Dataset Information

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

ABSTRACT:

Purpose

To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.

Theory and methods

Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HICU), provides fast, memory-efficient recovery of unsampled k-space points. For demonstration, HICU is applied to 6 2D T2-weighted brain, 7 2D cardiac cine, 5 3D knee, and 1 multi-shot diffusion weighted imaging (MSDWI) datasets.

Results

The 2D imaging results show that HICU can offer 1-2 orders of magnitude computation speedup compared to other Cl-MRI methods without sacrificing imaging quality. The 2D cine and 3D imaging results show that the computational acceleration techniques included in HICU yield computing time on par with SENSE-based compressed sensing methods with up to 3 dB improvement in signal-to-error ratio and better perceptual quality. The MSDWI results demonstrate the feasibility of HICU for a challenging multi-shot echo-planar imaging application.

Conclusions

The presented method, HICU, offers efficient computation and scalability as well as extendibility to a wide variety of MRI applications.

SUBMITTER: Zhao S

PROVIDER: S-EPMC8184615 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Publications

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

Zhao Shen S Potter Lee C LC Ahmad Rizwan R

Magnetic resonance in medicine 20210404 3

<h4>Purpose</h4>To present a computational procedure for accelerated, calibrationless magnetic resonance image (Cl-MRI) reconstruction that is fast, memory efficient, and scales to high-dimensional imaging.<h4>Theory and methods</h4>Cl-MRI methods can enable high acceleration rates and flexible sampling patterns, but their clinical application is limited by computational complexity and large memory footprint. The proposed computational procedure, HIgh-dimensional fast convolutional framework (HI ...[more]

PMID: 33817823

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Dataset Information

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

Purpose

Theory and methods

Results

Conclusions

Publications

High-dimensional fast convolutional framework (HICU) for calibrationless MRI.

Similar Datasets

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