DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claims 1-16 have been considered but are moot in view of new grounds of rejections.
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.
Claims 1-16 rejected under 35 U.S.C. 103 as being unpatentable over Arnold [US 20190150764 A1] in view of Sharma [US 20220082647 A1].
As per claim 1, Arnold teaches a method, comprising:
acquiring a set of perfusion data for a region of interest using an imaging system (Arnold Fig 1 ¶0033);
obtaining an artery signal from the set of perfusion data (Arnold Fig 1 arterial input function (AIF));
obtaining a tissue signal from the set of perfusion data (Arnold Fig 1, ¶0042 tissue concentration time curve (CTC)); and
providing the artery signal and the tissue signal to serve as inputs to one or more neural networks (Arnold ¶0049 “for a selected voxel, given CTC and AIF measurements, a pattern recognition model in the form of a novel bi-input convolutional neural network (bi-CNN), which takes the two inputs (CTC, AIF)”)
to determine one or more hemodynamic parameters for the region of interest (Arnold ¶0037, ¶0039 “the report may include anatomical images, perfusion parameter maps including CBF, CBV, MTT, TPP, Tmax…”),
wherein the one or more neural networks are trained using one or more clinical perfusion data (Arnold ¶0050, ¶0068, training the CNN using measured data)
Arnold does not expressly teach training with modified synthetic tissue data generated based one or more synthetic data modified or combined with one or more clinical perfusion data.
Sharma, in a related field of determining a cardiac metric using neural networks (Sharma ¶0002), teaches training with modified synthetic tissue data generated based one or more synthetic data modified or combined with one or more clinical perfusion data (Sharma Fig 4, ¶0110 “The cycle GAN 400 is trained by minimizing the loss 406”, ¶0109, real data 404 is combined / modified with synthetic data from generators 408 to create realistic synthetic data 402 and loss signals).
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the method in Arnold by utilizing modified synthetic data for training and machine learning. The motivation would be to generate precise quantitative assessment of cardiac function and/or of lesion-specific cardiac dysfunction (Sharma ¶0022).
As per claim 2, Arnold in view of Sharma further teaches wherein the set of perfusion data comprises at least one of computed tomography (CT) perfusion data, magnetic resonance imaging (MRI) perfusion data, positron emission tomography (PET) perfusion data, single photon emission computed tomography (SPECT) data, or ultrasound imaging data (Arnold ¶0033).
As per claim 3, Arnold in view of Sharma further teaches wherein the one or more synthetic data are generated based on a defined ground truth model (Sharma ¶0026-¶0027 “the input layer of the neural network system may be further configured to receive data representative of a prior medical examination”, ¶0057-¶0058 “The training of the neural network system … may comprise a coronary artery anatomical model and/or a coronary perfusion model. The training may comprise at least one of the following steps: adapting the coronary artery anatomical model to anatomical images; mapping of the adapted coronary artery anatomical model to the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state; …The coronary perfusion model may use information computed from the coronary artery anatomical model as input.” This model corresponds to a ground truth model, that uses actual information)
As per claim 4, Arnold in view of Sharma further teaches wherein the tissue signal is a convolution of the artery signal and a residual impulse function of the region of interest, and wherein the one or more hemodynamic parameters are determined from the residual impulse function (Arnold ¶0042 “where the measured tissue concentration time curve (CTC) of a voxel is directly proportional to the convolution of the arterial input function (AIF) and the residue function (R),”).
As per claim 5, Arnold in view of Sharma further teaches comprising correcting non-idealities in the set of perfusion data based on output from the one or more neural networks (Sharma Fig 4, training based on loss).
As per claim 6, Arnold in view of Sharma further teaches wherein the one or more hemodynamic parameters comprise at least one of a blood flow (BF), a blood volume (BV), a mean transit time (MTT), or a time to maximum (TMAX) (Arnold ¶0037, ¶0039).
As per claims 7-12 have limitations similar to claims 1-6 and are rejected for same reasons as above. Arnold in view of Sharma further teaches a system comprising: one or more processors; and memory, accessible by the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations (Arnold Fig 1).
As per claim 13, Arnold teaches a method for training one or more neural networks, comprising:
generating a set of
obtaining an artery signal from a set of perfusion data (Arnold ¶0043 equation 1, AIF(t));
generating a
training the one or more neural networks using a signal generated using the
Arnold does not disclose the data is synthetic data, based on a defined ground truth model, generating modified synthetic tissue signals, and training using the modified synthetic data.
Sharma, in a related field of determining a cardiac metric using neural networks (Sharma ¶0002), teaches using synthetic data (Sharma ¶0109 synthetic data from generators 408), based on a defined ground truth model (Sharma ¶0026-¶0027 ¶0057-¶0058 as discussed above with respect to claim 3), generating modified synthetic tissue signals (Sharma ¶0109, real data 404 is combined / modified with synthetic data from generators 408 to create realistic synthetic data 402 and loss signals), and training using the modified synthetic data (Sharma Fig 4, ¶0110 “The cycle GAN 400 is trained by minimizing the loss 406…”,).
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the method in Arnold by generating and utilizing synthetic and modified synthetic data for training the CNNs. The motivation would be to generate precise quantitative assessment of cardiac function and/or of lesion-specific cardiac dysfunction (Sharma ¶0022).
As per claim 14, Arnold in view of Sharma further teaches wherein the modified synthetic tissue signal comprises a perturbation related to perturbating of the perfusion data (Sharma Fig 4 perturbation / deviation is provided since loss is calculated for the perfusion data).
As per claim 15, Arnold in view of Sharma further teaches wherein the perturbation is associated with registration errors or with acquisition errors (Sharma ¶0092 “noisy training labels may be exploited by specifically encoding a weak supervision in the form of labeling functions. Labeling functions may have widely varying error rates and may conflict on certain data points. Labeling functions may be modeled as a generative process, leading to an automated denoising by learning the accuracies”, labeling/ registration loss is associated with the learning).
As per claim 16, Arnold in view of Sharma further teaches wherein the set of perfusion data comprises at least one of computed tomography (CT) perfusion data, magnetic resonance imaging (MRI) perfusion data, positron emission tomography (PET) perfusion data, single photon emission computed tomography (SPECT) data, or ultrasound imaging data (Arnold ¶0033).
Status of claims 17-20
Claims 17-20 have no prior art rejections. References of record are considered closest prior art. Claims 17-20 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, and if all objections to claim are overcome.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OOMMEN JACOB whose telephone number is (571)270-5166. The examiner can normally be reached 8:00-4:00.
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/Oommen Jacob/ Primary Examiner, Art Unit 3797