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Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

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Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction

Ranjeet Ranjan Jha¹, Gaurav Jaswal², Arnav Bhavsar¹

¹MANAS Lab, School of Computing and Electrical Engineering (SCEE), Indian Institute of Technology (IIT) Mandi, India.

Magnetic Resonance Imaging

|January 12, 2022

Summary

We developed a deep learning method to reconstruct high-resolution diffusion MRI data from single-shell acquisitions. This technique enhances brain white matter fiber tract analysis without increasing scan time.

Keywords:

Attention Module Diffusion MRI Encoder-Decoder N/W GANs MRI Multi-shell HARDI Single-shell HARDI Spherical Harmonics fODF

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Area of Science:

Neuroimaging
Diffusion MRI
Machine Learning

Background:

High angular resolution diffusion imaging (HARDI) is crucial for mapping brain white matter. Single-shell HARDI has limitations in resolving complex intravoxel structures.
Multi-shell HARDI offers higher resolution but requires longer acquisition times and complex setups, posing practical challenges.

Purpose of the Study:

To propose a novel deep learning framework for reconstructing multi-b-value diffusion MRI data from single-shell acquisitions.
To improve the estimation of intravoxel white matter structures and enhance the accuracy of fiber tractography.

Main Methods:

A Multilevel Hierarchical Spherical Harmonics Coefficients Reconstruction (MHSH) framework was developed, utilizing Slice Level ReconNet (SLRNet) and Volumetric ROI Level ReconNet (VPLRNet).
Reconstruction was performed in the spherical harmonics space to handle varying gradient directions effectively.
The framework was trained and validated using L1, Adversarial, and Total Variation loss functions on the Human Connectome Project (HCP) dataset.

Main Results:

The MHSH framework successfully reconstructed diffusion MRI volumes for b = 3000 s/mm² and b = 2000 s/mm² from b = 1000 s/mm² single-shell data.
Qualitative and quantitative performance measures demonstrated promising results, indicating improved resolution of intravoxel structures.
The method effectively leveraged contextual information within and across slices for enhanced reconstruction.

Conclusions:

The proposed deep learning approach offers a viable solution for obtaining high-resolution diffusion MRI data from efficient single-shell acquisitions.
This method has the potential to advance the study of brain white matter microstructures and neurological disorders without extending MRI scan times.
MHSH framework shows significant promise for improving diffusion MRI analysis in clinical and research settings.