 [0100].
Allowable Subject Matter
Claim 18 is 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.
The following is a statement of reasons for the indication of allowable subject matter:
None of the prior art of record discloses or teaches the claimed combinations, or feature the following:
Re-claim 18, performing at least one spatially-resolved analysis on a spatially-resolved subset of the obtained spatially-resolved B 1 amplitude measurements comprises performing the at least one spatially-resolved analysis on a spatially-resolved subset of the obtained 1D projection, and determining the updated value of the at least one RF parameter to be used in a series of MRI sequences based on the performed spatially-resolved analysis comprises determining the updated value of the at least one RF parameter to be used in a series of 2D MRI sequences based on the performed spatially-resolved analysis; and wherein the method further comprises obtaining the series of 2D MRI sequences using the updated value of the at least one RF parameter obtained after the determining the updated value.
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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/TAQI R NASIR/ Examiner, Art Unit 2858
/LEE E RODAK/ Supervisory Patent Examiner, Art Unit 2858