# US 11,079,454 B2 · Systems and methods for reconstruction of dynamic magnetic resonance imaging data

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- Number: US 11,079,454 B2
- Document ID: US11079454B2
- Status: Granted
- Kind: B2
- Application number: 16/633,412
- Filed: 2018-07-25
- Published: 2020-11-19
- Granted: 2021-08-03
- Earliest claimed: 2017-07-25
- Assignees: Nova Scotia Health Authority
- Applicants: Nova Scotia Health Authority
- Inventors: James Rioux; Nathan Murtha; Steven Beyea
- Web page: https://patentgrep.com/patent/US11079454B2

## CPC

- G01R 33/4824: {using a non-Cartesian trajectory}
- G01R 33/56308: {Characterization of motion or flow; Dynamic imaging}
- A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
- G01R 33/543: {Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription (G01R33/546 takes precedence)}
- 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 33/5611: {Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE (structural details of arrays of sub-coils G01R33/3415)}
- G01R 33/56325: {Cine imaging}
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.

## Claims

### Claim 1

A method of performing dynamic magnetic resonance imaging, the method comprising:
a) controlling a magnetic resonance imaging scanner to generate a sequence of RF pulses and detect RF signals that are responsively emitted by a subject positioned with a bore of the magnetic resonance imaging scanner, thereby obtaining an input dataset, wherein the sequence of RF pulses is selected to facilitate image reconstruction according to a plurality of temporal resolutions;
b) partitioning the input dataset to generate an image series of non-reconstructed image data, the image series having a selected temporal resolution;
c) selecting an image subset from the image series;
d) performing image reconstruction on the image subset, thereby obtaining a reconstructed image subset;
e) processing the image subset to determine one or more image quality metrics;
f) comparing the image quality metrics to pre-selected criteria associated with an estimated fidelity of a full reconstruction of the input dataset;
g) in the event that the one or more image quality metrics do not satisfy the pre-selected criteria, adjusting the temporal resolution and repeating operations b) to f); and
h) in the event that the one or more image quality metrics satisfy the pre-selected criteria, employing the current temporal resolution to perform full reconstruction on the input dataset.

### Claim 2 (depends on 1)

The method according to claim 1 further comprising communicating a measure associated with an estimated temporal fidelity of the full reconstruction of the input dataset.

### Claim 3 (depends on 1)

The method according to claim 1 wherein the input dataset is an undersampled image dataset, and such that reconstruction is performed according to a compressed sensing method.

### Claim 4 (depends on 1)

The method according to claim 1 wherein at least one image quality metric is based on a Structural Similarity index.

### Claim 5 (depends on 1)

The method according to claim 1 wherein one or more of the image quality metrics is generated based on a comparison with a reference image.

### Claim 6 (depends on 5)

The method according to claim 5 wherein the reference image is generated by collapsing temporal frames of the input dataset into a single image.

### Claim 7 (depends on 1)

The method according to claim 1 wherein when the one or more image quality metrics satisfy the pre-selected criteria, the following steps are performed one or more times in order to improve one or more of temporal fidelity and image quality prior to employing the current temporal resolution to perform full reconstruction on the input dataset:
i) varying one or more reconstruction parameters;
j) performing image reconstruction on the image subset, thereby obtaining the reconstructed image subset;
k) processing the image subset to determine the one or more image quality metrics; and
l) comparing the image quality metrics to the pre-selected criteria.

### Claim 8 (depends on 1)

The method according to claim 1 wherein when the one or more image quality metrics do not satisfy the pre-selected criteria, the following operations are performed one or more times in order to improve one or more of temporal fidelity and image quality prior to adjusting the temporal resolution and repeating operations b) to f):
i) varying one or more reconstruction parameters;
j) performing image reconstruction on the image subset, thereby obtaining the reconstructed image subset;
k) processing the image subset to determine the one or more image quality metrics; and
l) comparing the image quality metrics to the pre-selected criteria.

### Claim 9

A magnetic resonance imaging system comprising:
a magnetic resonance imaging scanner; and
control and processing hardware operatively coupled to said magnetic resonance imaging scanner, wherein said control and processing hardware comprises memory coupled with one or more processors to store instructions, which when executed by the one or more processors, causes the one or more processors to perform operations comprising:
a) controlling said magnetic resonance imaging scanner to generate a sequence of RF pulses and detect RF signals that are responsively emitted by a subject positioned with a bore of said magnetic resonance imaging scanner, thereby obtaining an input dataset, wherein the sequence of RF pulses is selected to facilitate image reconstruction according to a plurality of temporal resolutions;
b) partitioning the input dataset to generate an image series of non-reconstructed image data, the image series having a selected temporal resolution;
c) selecting an image subset from the image series;
d) performing image reconstruction on the image subset, thereby obtaining a reconstructed image subset;
e) processing the image subset to determine one or more image quality metrics;
f) comparing the image quality metrics to pre-selected criteria associated with an estimated fidelity of a full reconstruction of the input dataset;
g) in the event that the one or more image quality metrics do not satisfy the pre-selected criteria, adjusting the temporal resolution and repeating operations b) to f); and
h) in the event that the one or more image quality metrics satisfy the pre-selected criteria, employing the current temporal resolution to perform full reconstruction on the input dataset.

