FDETAILED 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/24/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-6, 8, 10-18, & 20-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-6, 8, 10-18, & 20-21 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 1 recites the limitation "the reference navigator" in Line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "to a neural network" in Line 4. It is unclear whether this is the same neural network from independent claim 1 or a new neural network.
Claim 20 recites the limitation "the reference navigator" in Line 8. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8, 16-18, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Polak et al (EP4394425B1; for citation purposes US20240206819A1; hereinafter referred to as Polak) in view of Splitthoff et al (US20220342018A1; hereinafter referred to as Splitthoff) and further in view of Jiang et al (W. Jiang et al., “Motion robust high resolution 3D free‐breathing pulmonary MRI using dynamic 3D image self‐navigator,” Magnetic Resonance in Medicine, vol. 79, no. 6, pp. 2954–2967, Oct. 2017; hereinafter referred to as Jiang)
Regarding Claim 1, Polak discloses a method for motion correction in a magnetic resonance imaging system (“A method for generating a motion-corrected MR image dataset of a subject” [Abstract]), the method comprising:
receiving data collected from imaging an object by the magnetic resonance imaging system, or image data reconstructed from the collected data (“The method includes: acquiring k-space data of an MR image of a subject in an imaging sequence;” [0011]);
estimating, based on a plurality of navigator signals acquired while the collected data is being collected, a motion parameter indicating a motion of the object (“wherein at least two low-resolution scout images of the subject are acquired interleaved with the k-space data of the imaging sequence; comparing the scout images with one another in order to detect and/or to estimate subject motion between the scout images; “ [0011]);
determining, based on the plurality of navigator signals, the reference navigator signal (“the act of estimating subject motion between the scout images includes registering the second scout image and optionally each further scout image with the first scout image to estimate motion parameters between the scout images. The motion trajectory of the subject is estimated by comparing the acquired k-space data with the first scout image.“ [0026];
and generating, based on the estimated motion parameter, the obtained data-consistency weighting matrix and the received data or reconstructed image data, motion-corrected image data (“and reconstructing a motion-corrected MR image dataset from the k-space data acquired in the imaging sequence by minimizing the data consistency error between the acquired k-space data and a forward model described by an encoding operator, wherein the encoding operator includes the motion trajectory of the subject during the imaging sequence, Fourier encoding, and optionally a phase operator, subsampling, and/or coil sensitivities of a multi-channel coil array.” [0011]).
Polak does not specifically discloses calculating a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal; obtaining a data-consistency weighting matrix output from a neural network by inputting thereto the calculated correlation value the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object.
However, in a similar field of endeavor, Splitthoff teaches a method for prospective or retrospective motion identification during an acquisition of magnetic resonance (MR) images of a patient by an imaging system [0005].
Splitthoff also teaches calculating a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal ( “the method comprising: acquiring a motion free reference; calculating, based on the motion free reference, a scout coil mixing matrix representing a linear combination of coils of the imaging system; acquiring MR data for the patient from the imaging system and applying the scout coil mixing matrix to the MR data to generate linearly combined motion data; calculating a second coil mixing matrix for a respective subset of MR data based on the linearly combined motion data; calculating a difference coil mixing error matrix for a respective subset of MR data based on the difference of the scout coil mixing matrix and the second coil mixing matrix;“ [0005], “In the motion case, off-diagonal values emerge and are related to the changes in the coil loadings due to the motion. The coil mixing error matrix (CMEM) is calculated (below at act A150) based on a difference between the linearly combined motion data and the linearly combined reference by determining the linear combination of that error” [0034]);
and obtaining a output from a neural network by inputting thereto the calculated correlation value, the output representing a certainty level of the collected data being corrupted by the motion of the object (“inputting the difference coil mixing error matrix into a neural network trained to output a motion assessment for the acquired MR data; and providing the motion assessment generated by the neural network to an operator.” [0005], “At Act A170, the control unit 20 provides the motion assessment generated by the neural network to an operator. A display 26 or other interface may be used to provide the motion assessment to the operator. The motion assessment may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction. In an embodiment, the motion assessment is or includes a motion score that quantifies the extent of the motion. The motion score may be compared to a threshold score. If the motion score exceeds the threshold, e.g., indicating serious or severe motion, a warning may be provided to the operator so that the operator may elect to redo the scan (or the portion of the scan). In an embodiment, the control unit 20 ranks the motion scores for each echo train and replaces or reacquires the data for each of the n-th highest ranked echo trains or above a certain level. For example, the control unit 20 may replace the data for the worst 5, 10, or 20% of the acquired data among other levels.” [0040])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak as outlined above with calculating a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal and obtaining a output from a neural network by inputting thereto the calculated correlation value, the output representing a certainty level of the collected data being corrupted by the motion of the object as taught by Splitthoff, because it rapidly produces a motion score or motion parameters that may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction [0017].
