# US 12,761,302 B2 · Single coil deep learning based magnetic resonance imaging system and method

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- Number: US 12,761,302 B2
- Document ID: US12761302B2
- Status: Granted
- Kind: B2
- Application number: 18/665,381
- Filed: 2024-05-15
- Published: 2024-11-21
- Granted: 2026-10-13
- Earliest claimed: 2023-05-16
- Assignees: GE Precision Healthcare LLC
- Applicants: GE Precision Healthcare LLC
- Inventors: Sudhanya Chatterjee; Harsh Kumar Agarwal; Florintina C; Rohan Keshav Patil; Suresh Emmanuel Devadoss Joel; Sajith Rajamani
- Web page: https://patentgrep.com/patent/US12761302B2

## CPC

- G01R 33/4818: {MR characterised by data acquisition along a specific k-space trajectory or by the temporal order of k-space coverage, e.g. centric or segmented coverage of k-space}
- G01R 33/385: using gradient magnetic field coils
- G01R 33/5608: {Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T)}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)

## Abstract

A method for magnetic resonance imaging (MRI) includes determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject. The method also includes acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor. The image of the subject is reconstructed by processing the set of k-space data using a deep learning (DL) network.

## Claims

### Claim 1

A method for magnetic resonance imaging (MRI) comprising:
determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject;
acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor; and
reconstructing an image of the subject by processing the set of k-space data using a deep learning (DL) network.

### Claim 2

A magnetic resonance imaging (MRI) system, comprising:
a magnet configured to generate a polarizing magnetic field about at least a portion of a subject arranged in the MRI system;
a gradient coil assembly including a readout gradient coil, a phase gradient coil, a slice selection gradient coil configured to apply at least one gradient field to the polarizing magnetic field;
a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject; and
a processing system programmed to:
determine a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from the subject;
acquire a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor; and
reconstruct an image of the subject by processing the set of k-space data using a deep learning (DL) network.

### Claim 3

A non-transitory computer-readable medium comprising instructions, which when executed by a computer, cause the computer to carry out a method for Single Shot Fast Spin Echo (SSFSE) T2 magnetic resonance imaging, the method comprising the steps of:
determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject;
acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor; and
reconstructing an image of the subject by processing the set of k-space data using a deep learning (DL) network.

### Claim 4 (depends on 1)

The method of claim 1, wherein the PF factor is determined based on echo spacing.

### Claim 5 (depends on 1)

The method of claim 1, wherein acquiring the set of k-space data from the subject using the PF factor along with the under-sampling technique comprises marking a portion of a k-space based on the PF factor and then a sub-portion in that portion is acquired using an under-sampling mask.

### Claim 6 (depends on 1)

The method of claim 1, wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels.

### Claim 7 (depends on 1)

The method of claim 1, wherein the DL network is based on an unrolled algorithm based deep learning reconstruction.

### Claim 8 (depends on 1)

The method of claim 1, wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM).

### Claim 9 (depends on 1)

The method of claim 1, wherein acquiring the set of k-space data comprises acquiring the set of k-space data using only a single radio frequency coil.

### Claim 10 (depends on 2)

The MRI system of claim 2, wherein the PF factor is determined based on echo spacing.

### Claim 11 (depends on 2)

The MRI system of claim 2, wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels.

### Claim 12 (depends on 2)

The MRI system of claim 2, wherein the DL network is based on an unrolled algorithm based deep learning reconstruction.

### Claim 13 (depends on 2)

The MRI system of claim 2, wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM).

### Claim 14 (depends on 3)

The non-transitory computer-readable medium of claim 3, wherein the instructions for acquiring the set of k-space data from the subject using the PF factor along with the under-sampling technique comprises instructions for marking a portion of a k-space based on the PF factor and then a sub-portion in that portion is acquired using an under-sampling mask.

### Claim 15 (depends on 3)

The non-transitory computer-readable medium of claim 3, wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels.

### Claim 16 (depends on 6)

The method of claim 6, wherein the DL network is trained using following steps:
randomly selecting a preliminary PF factor from the range of partial Fourier factors and a desired acceleration factor from the range of acceleration levels;
generating a preliminary PF mask based on the preliminary PF factor;
determining a preliminary acceleration factor based on the preliminary PF mask; and
dropping additional k-space lines in an alternating pattern from a periphery of the preliminary PF mask until the desired acceleration factor is reached if the preliminary acceleration factor does not satisfy the desired acceleration factor.

### Claim 17 (depends on 8)

The method of claim 8, wherein the weighted loss function is represented as:
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where £(I, Î) is the weighted loss function showing error between actual input data I and the predicted output data Î, and α, β represent weighting factors.

### Claim 18 (depends on 11)

The MRI system of claim 11, wherein the processing system is programmed to train the DL network using following steps:
randomly selecting a preliminary PF factor from the range of partial Fourier factors and a desired acceleration factor from the range of acceleration levels;
generating a preliminary PF mask based on the preliminary PF factor;
determining a preliminary acceleration factor based on the preliminary PF mask; and
dropping additional k-space lines in an alternating pattern from a periphery of the preliminary PF mask until the desired acceleration factor is reached if the preliminary acceleration factor does not satisfy the desired acceleration factor.

### Claim 19 (depends on 13)

The MRI system of claim 13, wherein the weighted loss function is represented as:
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where £(I, Î) is the weighted loss function showing error between actual input data I and the predicted output data Î, and α, β represent weighting factors.

### Claim 20 (depends on 19)

The MRI system of claim 19, wherein the processing system is programmed to acquire the set of k-space data using only a single radio frequency coil.