US-12625213-B2 - System and method for detecting motion-ridden shots in multi-shot acquisitions and utilizing deep learning based reconstruction for motion correction

Abstract

A method includes obtaining k-space data, wherein a plurality of navigator like echoes of the k-space data including an additional navigator like echo are acquired for each shot or a group of shots. The k-space data is motion corrupted. The method includes identifying any shots where a subject moved during acquisition based on the respective additional navigator like echoes. The method includes generating dominant pose k-space data based on identification of any shots where the subject moved during acquisition, the dominant pose k-space data includes only shots not affected by movement, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved. The method includes utilizing a deep learning-based reconstruction model on the motion-corrupted k-space data to modify motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image.

Inventors

• Megha Goel
• Sudhanya Chatterjee
• Sajith Rajamani
• Sudhir Ramanna
• Preetham Shankpal
• Imam Ahmed SHAIK
• Suresh Emmanuel Devadoss Joel
• Florintina Chaarlas
• Harsh Kumar Agarwal

Assignees

• GE Precision Healthcare LLC

Dates

Publication Date

20260512

Application Date

20240408

Claims (14)

1. 1 . A computer-implemented method, comprising: obtaining, via a processor, k-space data of a subject acquired with a magnetic resonance imaging (MRI) scanner, wherein a portion of the k-space data is motion corrupted, and wherein a plurality of navigator like echoes of the k-space data comprising an additional navigator like echo are acquired for each shot or a group of shots of a plurality of shots for a respective slice; identifying, via the processor, any shots of the plurality of shots where the subject moved during acquisition based on respective additional navigator like echoes for the plurality of shots; generating, via the processor, dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data comprising only shots of the plurality of shots not affected by movement of the subject during acquisition, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved during acquisition; and utilizing, via the processor, a deep learning-based reconstruction model on the k-space data to modify the portion of the motion-corrupted k-space data that is motion corrupted with other k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image from the k-space data, wherein utilizing the deep learning-based reconstruction model on the k-space data comprises: transforming the portion of the k-space data that is motion corrupted to a motion-corrupted image; transforming centrally located phase encoded lines from the k-space data into a contrast image, wherein the k-space data is derived from both a first set of shots where the subject moved during acquisition and a second set of shots not affected by movement of the subject during acquisition; inputting both the motion-corrupted image and the contrast image into the deep learning-based reconstruction model, wherein the dominant pose k-space data is utilized for application of soft data consistency in an unrolled framework; and outputting from the deep learning-based reconstruction model the reconstructed image.
2. 2 . The computer-implemented method of claim 1 , wherein the additional navigator like echo is acquired at a start, a middle, or an end of each shot of the plurality of shots for the respective slice where the additional navigator like echo is acquired.
3. 3 . The computer-implemented method of claim 1 , further comprising comparing the respective additional navigator like echoes for the plurality of shots to identify a subset of shots of the plurality of shots that corresponds to a dominant pose of the subject and to identify any shots of the plurality of shots where the subject moved during acquisition.
4. 4 . The computer-implemented method of claim 3 , wherein comparing the respective additional navigator like echoes comprises clustering the respective additional navigator like echoes to obtain positioning information to predict any shot where the subject moved during acquisition.
5. 5 . The computer-implemented method of claim 1 , further comprising: generating, via the processor, a k-space mask from the k-space data utilizing only the shots not affected by movement of the subject during acquisition, wherein the k-space mask is configured to mask the shots where the subject moved during acquisition; and applying, via the processor, the k-space mask to the k-space data to generate the dominant pose k-space data.
6. 6 . A system, comprising: a memory encoding processor-executable routines; and a processor configured to access the memory and to execute the processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to: obtain k-space data of a subject acquired with a magnetic resonance imaging (MRI) scanner, wherein a portion of the k-space data is motion corrupted, and wherein a plurality of navigator like echoes of the k-space data comprising an additional navigator like echo are acquired for each shot or a group of shots of a plurality of shots for a respective slice; identify any shots of the plurality of shots where the subject moved during acquisition based on respective additional navigator like echoes for the plurality of shots; generate dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data comprising only shots of the plurality of shots not affected by movement of the subject during acquisition, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved during acquisition; and utilize a deep learning-based reconstruction model on the k-space data to modify the portion of the k-space data that is motion corrupted with other k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image from the k-space data, wherein utilizing the deep learning-based reconstruction model on the k-space data comprises: transforming the portion of k-space data that is motion corrupted to a motion-corrupted image; transforming centrally located phase encoded lines from the k-space data into a contrast image, wherein the k-space data is derived from both a first set of shots where the subject moved during acquisition and a second set of shots not affected by movement of the subject during acquisition; inputting both the motion-corrupted image and the contrast image into the deep learning-based reconstruction model, wherein the dominant pose k-space data is utilized for application of soft data consistency in an unrolled framework; and outputting from the deep learning-based reconstruction model the reconstructed image.
7. 7 . The system of claim 6 , wherein the additional navigator like echo is acquired at a start, a middle, or an end of each shot of the plurality of shots for the respective slice where the additional navigator like echo is acquired.
8. 8 . The system of claim 6 , wherein the processor-executable routines, when executed by the processor, further cause the processor to compare the respective additional navigator like echoes for the plurality of shots to identify a subset of shots of the plurality of shots that corresponds to a dominant pose of the subject and to identify any shots of the plurality of shots where the subject moved during acquisition.
9. 9 . The system of claim 8 , wherein comparing the respective additional navigator like echoes comprises clustering the respective additional navigator like echoes to obtain positioning information to predict any shot where the subject moved during acquisition.
10. 10 . The system of claim 6 , wherein the processor-executable routines, when executed by the processor, further cause the processor to: generate a k-space mask from the k-space data utilizing only the shots not affected by movement of the subject during acquisition, wherein the k-space mask is configured to mask the shots where the subject moved during acquisition; and apply the k-space mask to the k-space data to generate the dominant pose k-space data.
11. 11 . A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to: obtain k-space data of a subject acquired with a magnetic resonance imaging (MRI) scanner, wherein a portion of the k-space data is motion corrupted, and wherein a plurality of navigator like echoes of the k-space data comprising an additional navigator like echo are acquired for each shot or a group of shots of a plurality of shots for a respective slice; identify any shots of the plurality of shots where the subject moved during acquisition based on respective additional navigator like echoes for the plurality of shots; generate dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data comprises only shots of the plurality of shots not affected by movement of the subject during acquisition, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved during acquisition; and utilize a deep learning-based reconstruction model on the k-space data to modify the portion of the k-space data that is motion corrupted with other k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image from the k-space data, wherein utilizing the deep learning-based reconstruction model on the k-space data comprises: transforming the portion of the k-space data that is motion corrupted to a motion-corrupted image; transforming centrally located phase encoded lines from the k-space data into a contrast image, wherein the k-space data is derived from both a first set of shots where the subject moved during acquisition and a second set of shots not affected by movement of the subject during acquisition; inputting both the motion-corrupted image and the contrast image into the deep learning-based reconstruction model, wherein the dominant pose k-space data is utilized for application of soft data consistency in an unrolled framework; and outputting from the deep learning-based reconstruction model the reconstructed image.
12. 12 . The non-transitory computer-readable medium of claim 11 , wherein the additional navigator like echo is acquired at a start, a middle, or an end of each shot of the plurality of shots for the respective slice where the additional navigator like echo is acquired.
13. 13 . The non-transitory computer-readable medium of claim 11 , wherein the processor-executable code, when executed by the processor, further cause the processor to compare the respective additional navigator like echoes for the plurality of shots to identify a subset of shots of the plurality of shots that corresponds to a dominant pose of the subject and to identify any shots of the plurality of shots where the subject moved during acquisition.
14. 14 . The non-transitory computer-readable medium of claim 11 , wherein the processor-executable code, when executed by the processor, further cause the processor to: generate a k-space mask from the k-space data utilizing only the shots not affected by movement of the subject during acquisition, wherein the k-space mask is configured to mask the shots where the subject moved during acquisition; and apply the k-space mask to the k-space data to generate the dominant pose k-space data.