Polak in view of Splitthoff does not specifically teach obtaining a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object.
However, in a similar field of endeavor, Jiang teaches a motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data [Abstract].
Jiang also teaches obtaining a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with obtaining a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 2, Polak discloses that the collected data is k-space data acquired by a plurality of shots, each shot acquiring a plurality of k-space lines, each k-space line comprising a plurality of k-space points (“In the imaging sequence, several k-space lines may be acquired in each of a plurality of echo trains. An “echo train,” also referred to as “shot,” includes a plurality of MR echoes, e.g., spin echoes and/or gradient echoes.” [0016]),
each navigator signal of the plurality of navigator signals is acquired during one of the plurality of shots (“The low-resolution scout images may be acquired using the same imaging protocol as the k-space data of the diagnostic MR image. The contrast of the scout images may be the same as that of the diagnostic MR image because contrast matching is important for motion estimation. In certain embodiments, one low-resolution scout image may be acquired in one shot. The term “interleaved” means that at least one low-resolution scout image is acquired in between the k-space data of the imaging sequence,” [0020]),
Polak does not specifically disclose that each weighting element of the data-consistency weighting matrix corresponds to a k-space point and represents a certainty level of the corresponding k-space point being corrupted by the motion of the object.
However, in a similar field of endeavor, Jiang teaches that each weighting element of the data-consistency weighting matrix corresponds to a k-space point and represents a certainty level of the corresponding k-space point being corrupted by the motion of the object (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with each weighting element of the data-consistency weighting matrix corresponds to a k-space point and represents a certainty level of the corresponding k-space point being corrupted by the motion of the object as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 3, Polak in view of Splitthoff discloses all limitations noted above except that the step of obtaining the data-consistency weighting matrix further comprise: assigning, based on a comparison of the correlation value calculated for each navigator signal, of the plurality of navigator signals, with an empirical correlation threshold, a particular value to the number of weighting elements of the weighting matrix, the number of weighting elements corresponding to a number of k-space points acquired by one of the plurality of shots that corresponds to the navigator signal.
However, in a similar field of endeavor, Jiang teaches that the step of obtaining the data-consistency weighting matrix further comprise: assigning, based on a comparison of the correlation value calculated for each navigator signal, of the plurality of navigator signals, with an empirical correlation threshold, a particular value to the number of weighting elements of the weighting matrix, the number of weighting elements corresponding to a number of k-space points acquired by one of the plurality of shots that corresponds to the navigator signal (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with the step of obtaining the data-consistency weighting matrix further comprise: assigning, based on a comparison of the correlation value calculated for each navigator signal, of the plurality of navigator signals, with an empirical correlation threshold, a particular value to the number of weighting elements of the weighting matrix, the number of weighting elements corresponding to a number of k-space points acquired by one of the plurality of shots that corresponds to the navigator signal as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 4, Polak in view of Splitthoff discloses all limitations noted above except that the assigned particular value is a value within a predefined range, and the assigning step further comprises: assigning, in response to the comparison indicating a stronger correlation, the particular value, which approaches a first end of the predefined range to represent a higher certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating a weaker correlation, the particular value, which approaches a second end of the predefined range to represent a higher certainty level of the number of k-space points being corrupted by the motion.
