US-12625215-B2 - Compressed sensing using different k-space sampling patterns

US12625215B2US 12625215 B2US12625215 B2US 12625215B2US-12625215-B2

Abstract

A system and method comprises acquisition of a plurality of k-space sets, each of the plurality of k-space sets comprising a different incoherent variable-density under-sampled combination of k-space data points, performance of iterative reconstruction on the plurality of k-space sets to generate a plurality of images, where each of the plurality of images is associated with a different one of the plurality of k-space sets, and averaging of the generated plurality of images to generate an image.

Inventors

Wolfgang REHWALD
Jianing Pang

Assignees

Siemens Healthineers Ag

Dates

Publication Date: 20260512
Application Date: 20240613

Claims (20)

1 . A magnetic resonance imaging system comprising: a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject; a plurality of gradient coils configured to apply at least one gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an excitation field to the subject and to acquire magnetic resonance (MR) data from the subject; and a processing unit to execute program code to cause the system to: acquire a plurality of k-space sets, each of the plurality of k-space sets comprising a different incoherent variable-density under-sampled combination of k-space data points; perform iterative reconstruction on the plurality of k-space sets to generate a plurality of images, where each of the plurality of images is associated with a different one of the plurality of k-space sets; and generate an image based on the generated plurality of images.
2 . The system of claim 1 , wherein the iterative reconstruction comprises a compressed sensing reconstruction.
3 . The system of claim 2 , wherein the iterative reconstruction comprises a joint reconstruction based on a joint sparsity of the plurality of k-space sets.
4 . The system of claim 3 , wherein the plurality of images comprise complex pixel values, and wherein generation of the image comprises averaging the plurality of images.
5 . The system of claim 4 , the processing unit to execute program code to cause the system to: motion-correct each of the plurality of images to a same reference image before generation of the image.
6 . The system of claim 1 , wherein the plurality of images comprise complex pixel values, and wherein generation of the image comprises averaging the plurality of images.
7 . The system of claim 6 , the processing unit to execute program code to cause the system to: motion-correct each of the plurality of images to a same reference image before generation of the image.
8 . The system of claim 1 , wherein each of the plurality of k-space data segments is acquired using a different inversion time.
9 . The system of claim 1 , wherein acquisition of the plurality of k-space sets comprises acquisition of each of the plurality of k-space sets at a predetermined time after an inversion pulse, wherein the iterative reconstruction comprises a joint reconstruction based on a joint sparsity of the plurality of k-space sets, and wherein generation of the image comprises averaging the plurality of images; the processing unit to execute program code to cause the system to: acquire a second plurality of k-space sets interleaved with acquisition of the plurality of k-space sets without a leading inversion pulse; perform a second joint reconstruction based on a second joint sparsity of the second plurality of k-space sets to generate a second plurality of images, where each of the second plurality of images is associated with a different one of the second plurality of k-space sets; generate a second image by averaging the second plurality of images; and perform a phase-sensitive reconstruction based on the image and the second image to generate a phase-sensitive inversion recovery image.
10 . A method comprising: acquiring a plurality of k-space sets, each of the plurality of k-space sets comprising a different incoherent variable-density under-sampled combination of k-space data points; performing iterative reconstruction on the plurality of k-space sets to generate a plurality of images, where each of the plurality of images is associated with a different one of the plurality of k-space sets; and combining the generated plurality of images to generate an image.
11 . The method of claim 10 , wherein the iterative reconstruction comprises a joint compressed sensing reconstruction based on a joint sparsity in image space of the plurality of k-space sets.
12 . The method of claim 10 , wherein the plurality of images comprise complex pixel values, and wherein combining the generated plurality of images comprises averaging the plurality of images.
13 . The method of claim 12 , further comprising: motion-correcting each of the plurality of images to a same reference image before generation of the image.
14 . The method of claim 10 , wherein each of the plurality of k-space sets is acquired using a different inversion time.
15 . The method of claim 10 , wherein acquiring the plurality of k-space sets comprises acquiring each of the plurality of k-space sets at a predetermined time after an inversion pulse, wherein the iterative reconstruction comprises a joint reconstruction based on a joint sparsity of the plurality of k-space sets, and wherein combining the generated plurality of images comprises averaging the plurality of images; the method further comprising: acquiring a second plurality of k-space sets interleaved with acquisition of the plurality of k-space sets without a leading inversion pulse; performing a second joint reconstruction based on a second joint sparsity of the second plurality of k-space sets to generate a second plurality of images, where each of the second plurality of images is associated with a different one of the second plurality of k-space sets; generating a second image by averaging the second plurality of images; and performing a phase-sensitive reconstruction based on the image and the second image to generate a phase-sensitive inversion recovery image.
16 . One or more non-transitory computer-readable media storing program code executable by one or more processing units to cause a computing system to: acquire a plurality of k-space sets, each of the plurality of k-space sets comprising a different incoherent variable-density under-sampled combination of k-space data points; perform iterative reconstruction on the plurality of k-space sets to generate a plurality of images, where each of the plurality of images is associated with a different one of the plurality of k-space sets; and averaging the generated plurality of images to generate an image.