Description

BACKGROUND The subject matter disclosed herein relates to medical imaging and, more particularly, to a system and a method for detecting motion-ridden shots in multi-shot acquisitions and utilizing deep learning-based reconstruction for motion correction. Non-invasive imaging technologies allow images of the internal structures or features of a patient/object to be obtained without performing an invasive procedure on the patient/object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through a target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient/object. During magnetic resonance imaging (MRI), when a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment, Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated and this signal may be received and processed to form an image. When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradient fields vary according to the particular localization method being used. The resulting set of received nuclear magnetic resonance (NMR) signals are digitized and processed to reconstruct the image using one of many well-known reconstruction techniques. Since MRI acquisitions are relatively long (e.g. greater than one minute), there is a chance that patient movement may occur during the scan. This caused ghosting or blurring-like artifacts to manifest in the reconstructed image, which makes the image non-diagnostic. While patients are trained before scanning to be still, motion may still occur for patients (e.g., patients who are in pain). BRIEF DESCRIPTION A summary of certain embodiments disclosed herein is set forth below. It should be understood that these aspects are presented merely to provide the reader with a brief summary of these certain embodiments and that these aspects are not intended to limit the scope of this disclosure. Indeed, this disclosure may encompass a variety of aspects that may not be set forth below. In one embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, via a processor, k-space data of a subject with a magnetic resonance imaging (MRI) scanner acquired, wherein the k-space data is motion corrupted, and wherein a plurality of navigator like echoes of the k-space data including an additional navigator like echo are acquired for each shot or a group of shots of a plurality of shots for a respective slice. The computer-implemented method also includes identifying, via the processor, any shots of the plurality of shots where the subject moved during acquisition based on the respective additional navigator like echoes for the plurality of shots. The computer-implemented method further includes generating, via the processor, dominant pose k-space data based on identification of any shots of the plurality of shots where the subject moved during acquisition, the dominant pose k-space data includes only shots of the plurality of shots not affected by movement of the subject during acquisition, wherein the dominant pose k-space data is missing k-space data due to rejecting the shots where the subject moved during acquisition. The computer-implemented method even further includes utilizing, via the processor, a deep learning-based reconstruction model on motion-corrupted k-space data to modify the motion-corrupted k-space data with k-space data that is consistent with the dominant pose k-space data to generate a reconstructed image. In another embodiment, a system is provided. The system includes a memory encoding processor-executable routines. The system also includes a processor configured to access the memory and to execute the processor-executable routines, wherein the process-executable routines, when executed by the processor, cause the processor to perform actions. The actions include obtaining k-space data of a subject acquired with a magnetic resonance imaging (MRI) scanner, wherein the k-space data is motion corrupted, and wherein a plurality