However, in a similar field of endeavor, Jiang teaches that the assigned particular value is a value within a predefined range, and the assigning step further comprises: assigning, in response to the comparison indicating a stronger correlation, the particular value, which approaches a first end of the predefined range to represent a higher certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating a weaker correlation, the particular value, which approaches a second end of the predefined range to represent a higher certainty level of the number of k-space points being corrupted by the motion (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with the assigned particular value is a value within a predefined range, and the assigning step further comprises: assigning, in response to the comparison indicating a stronger correlation, the particular value, which approaches a first end of the predefined range to represent a higher certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating a weaker correlation, the particular value, which approaches a second end of the predefined range to represent a higher certainty level of the number of k-space points being corrupted by the motion as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 5, Polak in view of Splitthoff discloses all limitations noted above except that the assigned particular value is either a first predefined value or a second predefined value, and the assigning step further comprises: assigning, in response to the comparison indicating the correlation beyond the empirical correlation threshold, the first predefined value to represent a high certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating the correlation short of the empirical correlation threshold, the second predefined value to represent a high certainty level of the number of k-space points being corrupted by the motion.
However, in a similar field of endeavor, Jiang teaches that the assigned particular value is either a first predefined value or a second predefined value, and the assigning step further comprises: assigning, in response to the comparison indicating the correlation beyond the empirical correlation threshold, the first predefined value to represent a high certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating the correlation short of the empirical correlation threshold, the second predefined value to represent a high certainty level of the number of k-space points being corrupted by the motion (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with the assigned particular value is either a first predefined value or a second predefined value, and the assigning step further comprises: assigning, in response to the comparison indicating the correlation beyond the empirical correlation threshold, the first predefined value to represent a high certainty level of the number of k-space points not being corrupted by the motion, and assigning, in response to the comparison indicating the correlation short of the empirical correlation threshold, the second predefined value to represent a high certainty level of the number of k-space points being corrupted by the motion as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 6, Polak in view of Splitthoff discloses all limitations noted above except that the second predefined value is set at 0 to reject the number of k-space points because of the motion, such that the number of k-space points are not to be used in reconstruction of the image data, and the estimating step is omitted.
However, in a similar field of endeavor, Jiang teaches that the second predefined value is set at 0 to reject the number of k-space points because of the motion, such that the number of k-space points are not to be used in reconstruction of the image data, and the estimating step is omitted (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with the second predefined value is set at 0 to reject the number of k-space points because of the motion, such that the number of k-space points are not to be used in reconstruction of the image data, and the estimating step is omitted as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 8, Polak discloses all limitations noted above except that obtaining motion-free navigator data; determining different motions to be simulated; generating motion-affected navigator data by simulating a corresponding influence of each motion on the motion-free navigator data; and using the motion-free navigator data and the motion-affected navigator data to train the neural network, so as to learn a mapping from the motion-affected navigator data to a corresponding data consistency weighting matrix.
However, in a similar field of endeavor, Splitthoff teaches obtaining motion-free navigator data (“the method comprising: acquiring a motion free reference; calculating, based on the motion free reference, a scout coil mixing matrix representing a linear combination of coils of the imaging system” [0005])
determining different motions to be simulated; generating motion-affected navigator data by simulating a corresponding influence of each motion on the motion-free navigator data; and using the motion-free navigator data and the motion-affected navigator data to train the neural network, so as to learn a mapping from the motion-affected navigator data to a corresponding data consistency weighting matrix (“The training data for the model includes ground truth data or gold standard data acquired or simulated prior to training the neural network. Ground truth data and gold standard data is data that includes correct or reasonably accurate labels that are verified manually or by some other accurate method. The training data may be acquired at any point prior to inputting the training data into the neural network. The neural network may input the training data (e.g., CMM data, CMEM data and other information) and output a prediction or classification, for example of motion. In an example the prediction may include a motion score that quantifies the amount of motion in the input data. In another example, the prediction or classification may include motion parameters that quantify and describe the detected motion. The prediction is compared to the annotations (e.g., motion scores or values for respective degree of freedoms describing the 3D motion state of the patient at the time of the acquisition of a chunk of the data relative to an initial position) from the training data. “ [0039])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak as outlined above with obtaining motion-free navigator data; determining different motions to be simulated; generating motion-affected navigator data by simulating a corresponding influence of each motion on the motion-free navigator data; and using the motion-free navigator data and the motion-affected navigator data to train the neural network, so as to learn a mapping from the motion-affected navigator data to a corresponding data consistency weighting matrix as taught by Splitthoff, because it rapidly `produces a motion score or motion parameters that may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction [0017].
