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 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.
Claim(s) 1—4, 6, 13, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pfeuffer et al. (US-20150285877-A1)as applied to claim in view of Takeshima (US-20220019850-A1).
Regarding claim 1
Pfeuffer discloses
A method for automatically determining a configuration for magnetic resonance imaging (MRI) ([0039] & Claim 13), the method being computer-implemented ([0039]) and comprising:
receiving an initial scanner model for an MRI scanner ([0040]), the initial scanner model specifying a deviation of a main magnetic field from a predefined target main magnetic field ([0022]), specifying a deviation of a magnetic field
gradient from a predefined target gradient field ([0047] & [0075] & [0022]), or a combination thereof;
receiving MRI measurement data measured by using the MRI scanner ([0002] & [0011]);
generating a first updated scanner model, the generating of the first updated scanner model ([0047]—[0048], the maps are updated and used to calculate the gradients),
Pfeuffer does not disclose
“comprising applying a trained first machine learning model (MLM) to first input data that depends on the initial scanner model and the MRI measurement data; and
determining the configuration for MRI depending on the first updated scanner model”.
Takeshima, however, teaches
comprising applying a trained first machine learning model (MLM) to first input data that depends on the initial scanner model and the MRI measurement data ([0035] & [0043]—[0044]); and
determining the configuration for MRI depending on the first updated scanner model ([0082], the updated learning model changes the MRI configuration. Then this configuration is used for the new scan.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “machine learning module” as taught by Takeshima in the method of Pfeuffer.
The justification for this modification would be to speed up analysis and improve diagnostic accuracy.
Regarding claim 2
Pfeuffer in view of Takeshima teach the method of claim 1,
Pfeuffer, applied to claim 2, further teaches
wherein the configuration for MRI comprises respective values for one or more parameters specifying a data acquisition by the MRI scanner ([0011]—[0015], the B.sub.1 maps are created and updated and are data acquisition parameters).
Regarding claim 3
Pfeuffer in view of Takeshima teach the method of claim 2,
Pfeuffer, applied to claim 3, further discloses
wherein the one or more parameters specifying the data acquisition comprises:
one or more currents for active shimming ([0004], shim coils are active shimming by defintion);
one or more parameters defining an application of an RF-pulse ([0011], the parameters are the maps of the RF coils);
one or more parameters defining a k-space sampling scheme ([0033], takes into account B.sub.1 maps and also k-space trajectories); or any combination thereof.
Regarding claim 4
Pfeuffer in view of Takeshima teach the method of claim 1,
Takeshima, applied to claim 4, further teaches
wherein the configuration for MRI comprises data specifying an MRI image reconstruction ([0031]).
Regarding claim 6
Pfeuffer in view of Takeshima teach the method of claim 1,
Pfeuffer, applied to claim 6, further teaches
further comprising receiving a sequence description of an MRI sequence corresponding to the MRI measurement data ([0009]—[0011]),
wherein the first input data depends on the sequence description ([0071], the user inputs first input data, example SNR).
Regarding claim 13
Pfeuffer in view of Takeshima teach the method of claim 1,
Takeshima, applied to claim 13, further teaches
further comprising:
performing an MRI of an object, the performing of the MRI of the object ([0017]) comprising:
configuring the MRI scanner according to the determined configuration for
MRI ([0024]), generating further MRI measurement data representing the object using the configured MRI scanner ([0015]—[0017]), and reconstructing an MRI image of the object based on the further MRI measurement data ([0087] & [0031] & [0039]); or
generating further MRI measurement data representing the object using the
MRI scanner and reconstructing an MRI image of the object based on the further MRI measurement data according to the determined configuration for MRI ([0015]—[0017]).
Regarding claim 15
Pfeuffer discloses
A data processing apparatus ([0002] & [0011], apparatus processes MRI data), comprising:
at least one computing unit ([0039]) configured to automatically determine a configuration for magnetic resonance imaging (MRI) ([0039] & Claim 13), the at least one computing unit configured to automatically determine the configuration for MRI comprising the at least one computing unit being configured to (Claim 13):
receive an initial scanner model for an MRI scanner ([0053]), the initial scanner model specifying a deviation of a main magnetic field from a predefined target main magnetic field ([0022]), specifying a deviation of a magnetic field gradient from a predefined target gradient field, or a combination thereof ([0047] & [0075] & [0022]);
receive MRI measurement data measured using the MRI scanner ([0002] & [0011]);
generate a first updated scanner model, the generation of the first updated
scanner model ([0047]—[0048], the maps are updated and used to calculate the gradients)
Pfeuffer does not disclose
“comprising application of a trained first machine learning model (MLM) to first input data that depends on the initial scanner model and the MRI measurement data; and
determine the configuration for MRI depending on the first updated scanner
model”.
