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 .
Specification
The disclosure is objected to because of the following informalities: Page 12, lines 19-20 has an unintentional paragraph break.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because a product claim to a software program that does not also contain at least one structural limitation has no physical or tangible form, and thus does not fall within any statutory category.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (“Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition”), hereinafter referred to as Zhang. With reference to claim 1, Zhang teaches a medical system comprising: - a memory storing machine executable instructions (Section 4.4, last paragraph); - a computational system (Section 4.4, last paragraph), wherein execution of the machine executable instructions causes the computational system to: - receive a test magnetic resonance image reconstructed from undersampled k-space data (see the loop in Fig 1, the evaluator receives an input at each iteration of the loop); - receive a test signal in response to causing an input of the test magnetic resonance image into an out of distribution testing neural network (output of the evaluator network on Fig 12), wherein the test neural network is configured for outputting the test signal in response to receiving the test magnetic resonance image, wherein the test signal is descriptive if the test magnetic resonance image is within a training distribution defined by a set of training data (Section 4.3 "When training the reconstruction network, we will be using the evaluator as additional regularization to encourage the reconstructed image to have phantasized k-space rows that look as if they came from the distribution of true measured rows", "we leverage the idea of adversarial learning and train a discriminator-like evaluator to score the measurements and meanwhile encourage the reconstruction network to produce results that match the true measurement distribution"; see also the joint adversarial training of the evaluator network and the reconstruction network in sections 4.3 and 4.4 in relation with equations (3)-(6) and with Fig 3; see also Fig 12 showing the evaluator network and the reconstruction network); and - provide (206) the test signal (section 4.5, either for another iteration or for a stopping criterion). With reference to claim 2, Zhang further teaches execution of the machine executable instructions further causes the computational system to cause a reconstruction of a clinical magnetic resonance image from the undersampled k-space data according using a compressed sensing magnetic resonance imaging reconstruction algorithm if the test signal indicates that the test magnetic resonance image is within the training distribution (Section 4.5). With reference to claim 3, Zhang further teaches the compressed sensing magnetic resonance imaging reconstruction algorithm is configured for reconstructing the clinical magnetic resonance image iteratively using an image processing neural network (Section 4.1). With reference to claim 4, Zhang further teaches the image processing neural network is configured as any one of the following: - a denoising filter for denoising an intermediate image between each iteration; and - as an image compression algorithm (Section 4.1). With reference to claim 5, Zhang further teaches the compressed sensing magnetic resonance imaging reconstruction algorithm is a numerical image reconstruction algorithm configured for finding solutions to underdetermined linear systems descriptive of a reconstruction of the clinical magnetic resonance image from the undersampled k-space data (Section 4.1).
With reference to claim 6, Zhang further teaches the compressed sensing magnetic resonance imaging reconstruction algorithm comprises an image reconstruction neural network configured for reconstructing the clinical magnetic resonance image from the undersampled k-space data at each stage of an iterative compressed sensing algorithm (Section 4.1).
With reference to claim 7, Zhang further teaches the out of distribution testing neural network is trained as a discriminator neural network in a generative adversarial network using the training data (Section 4.3). With reference to claim 8, Zhang further teaches the out of distribution testing neural network is trained as a discriminator neural network in a generative adversarial network (500, 700) using the training data (Section 4.3). With reference to claim 9, Zhang further teaches the generative adversarial network comprises a generative neural network configured for generating simulated images in response to receiving a simulated test image (Section 4.3). With reference to claim 10, Zhang further teaches the test magnetic resonance image is reconstructed from the undersampled k- space data using a Fourier transform (Section 3, Section 4.3). With reference to claim 11, Zhang further teaches the memory further contains pulse sequence commands configured to control a magnetic resonance imaging system (302) to acquire the undersampled k-space data from the region of interest, wherein execution of the machine executable instructions further causes the computational system to acquire the undersampled k-space data by controlling the magnetic resonance imaging system with the pulse sequence commands (Fig. 1). With reference to claim 12, Zhang further teaches execution of the machine executable instructions further causes the computational system to perform any one of the following if the test signal indicates that the test magnetic resonance image is outside of the training distribution: - provide a warning signal; - request a reacquisition of the undersampled k-space data; - request a reconstruction of the clinical magnetic resonance image using a purely numerical reconstruction algorithm; - control the magnetic resonance imaging system to continue acquisition of the undersampled k-space data; reconstruct the test magnetic resonance image from undersampled k-space data descriptive of a region of interest of a subject and receive the test signal in response to inputting the test magnetic resonance image into the out of distribution testing neural network (Fig. 1, Section 4.5).
With reference to claim 13, Zhang further teaches training data for the out of distribution testing neural network comprises simulated undersampled k-space data constructed from fully sampled k-space data and simulated test magnetic resonance images reconstructed from the simulated undersampled k-space data (Section 4.3).
With reference to claim 14, Zhang teaches A method of operating a medical system, wherein the method comprises: - receiving a test magnetic resonance image reconstructed from undersampled k-space data (see the loop in Fig 1, the evaluator receives an input at each iteration of the loop); - receiving a test signal in response to inputting the test magnetic resonance image into an out of distribution testing neural network (output of the evaluator network on Fig 12), wherein the test neural network is configured for outputting a test signal in response to receiving the test magnetic resonance image, wherein the test signal is descriptive if the test magnetic resonance image is within a training distribution defined by a set of training data (Section 4.3 "When training the reconstruction network, we will be using the evaluator as additional regularization to encourage the reconstructed image to have phantasized k-space rows that look as if they came from the distribution of true measured rows", "we leverage the idea of adversarial learning and train a discriminator-like evaluator to score the measurements and meanwhile encourage the reconstruction network to produce results that match the true measurement distribution"; see also the joint adversarial training of the evaluator network and the reconstruction network in sections 4.3 and 4.4 in relation with equations (3)-(6) and with Fig 3; see also Fig 12 showing the evaluator network and the reconstruction network); and
- providing the test signal (section 4.5, either for another iteration or for a stopping criterion).
With reference to claim 15, Zhang teaches A computer program comprising machine executable instructions for execution by a computational system, wherein execution of the machine executable instructions causes the computational system to:
- receive a test magnetic resonance image reconstructed from undersampled k-space data (see the loop in Fig 1, the evaluator receives an input at each iteration of the loop); - receive a test signal in response to causing an input of the test magnetic resonance image into an out of distribution testing neural network (output of the evaluator network on Fig 12), wherein the test neural network is configured for outputting the test signal in response to receiving the test magnetic resonance image, wherein the test signal is descriptive if the test magnetic resonance image is within a training distribution defined by a set of training data (Section 4.3 "When training the reconstruction network, we will be using the evaluator as additional regularization to encourage the reconstructed image to have phantasized k-space rows that look as if they came from the distribution of true measured rows", "we leverage the idea of adversarial learning and train a discriminator-like evaluator to score the measurements and meanwhile encourage the reconstruction network to produce results that match the true measurement distribution"; see also the joint adversarial training of the evaluator network and the reconstruction network in sections 4.3 and 4.4 in relation with equations (3)-(6) and with Fig 3; see also Fig 12 showing the evaluator network and the reconstruction network); and - provide (206) the test signal (section 4.5, either for another iteration or for a stopping criterion).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Duffy et al. (US 2023/0245314 A1) teach an automated medical image quality control system.
Maier-Hein et al. (US 2022/0012874 A1) teach a method and system for augmented imaging using multispectral information.
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/GREGORY H CURRAN/Primary Examiner, Art Unit 2852