Regarding Claim 16, Polak discloses that the step of receiving the plurality of navigator signals further comprises acquiring the plurality of navigator signals from a non-imaging k-space echo inserted into a pulse sequence of the magnetic resonance imaging system, a respiratory bellow, an electrocardiogram signal, a camera with an external marker, a camera without an external marker, or a pilot-tone-based motion detection signal (“The disclosure thus proposes to acquire multiple scout scans/images throughout the imaging scan by interleaving scouts with the standard MR image data acquisition.” [0012], “These additional scout scans are interleaved with the imaging sequence. For example, in MPRAGE, scout scans may be acquired every third to eighth, (e.g., every fourth, fifth, or sixth), repetition time (TR) and/or every third to eighth, (e.g., every fourth, fifth, or sixth) shot. By contrast, in the standard SAMER sequences, only a single scout scan is acquired either before or after the imaging sequence. In the present disclosure, at least one scout scan may be incorporated into the imaging sequence, in addition to a scout scan before and/or after. Multiple scout scans may be incorporated into the imaging sequence. Alternatively, there may be only two scout scans, one before and one after the imaging sequence.” [0020]).
Regarding Claim 17, Polak discloses that the plurality of navigator signals further comprises acquiring the plurality of navigator signals in a form of a 3D volume, a 2D image, or a 1D signal (“The low-resolution scout images may have a spatial resolution of 2×2 mm2 to 8×8 mm2, 3×3 to 5×5 mm2, or 4×4 mm2 in the phase-encode plane. The scout images may be 2D or 3D images.” [0021]).
Regarding Claim 18, Polak discloses that the estimating step further comprises estimating, as the motion parameter, at least one of a distance of a translation and an angle degree of a rotation (“Thereby, for example six rigid-body motion parameters {circumflex over (θ)}i may be estimated for each time point i, wherein each time point may for example correspond to one echo train of the imaging sequence. I” [0104]).
Regarding Claim 20, Polak discloses a apparatus for motion correction in a magnetic resonance imaging system (“A method for generating a motion-corrected MR image dataset of a subject” [Abstract]), the apparatus comprising:
processing circuitry configured to (“The computer includes: an interface configured to receive k-space data and scout images acquired according to the method described herein, and a processing unit configured to estimate the motion-corrected image dataset “ [0051]):
receive data collected from imaging an object by the magnetic resonance imaging system, or image data reconstructed from the collected data (“The method includes: acquiring k-space data of an MR image of a subject in an imaging sequence;” [0011]);
estimate, based on a plurality of navigator signals acquired while the collected data is being collected, a motion parameter indicating a motion of the object (“wherein at least two low-resolution scout images of the subject are acquired interleaved with the k-space data of the imaging sequence; comparing the scout images with one another in order to detect and/or to estimate subject motion between the scout images; “ [0011]);
determine, based on the plurality of navigator signals, the reference navigator signal (“the act of estimating subject motion between the scout images includes registering the second scout image and optionally each further scout image with the first scout image to estimate motion parameters between the scout images. The motion trajectory of the subject is estimated by comparing the acquired k-space data with the first scout image.“ [0026];
and generate, based on the estimated motion parameter, the obtained data-consistency weighting matrix and the received data or reconstructed image data, motion-corrected image data (“and reconstructing a motion-corrected MR image dataset from the k-space data acquired in the imaging sequence by minimizing the data consistency error between the acquired k-space data and a forward model described by an encoding operator, wherein the encoding operator includes the motion trajectory of the subject during the imaging sequence, Fourier encoding, and optionally a phase operator, subsampling, and/or coil sensitivities of a multi-channel coil array.” [0011]).
Polak does not specifically disclose to calculate a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal; obtain a data-consistency weighting matrix output from a neural network by inputting thereto the calculated correlation value the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object.
However, in a similar field of endeavor, Splitthoff teaches a method for prospective or retrospective motion identification during an acquisition of magnetic resonance (MR) images of a patient by an imaging system [0005].
