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MULTI-SHOT SENSITIVITY-ENCODED DIFFUSION MRI USING MODEL-BASED DEEP LEARNING (MODL-MUSSELS).

Hemant K Aggarwal¹, Merry P Mani¹, Mathews Jacob¹

¹University of Iowa, Iowa, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|February 15, 2021

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We developed a new deep learning method to fix MRI image distortions. This approach significantly speeds up image reconstruction while maintaining high quality.

Area of Science:

Medical Imaging
Machine Learning
Biomedical Engineering

Background:

Multishot diffusion-weighted echo-planar imaging (DW-EPI) is crucial for MRI.
Phase errors in DW-EPI can degrade image quality and diagnostic accuracy.
Existing methods like MUSSELS offer solutions but can be computationally intensive.

Purpose of the Study:

To introduce a novel model-based deep learning (MoDL) architecture for correcting phase errors in multishot DW-EPI.
To generalize and improve upon the MUSSELS algorithm.
To reduce the computational complexity of phase error correction in MRI.

Main Methods:

Developed a MoDL framework by adapting an iterative reweighted least-squares implementation of MUSSELS.
Replaced the linear filter bank in MUSSELS with a convolutional neural network (CNN).

Keywords:

Convolutional Neural Network Echo Planar Imaging K-space Deep learning

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Trained the CNN parameters using exemplary MRI data.

Main Results:

The proposed MoDL algorithm significantly reduces computational complexity compared to MUSSELS.
Achieved comparable image quality to existing methods.
Demonstrated effective correction of phase errors in multishot DW-EPI.

Conclusions:

The proposed MoDL architecture offers an efficient and effective solution for phase error correction in multishot DW-EPI.
This method holds promise for accelerating MRI acquisition and improving image analysis.
The generalization of MUSSELS using deep learning provides a powerful new tool in medical imaging.