Takeshima, however, teaches
comprising application of a trained first machine learning model (MLM) to first input data that depends on the initial scanner model and the MRI measurement data ([0035] & [0043]—[0044]); and
determine the configuration for MRI depending on the first updated scanner
model ([0082], the updated learning model changes the MRI configuration. Then this configuration is used for the new scan.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “machine learning module” as taught by Takeshima in the method of Pfeuffer.
The justification for this modification would be to speed up analysis and improve diagnostic accuracy.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pfeuffer et al. (US-20150285877-A1)as applied to claim in view of Takeshima (US-20220019850-A1) in view of Dannels (US-20120032676-A1).
Regarding claim 5
Pfeuffer in view of Takeshima teach the method of claim 4,
Pfeuffer in view of Takeshima do not teach
“wherein the data specifying an MRI image reconstruction
comprises:
a correction map for diffusion imaging;
bias field correction data;
undistortion data for compensating a potential geometric image distortion; or
any combination thereof”.
Dannels, however, teaches
wherein the data specifying an MRI image reconstruction
comprises:
a correction map for diffusion imaging ([0023]);
bias field correction data ([0004] & [0014]);
undistortion data for compensating a potential geometric image distortion ([0036] & [0056]); or
any combination thereof
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “correction maps compensating for geometric distortion” as taught by Dannels in the method of Pfeuffer in view of Takeshima.
The justification for this modification would be to correct for non-uniformity in MRI images ([0014], Dannels).
Claim(s) 11, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pfeuffer et al. (US-20150285877-A1)as applied to claim in view of Takeshima (US-20220019850-A1) in view of Bafkar et al. (US-20220222823-A1).
Regarding claim 11
Pfeuffer in view of Takeshima teach the method of claim 1,
Although strongly implied, Pfeuffer in view of Takeshima do not explicitly teach
“further comprising receiving a patient model specifying body properties of a patient or a patient population,
wherein the first input data depends on the patient model”.
Shahidi, however, discloses
further comprising receiving a patient model specifying body properties of a patient or a patient population,
wherein the first input data depends on the patient model ([0160]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “input parameters depending on patient” as taught by Shahidi in the method of Pfeuffer in view of Takeshima.
The justification for this modification would be to create a patient-specific MRI image that images a specific area of that subject’s affliction.
Regarding claim 12
Pfeuffer in view of Takeshima teach the method of claim 1,
Although strongly implied, Pfeuffer in view of Takeshima do not explicitly teach
“wherein the MRI measurement data comprises patient adjustment scan data, patient diagnostic scan data, phantom calibration scan data, or any combination thereof”.
Bafkar, however, teaches
wherein the MRI measurement data comprises patient adjustment scan data, patient diagnostic scan data, phantom calibration scan data, or any combination
thereof ([0160]—[0161], he input parameters are diagnostic data and calibration data [0007]),
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “diagnostic/calibration data” as taught by Bafkar in the method of Pfeuffer in view of Takeshima.
The justification for this modification would be to 1) calibrate the MRI system on a patient-specific basis, and 2) use patient-specific data to get a clearer image of specific areas of that patient that need analyzing.
Allowable Subject Matter
Claim 7—10, 14, 16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 7
Nothing in the prior art of record teaches or discloses
“fused data is generated by fusing at least the encoded MRI measurement data and the encoded model data; and
the first MLM comprises a model decoder module, and the first updated scanner model is generated by applying the model decoder module to the fused data”.
In conjunction with the rest of the claim language.
Regarding claim 8
Nothing in the prior art of record teaches or discloses
“generating a second updated scanner model depending on the first updated scanner model, depending on the initial scanner model, or depending on a
combination thereof using an estimation model for predicting a temporal state change of the MRI scanner; and
determining the configuration for MRI or a further configuration for MRI depending on the second updated scanner model”.
In conjunction with the rest of the claim language.
Regarding claims 9 & 10
The claims are allowable due to their dependencies on objected-to claim 8.
Regarding claim 14
Nothing in the prior art of record teaches or discloses
“generating a reconstructed scanner model, the generating of the reconstructed scanner model comprising applying the first MLM to first input training data that depends on the perturbed scanner model and the MRI training data; and
updating the first MLM depending on a value of a predefined loss function that depends on the initial scanner training model and the reconstructed scanner model”.
In conjunction with the rest of the claim language.
Regarding claim 16
Nothing in the prior art of record teaches or discloses
“generate a reconstructed scanner model, the generation of the reconstructed
scanner model comprising application of the first MLM to first input training data that depends on the perturbed scanner model and the MRI training data; and
update the first MLM depending on a value of a predefined loss function that
depends on the initial scanner training model and the reconstructed scanner model”.
In conjunction with the rest of the claim language.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK WENDEROTH whose telephone number is (571)270-1945. The examiner can normally be reached M-F 7 a.m. - 4 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Walter Lindsay can be reached at 571-272-1674. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WALTER L LINDSAY JR/Supervisory Patent Examiner, Art Unit 2852
/Frederick Wenderoth/
Examiner, Art Unit 2852
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