Splitthoff also teaches calculate a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal ( “the method comprising: acquiring a motion free reference; calculating, based on the motion free reference, a scout coil mixing matrix representing a linear combination of coils of the imaging system; acquiring MR data for the patient from the imaging system and applying the scout coil mixing matrix to the MR data to generate linearly combined motion data; calculating a second coil mixing matrix for a respective subset of MR data based on the linearly combined motion data; calculating a difference coil mixing error matrix for a respective subset of MR data based on the difference of the scout coil mixing matrix and the second coil mixing matrix;“ [0005], “In the motion case, off-diagonal values emerge and are related to the changes in the coil loadings due to the motion. The coil mixing error matrix (CMEM) is calculated (below at act A150) based on a difference between the linearly combined motion data and the linearly combined reference by determining the linear combination of that error” [0034]);
and obtain a output from a neural network by inputting thereto the calculated correlation value, the output representing a certainty level of the collected data being corrupted by the motion of the object (“inputting the difference coil mixing error matrix into a neural network trained to output a motion assessment for the acquired MR data; and providing the motion assessment generated by the neural network to an operator.” [0005], “At Act A170, the control unit 20 provides the motion assessment generated by the neural network to an operator. A display 26 or other interface may be used to provide the motion assessment to the operator. The motion assessment may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction. In an embodiment, the motion assessment is or includes a motion score that quantifies the extent of the motion. The motion score may be compared to a threshold score. If the motion score exceeds the threshold, e.g., indicating serious or severe motion, a warning may be provided to the operator so that the operator may elect to redo the scan (or the portion of the scan). In an embodiment, the control unit 20 ranks the motion scores for each echo train and replaces or reacquires the data for each of the n-th highest ranked echo trains or above a certain level. For example, the control unit 20 may replace the data for the worst 5, 10, or 20% of the acquired data among other levels.” [0040])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak as outlined above with calculate a correlation value between each navigator signal of the plurality of navigator signals and the reference navigator signal and obtain a output from a neural network by inputting thereto the calculated correlation value, the output representing a certainty level of the collected data being corrupted by the motion of the object as taught by Splitthoff, because it rapidly produces a motion score or motion parameters that may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction [0017].
Polak in view of Splitthoff does not specifically teach obtain a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object.
However, in a similar field of endeavor, Jiang teaches a motion robust high resolution 3D free-breathing pulmonary MRI utilizing a novel dynamic 3D image navigator derived directly from imaging data [Abstract].
Jiang also teaches obtain a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with obtain a data-consistency weighting matrix, where the data-consistency weighting matrix including a number of weighting elements each representing a certainty level of the collected data being corrupted by the motion of the object as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Claims 10-15 & 21 are rejected under 35 U.S.C. 103 as being unpatentable over Polak in view of Splitthoff and further in view of Jiang as applied to Claim 2 above, and further in view of Schlemper et al (US20200294229A1; hereinafter referred to as Schlemper)
Regarding Claim 10, Polak in view of Splitthoff and further in view of Jiang discloses all limitations noted above except that the step of generating the motion-corrected image data further comprises: applying the received data or the reconstructed image data, the weighting matrix, and the motion parameter to a neural network; and obtaining, as the motion-corrected image data, an output of the neural network.
However, in a similar field of endeavor, Schlemper teaches techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system [Abstract].
Schlemper also teaches that the step of generating the motion-corrected image data further comprises: applying the received data or the reconstructed image data, the weighting matrix, and the motion parameter to a neural network; and obtaining, as the motion-corrected image data, an output of the neural network (“In the illustrated embodiment, neural network model 204 includes pre-reconstruction neural network 210 configured to perform one or more pre-processing tasks (e.g., motion correction, RF interference removal, noise removal), reconstruction neural network 212 configured to reconstruct one or more images from the output of the neural network 210 (e.g., including when the MR data is undersampled), and post-reconstruction neural network 214 configured to perform one or more post-processing tasks (e.g., combining images generated from data collected by different coils, image registration, signal averaging, denoising, and correction for intensity variation) on the MR images generated by the reconstruction neural network 212.” [0092]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff and further in view of Jiang as outlined above with the step of generating the motion-corrected image data further comprises: applying the received data or the reconstructed image data, the weighting matrix, and the motion parameter to a neural network; and obtaining, as the motion-corrected image data, an output of the neural network as taught by Schlemper, because it allows for the investigation of internal structures and/or biological processes within the body for diagnostic, therapeutic and/or research purposes [0003].
