Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder

Matthew Lyon, Mauricio A Álvarez, Paul Armitage

Keywords: Diffusion MRI, Deep Learning, Angular super-resolution, Recurrent CNN, Image Synthesis

TL;DR: We construct a 3D Recurrent CNN architecture to perform super angular resolution on dMRI data.

Abstract:

High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time
in clinical settings, thus restricting the use of downstream analysis techniques that
would otherwise be available. In this work we develop a 3D recurrent convolutional neural
network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space)
domain. Our approach formulates the task of angular super-resolution as a patch-wise regression
using a 3D autoencoder conditioned on target b-vectors. Within the network we use a
convolutional long short term memory (ConvLSTM) cell to model the relationship between
q-space samples. We compare model performance against a baseline spherical harmonic
interpolation and a 1D variant of the model architecture. We show that the 3D model has the
lowest error rates across different subsampling schemes and b-values. The relative performance
of the 3D RCNN is greatest in the very low angular resolution domain. Code for this project is
available at github.com/m-lyon/dMRI-RCNN.

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Paper Type: methodological development

Primary Subject Area: Image Synthesis

Secondary Subject Area: Image Acquisition and Reconstruction

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Code And Data: code for this project can be found at https://github.com/m-lyon/dMRI-RCNN. The HCP data used to train and validate results are available through the HCP at https://db.humanconnectome.org.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/angular-super-resolution-in-diffusion-mri/code)