Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers

Abstract

Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.