Regarding Claim 11, Polak in view of Splitthoff and further in view of Jiang discloses all limitations noted above except that the applying step further comprises applying the obtained data or the reconstructed image data, the generated weighting matrix, and the estimated motion parameter to a model-driven deep learning framework having a pre-determined number of iterations, wherein the model-driven deep learning framework includes a neural network and performs a data consistency process.
However, in a similar field of endeavor, Schlemper teaches that the applying step further comprises applying the obtained data or the reconstructed image data, the generated weighting matrix, and the estimated motion parameter to a model-driven deep learning framework having a pre-determined number of iterations, wherein the model-driven deep learning framework includes a neural network and performs a data consistency process (“In the illustrated embodiment, neural network model 204 includes pre-reconstruction neural network 210 configured to perform one or more pre-processing tasks (e.g., motion correction, RF interference removal, noise removal), reconstruction neural network 212 configured to reconstruct one or more images from the output of the neural network 210 (e.g., including when the MR data is undersampled), and post-reconstruction neural network 214 configured to perform one or more post-processing tasks (e.g., combining images generated from data collected by different coils, image registration, signal averaging, denoising, and correction for intensity variation) on the MR images generated by the reconstruction neural network 212.” [0092], “one or more of the blocks 316-1, 316-2, . . . , 316-n may have the architecture of illustrative block 316-i in FIG. 3B, which includes a data consistency block 320, and a convolutional neural network block 350” [0144]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff and further in view of Jiang as outlined above with the applying step further comprises applying the obtained data or the reconstructed image data, the generated weighting matrix, and the estimated motion parameter to a model-driven deep learning framework having a pre-determined number of iterations, wherein the model-driven deep learning framework includes a neural network and performs a data consistency process as taught by Schlemper, because it allows for the investigation of internal structures and/or biological processes within the body for diagnostic, therapeutic and/or research purposes [0003].
Regarding Claim 12, Polak in view of Splitthoff and further in view of Jiang discloses all limitations noted above except that the neural network is a U-net, a residual U-net, a residual network, an inception-residual network, or a linear convolutional network.
However, in a similar field of endeavor, Schlemper teaches that the neural network is a U-net, a residual U-net, a residual network, an inception-residual network, or a linear convolutional network (“The neural network 240 may be implemented using a U-Net architecture. Alternatively, a ResNet type architecture may be used where convolutional blocks have residual connections.” [0131]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff and further in view of Jiang as outlined above with the neural network is a U-net, a residual U-net, a residual network, an inception-residual network, or a linear convolutional network as taught by Schlemper, because it allows for the investigation of internal structures and/or biological processes within the body for diagnostic, therapeutic and/or research purposes [0003].
Regarding Claim 13, Polak in view of Splitthoff and further in view of Jiang discloses all limitations noted above except that the neural network is a complex U-net, and a plurality of parameters of the complex U-net are shared across the pre- determined number of iterations.
However, in a similar field of endeavor, Schlemper teaches that the neural network is a complex U-net, and a plurality of parameters of the complex U-net are shared across the pre- determined number of iterations (“The neural network 240 may be implemented using a U-Net architecture. Alternatively, a ResNet type architecture may be used where convolutional blocks have residual connections.” [0131]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff and further in view of Jiang as outlined above with the neural network is a complex U-net, and a plurality of parameters of the complex U-net are shared across the pre- determined number of iterations as taught by Schlemper, because it allows for the investigation of internal structures and/or biological processes within the body for diagnostic, therapeutic and/or research purposes [0003].
Regarding Claim 14, Polak in view of Splitthoff discloses all limitations noted above except that the data consistency process uses a conjugate gradient iteration algorithm, a proximal gradient algorithm, an orthogonal matching pursuit algorithm, an iterative hard thresholding algorithm, a split Bregman-based algorithm, or a gradient descent algorithm.
