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Accelerating Dynamic MRI Reconstruction Using Adaptive Sequentially Truncated Higher-Order Singular Value

Yang Li¹, Qiannan Shen¹, Mingfeng Jiang¹

¹School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, China.

Current Medical Imaging

|March 4, 2022

Summary

This summary is machine-generated.

This study introduces a faster method for reconstructing dynamic magnetic resonance imaging (dMRI) data. The new approach improves both the speed and quality of cardiac dMRI scans, enabling wider clinical use.

Keywords:

compressed sensing dMRI image reconstruction low-rank tensor space structure sequentially truncated HOSVD tensor decomposition

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Area of Science:

Medical Imaging
Biophysics
Computational Science

Background:

Dynamic magnetic resonance imaging (dMRI) is crucial for cardiac imaging but limited by slow data acquisition.
Accelerating dMRI is essential for expanding its clinical applications in perfusion and functional assessments.

Purpose of the Study:

To develop an efficient method for reconstructing dynamic magnetic resonance imaging (dMRI) data from highly undersampled k-space.
To transform the dMRI reconstruction into a low-rank and sparse tensor recovery problem.

Main Methods:

A novel sequentially truncated higher-order singular value decomposition (ST-HOSVD) method is proposed for approximating low-rank tensor structures.
Tensor kernel norm and l1 norm are used to constrain low-rank and sparse components, respectively.
Iterative soft-thresholding algorithm is employed to solve the optimization problem, reducing computational load.

Main Results:

The proposed method demonstrates superior performance in reconstruction speed and quality compared to state-of-the-art techniques.
Experimental results on 3D and 4D dMRI datasets validate the effectiveness of the approach.

Conclusions:

The tensor-based decomposition effectively represents multidimensional MRI time series.
The adaptive ST-HOSVD and l1-norm constrained sparse component recovery achieve superior reconstruction performance and efficiency for dMRI.