### Claim 10 (depends on 9)

The system according to claim 9 wherein said control and processing hardware is configured to communicate a measure associated with an estimated temporal fidelity of the full reconstruction of the input dataset.

### Claim 11 (depends on 10)

The system according to claim 10 wherein said control and processing hardware is configured such that when the one or more image quality metrics satisfy the pre-selected criteria, the following operations are performed one or more times in order to improve one or more of temporal fidelity and image quality prior to employing the current temporal resolution to perform full reconstruction on the input dataset:
i) varying one or more reconstruction parameters;
j) performing image reconstruction on the image subset, thereby obtaining the reconstructed image subset;
k) processing the image subset to determine the one or more image quality metrics; and
l) comparing the image quality metrics to the pre-selected criteria.

### Claim 12 (depends on 10)

The system according to claim 10 wherein said control and processing hardware is configured such that when the one or more image quality metrics do not satisfy the pre-selected criteria, the following operations are performed one or more times in order to improve one or more of temporal fidelity and image quality prior to adjusting the temporal resolution and repeating operations b) to f):
i) varying one or more reconstruction parameters;
j) performing image reconstruction on the image subset, thereby obtaining the reconstructed image subset;
k) processing the image subset to determine the one or more image quality metrics; and
l) comparing the image quality metrics to the pre-selected criteria.

### Claim 13 (depends on 9)

The system according to claim 9 wherein said control and processing hardware is configured such that the input dataset is an undersampled image dataset, and such that reconstruction is performed according to a compressed sensing method.

### Claim 14 (depends on 9)

The system according to claim 9 wherein said control and processing hardware is configured such that at least one image quality metric is based on a Structural Similarity index.

### Claim 15 (depends on 9)

The system according to claim 9 wherein said control and processing hardware is configured such that one or more of said image quality metrics is generated based on a comparison with a reference image.

### Claim 16 (depends on 15)

The system according to claim 15 wherein said control and processing hardware is configured such that said reference image is generated by collapsing temporal frames of the input dataset into a single image.

### Claim 17

A method of performing dynamic magnetic resonance imaging, the method comprising:
a) controlling a magnetic resonance imaging scanner to generate a sequence of RF pulses and detect RF signals that are responsively emitted by a subject positioned with a bore of the magnetic resonance imaging scanner, thereby obtaining an input dataset, wherein the sequence of RF pulses is selected to facilitate image reconstruction according to a plurality of temporal resolutions;
b) partitioning the input dataset to generate an image series of non-reconstructed image data, the image series having a selected temporal resolution;
c) performing image reconstruction on the image series, thereby obtaining a set of reconstructed image frames;
d) selecting an image subset of reconstructed image frames from the set of reconstructed image frames;
e) processing the image subset to determine one or more image quality metrics;
f) comparing the image quality metrics to pre-selected criteria associated with an estimated fidelity of a full reconstruction of the input dataset;
g) in the event that the one or more image quality metrics do not satisfy the pre-selected criteria, adjusting the temporal resolution and repeating operations b) to f); and
h) in the event that the one or more image quality metrics satisfy the pre-selected criteria, employing the current temporal resolution to perform full reconstruction on the input dataset.

### Claim 18

A magnetic resonance imaging system comprising:
a magnetic resonance imaging scanner; and
control and processing hardware operatively coupled to said magnetic resonance imaging scanner, wherein said control and processing hardware comprises memory coupled with one or more processors to store instructions, which when executed by the one or more processors, causes the one or more processors to perform operations comprising:
a) controlling said magnetic resonance imaging scanner to generate a sequence of RF pulses and detect RF signals that are responsively emitted by a subject positioned with a bore of said magnetic resonance imaging scanner, thereby obtaining an input dataset, wherein the sequence of RF pulses is selected to facilitate image reconstruction according to a plurality of temporal resolutions;
b) partitioning the input dataset to generate an image series of non-reconstructed image data, the image series having a selected temporal resolution;
c) performing image reconstruction on the image series, thereby obtaining a set of reconstructed image frames;
d) selecting an image subset of reconstructed image frames from the set of reconstructed image frames;
e) processing the image subset to determine one or more image quality metrics;
f) comparing the image quality metrics to pre-selected criteria associated with an estimated fidelity of a full reconstruction of the input dataset;
g) in the event that the one or more image quality metrics do not satisfy the pre-selected criteria, adjusting the temporal resolution and repeating operations b) to f); and
h) in the event that the one or more image quality metrics satisfy the pre-selected criteria, employing the current temporal resolution to perform full reconstruction on the input dataset.