However, in a similar field of endeavor, Jiang teaches that the data consistency process uses a conjugate gradient iteration algorithm, a proximal gradient algorithm, an orthogonal matching pursuit algorithm, an iterative hard thresholding algorithm, a split Bregman-based algorithm, or a gradient descent algorithm (“This optimization problem in Equation [1] was solved by a fast iterative shrinkage-thresholding algorithm (FISTA) (61) with singular value thresholding and randomized block shifting (62).” [Dynamic 3D Self-Navigator Using Locally Low-Rank Constraints], “Soft-gating parameters were experimentally tuned: a small set of parameters were tried and image quality was assessed visually to balance between motion blurring and SNR. We found that the weighting parameter α suggested by Zhang et al. (47) (3/max (d[n])) provided good tradeoff, and hence was used for the rest of the study. The threshold parameter was set as 25% of the maximum respiratory position. We solved the optimization in Equation [3] using FISTA (61).” [Soft-Gating L1-ESPIRiT Reconstruction]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with the data consistency process uses a conjugate gradient iteration algorithm, a proximal gradient algorithm, an orthogonal matching pursuit algorithm, an iterative hard thresholding algorithm, a split Bregman-based algorithm, or a gradient descent algorithm as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Regarding Claim 15, Polak discloses all limitations noted above except obtaining fully-sampled motion-free k-space data acquired by a plurality of shots; generating motion-corrupted k-space data by simulating corresponding influences caused by motions having different motion parameters on different shots; generating navigator data corresponding to the motions having different motion parameters; generating the data-consistency weighting matrixes based on the navigator data; and using the fully-sampled motion-free k-space data, the motion-corrupted k-space data, the data-consistency weighting matrixes, and the motion parameters to train the deep learning framework, so as to learn a mapping from the motion-corrupted k-space data to the fully- sampled motion-free image data.
However, in a similar field of endeavor, Splitthoff teaches obtaining fully-sampled motion-free k-space data acquired by a plurality of shots (“the method comprising: acquiring a motion free reference; calculating, based on the motion free reference, a scout coil mixing matrix representing a linear combination of coils of the imaging system” [0005])
generating motion-corrupted k-space data by simulating corresponding influences caused by motions having different motion parameters on different shots; generating navigator data corresponding to the motions having different motion parameters; generating the data-consistency weighting matrixes based on the navigator data; and using the fully-sampled motion-free k-space data, the motion-corrupted k-space data, the data-consistency weighting matrixes, and the motion parameters to train the deep learning framework, so as to learn a mapping from the motion-corrupted k-space data to the fully- sampled motion-free image data (“The training data for the model includes ground truth data or gold standard data acquired or simulated prior to training the neural network. Ground truth data and gold standard data is data that includes correct or reasonably accurate labels that are verified manually or by some other accurate method. The training data may be acquired at any point prior to inputting the training data into the neural network. The neural network may input the training data (e.g., CMM data, CMEM data and other information) and output a prediction or classification, for example of motion. In an example the prediction may include a motion score that quantifies the amount of motion in the input data. In another example, the prediction or classification may include motion parameters that quantify and describe the detected motion. The prediction is compared to the annotations (e.g., motion scores or values for respective degree of freedoms describing the 3D motion state of the patient at the time of the acquisition of a chunk of the data relative to an initial position) from the training data. “ [0039])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak as outlined above with obtaining fully-sampled motion-free k-space data acquired by a plurality of shots; generating motion-corrupted k-space data by simulating corresponding influences caused by motions having different motion parameters on different shots; generating navigator data corresponding to the motions having different motion parameters; generating the data-consistency weighting matrixes based on the navigator data; and using the fully-sampled motion-free k-space data, the motion-corrupted k-space data, the data-consistency weighting matrixes, and the motion parameters to train the deep learning framework, so as to learn a mapping from the motion-corrupted k-space data to the fully- sampled motion-free image data as taught by Splitthoff, because it rapidly `produces a motion score or motion parameters that may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction [0017].
Regarding Claim 21, Polak discloses all limitations above except the step of obtaining the data-consistency weighting matrix further comprises obtaining the data-consistency weighting matrix output from a first neural network by inputting thereto the calculated correlation value.