17 . The one or more non-transitory computer-readable media of claim 16 , wherein the iterative reconstruction comprises a joint reconstruction based on a joint sparsity of the plurality of k-space sets.
18 . The one or more non-transitory computer-readable media of claim 16 , wherein acquisition of the plurality of k-space sets comprises acquisition of each of the plurality of k-space sets at a predetermined time after an inversion pulse, and wherein the iterative reconstruction comprises a joint reconstruction based on a joint sparsity of the plurality of k-space sets; the program code executable by one or more processing units to cause a computing system to: acquire a second plurality of k-space sets interleaved with acquisition of the plurality of k-space sets without a leading inversion pulse; perform a second joint reconstruction based on a second joint sparsity of the second plurality of k-space sets to generate a second plurality of images, where each of the second plurality of images is associated with a different one of the second plurality of k-space sets; average the second plurality of images to generate a second image; and perform a phase-sensitive reconstruction based on the image and the second image to generate a phase-sensitive inversion recovery image.
19 . The one or more non-transitory computer-readable media of claim 16 , the program code executable by one or more processing units to cause a computing system to: motion-correct each of the plurality of images to a same reference image before generation of the image.
20 . The one or more non-transitory computer-readable media of claim 16 , wherein each of the plurality of k-space sets is acquired using a different inversion time.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63/605,676, filed Dec. 4, 2023, the disclosure of which is incorporated herein by reference for all purposes. BACKGROUND A Magnetic Resonance (MR) scanner generates images of patient anatomy using timed sequences of radio-frequency (RF) pulses. MR imaging is useful in scenarios requiring high contrast between different soft tissues. MR imaging consists of the acquiring k-space data and calculating an image from the k-space data using a two-dimensional Fast Fourier Transform (FFT). Different regions of k-space represent different image properties. For example, the center region of k-space contains image brightness and contrast information, and the edges of k-space contain image sharpness and detail information. A low-resolution image may therefore be calculated using only k-space data from the center region of k-space. A high-resolution image typically requires k-space data from all of k-space. In segmented MR imaging, an image is reconstructed from k-space data acquired in multiple shots. Each shot acquires a different portion of k-space, for example m lines of an n-line k-space. Typically, each shot is taken with precise temporal resolution (i.e., in the same cardiac phase but in a different heartbeat). Acquisition of all k-space lines therefore occurs over n heartbeats. According to a Late Gadolinium Enhancement (LGE) sequence, a T1-shortening Gadolinium-based contrast agent is injected into a patient, and an inversion recovery (IR)-prepared sequence is performed. When used for cardiac imaging, for example, the LGE sequence produces T1-weighted images in which viable (i.e., healthy, living) myocardium appears dark and infarcted (i.e., dead) myocardium appears bright. The amount of data needed to create an LGE image with high spatial and temporal resolution is too large to be acquired in a single shot. Therefore, the data is acquired in segments over multiple heartbeats while the patient holds their breath. Such breath-held, high-spatial resolution segmented LGE images are susceptible to ghosting artifacts. Ghosting artifacts are fake, periodic replications of moving structures, for example the myocardial wall and the interface between chest fat and surrounding air. Ghosting artifacts can be caused by small motions resulting from imperfect breath holding, arrhythmia, or poor ECG-triggering. Ghosting artifacts are naturally absent from single-shot images since artificial frequencies cannot be created without segmentation. However, even at similar spatial resolutions, a single-shot image exhibits a lower signal-to-noise ratio (SNR), poorer temporal resolution, and reduced T1-contrast as compared to an image resulting from a segmented acquisition. Systems are desired to efficiently generate high-quality images using single-shot k-space data acquisition. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates acquisition of multiple different incoherent distributions of k-space data and generation of an image therefrom according to some embodiments. FIG. 2 is a flow diagram of a process to generate an image from multiple different incoherent distributions of k-space data according to some embodiments. FIG. 3 illustrates different incoherent distributions of k-space data acquired in respective single shots according to some embodiments. FIG. 4 illustrates a pulse sequence to acquire different incoherent distributions of k-space data in respective single shots according to some embodiments. FIG. 5a illustrates generation of images from different incoherent distributions of k-space data according to some embodiments. FIG. 5b illustrates generation of images from different incoherent distributions of k-space data using joint reconstruction according to some embodiments. FIG. 6 illustrates images reconstructed from different incoherent distributions of k-space data and a combined image generated therefrom according to some embodiments. FIG. 7 is a block diagram of an example MR system for use in some embodiments. DETAILED DESCRIPTION The following description is provided to enable any person in the art to make and use the described embodiments. Various modifications will remain apparent to those in the art. Embodiments provide a Compressed Sensing (CS) single-shot method where each of multiple single shots is acquired with a different k-space sampling pattern. Images reconstructed from each single shot therefore exhibit artifacts which are unique to each image. The images may then be combined (e.g., averaged) to generate an image exhibiting substantially reduced, or no, artifacts. Embodiments may therefore provide improved image quality in the presence of motion and flow over segmented IR-prepared sequences. Moreover, the acquisition of multiple single shots of the same region of interest enables joint CS reconstruction of the images. Joint CS reconstruction exploits inter-image sparsity in addition to i