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Related Experiment Video

Updated: Jul 16, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Joint Reconstruction of Multiple b-Values and Multiple Directions for Accelerating Diffusion Kurtosis Imaging.

Jian Lyu¹, Li Guo², Wen Zhong³

¹Department of the Radiation Oncology Physics, Foshan Key Laboratory of Precision Therapy in Oncology and Neurology, The First People's Hospital of Foshan (The Affiliated Foshan Hospital of Southern University of Science and Technology), School of Medicine, Southern University of Science and Technology, Foshan, Guangdong, China.

Magnetic Resonance in Medicine

|July 14, 2026

Summary

This study introduces a model-based reconstruction method to accelerate diffusion kurtosis imaging (DKI) acquisition. The new technique significantly improves accuracy and stability for DKI parameter estimation, enabling faster, higher-resolution scans.

Keywords:

diffusion kurtosis imaging interleaved EPI DWI model‐based reconstruction physically relevant constraint total variation

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Area of Science:

Medical Imaging
Diffusion MRI
Quantitative Imaging

Background:

Multishot interleaved echo-planar imaging (iEPI) offers higher resolution for diffusion kurtosis imaging (DKI) than single-shot EPI.
Clinical use of iEPI DKI is limited by its long acquisition times.

Purpose of the Study:

To develop and validate a novel model-based reconstruction approach for accelerating iEPI DKI acquisition.
To improve the temporal efficiency of high-resolution DKI while maintaining accuracy.

Main Methods:

A model-based framework was developed for direct DKI tensor estimation from k-space data.
The method incorporates an intrinsic DKI signal model as a prior, utilizing total variation (TV) regularization and a physically relevant (PhyR) constraint (mDKI-TV-PhyR).
Performance was evaluated against conventional methods and variants using simulated and in vivo data with 4-fold undersampling.

Main Results:

The mDKI-TV-PhyR method demonstrated lower root-mean-square error (RMSE) for all DKI parameters compared to conventional methods.
Bland-Altman analysis indicated the smallest bias and narrowest limits of agreement for fractional anisotropy (FA) and mean kurtosis (MK).
mDKI-TV-PhyR produced MK maps without artifacts, showed improved stability, and reduced FA bias compared to mDKI-TV.

Conclusions:

The proposed mDKI-TV-PhyR method significantly accelerates iEPI DKI acquisition.
This approach holds substantial promise for clinical applications requiring both high temporal efficiency and spatial resolution.
The method enhances the feasibility of advanced diffusion imaging techniques in clinical settings.