However, in a similar field of endeavor, Splitthoff teaches obtaining a output from from a first neural network by inputting thereto the calculated correlation value (“inputting the difference coil mixing error matrix into a neural network trained to output a motion assessment for the acquired MR data; and providing the motion assessment generated by the neural network to an operator.” [0005], “At Act A170, the control unit 20 provides the motion assessment generated by the neural network to an operator. A display 26 or other interface may be used to provide the motion assessment to the operator. The motion assessment may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction. In an embodiment, the motion assessment is or includes a motion score that quantifies the extent of the motion. The motion score may be compared to a threshold score. If the motion score exceeds the threshold, e.g., indicating serious or severe motion, a warning may be provided to the operator so that the operator may elect to redo the scan (or the portion of the scan). In an embodiment, the control unit 20 ranks the motion scores for each echo train and replaces or reacquires the data for each of the n-th highest ranked echo trains or above a certain level. For example, the control unit 20 may replace the data for the worst 5, 10, or 20% of the acquired data among other levels.” [0040])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak as outlined above with obtaining a output from a neural network by inputting thereto the calculated correlation value as taught by Splitthoff, because it rapidly produces a motion score or motion parameters that may be used for the prospective adaption of the acquisition parameters, the generation of a warning for physicians indicating reacquisition might be beneficial, and/or initialization of other forms of retrospective correction [0017].
Polak in view of Splitthoff does not specifically teach obtaining a data-consistency weighting matrix.
However, in a similar field of endeavor, Jiang teaches obtaining a data-consistency weighting matrix (“The concept of soft-gating is illustrated in the top branch of Figure 1. The weights effectively take account for motion induced data inconsistency. We use the soft-gating approach by modifying the basic image reconstruction model (Eq. [2]) to incorporate appropriate weights W: Here, W is a diagonal matrix containing the soft-gating weights, which are applied to the data consistency term. Let w[n] be the vector representing the diagonal entries of W. A different weight w[n] is estimated for each radial spoke n, ranging between 0 and 1: where d[n] represents the estimated respiratory motion with respect to the end of expiration or the end of inspiration (we picked the end of expiration state in this manuscript since more time is typically spent in expiration during a respiration cycle), threshold is a threshold of the respiratory motion, and α is a scaling factor. For data experiencing more respiratory motion corruption, their weights are smaller and thus they contribute less to the data consistency term in Equation [3]. “ [Motion Compensated Reconstruction])
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff as outlined above with obtaining a data-consistency weighting matriX as taught by Jiang, because soft gating is a computationally efficient iterative method in which the data consistency term in the optimization is preferentially weighted based on distances from the chosen respiratory motion state [Introduction].
Polak in view of Splitthoff and further in view of Jiang discloses all limitations noted above except that the step of generating the motion-corrected image data further comprises inputting the estimated motion parameter, the obtained data-consistency weighting matrix, and the received data or the reconstructed image data to a second neural network, and obtaining, as the motion-corrected image data, an output of the second neural network.
However, in a similar field of endeavor, Schlemper teaches that the step of generating the motion-corrected image data further comprises inputting the estimated motion parameter, the obtained data-consistency weighting matrix, and the received data or the reconstructed image data to a second neural network, and obtaining, as the motion-corrected image data, an output of the second neural network (“In the illustrated embodiment, neural network model 204 includes pre-reconstruction neural network 210 configured to perform one or more pre-processing tasks (e.g., motion correction, RF interference removal, noise removal), reconstruction neural network 212 configured to reconstruct one or more images from the output of the neural network 210 (e.g., including when the MR data is undersampled), and post-reconstruction neural network 214 configured to perform one or more post-processing tasks (e.g., combining images generated from data collected by different coils, image registration, signal averaging, denoising, and correction for intensity variation) on the MR images generated by the reconstruction neural network 212.” [0092]).
It would have been obvious to an ordinary skilled person in the art before the effective filing date of the claimed invention to modify the system of Polak in view of Splitthoff and further in view of Jiang as outlined above with the step of generating the motion-corrected image data further comprises inputting the estimated motion parameter, the obtained data-consistency weighting matrix, and the received data or the reconstructed image data to a second neural network, and obtaining, as the motion-corrected image data, an output of the second neural network as taught by Schlemper, because it allows for the investigation of internal structures and/or biological processes within the body for diagnostic, therapeutic and/or research purposes [0003].
Conclusion
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/Steven Maldonado/
Patent Examiner, Art Unit 3797
/CHRISTOPHER KOHARSKI/Supervisory Patent Examiner, Art Unit 3797