A supervised learning approach for diffusion MRI quality control with minimal training data

Mark S Graham,Ivana Drobnjak,Hui Zhang

doi:10.1016/j.neuroimage.2018.05.077

Abstract

Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. Recently supervised learning approaches based on convolutional neural networks have been shown to be competitive with manual inspection. A drawback of these approaches is they still require a manually labelled dataset for training, which is itself time-consuming to produce and still introduces an element of subjectivity. In this work we demonstrate the need for manual labelling can be greatly reduced by training on simulated data, and using a small amount of labelled data for a final calibration step. We demonstrate its potential for the detection of severe movement artefacts, and compare performance to a classifier trained on manually-labelled real data.

Highlights

Quality control (QC) involves ensuring a dataset meets a certain set of standards before the dataset is given the clearance for inclusion in subsequent analyses
For a typical study, which may involve hundreds of subjects, the process can be extremely time-consuming. This is especially true in diffusion MRI (DW-MR) where many volumes might be acquired for every subject, and there are numerous artefacts that each volume must be screened for, such as intravolume movement, radiofrequency spikes, chemical shifts, and ghosting
Some artefacts can be hard to detect with manual QC, such as ghosting artefacts which require the careful examination of every slice in a volume

Summary

Introduction

Quality control (QC) involves ensuring a dataset meets a certain set of standards before the dataset is given the clearance for inclusion in subsequent analyses. For a typical study, which may involve hundreds of subjects, the process can be extremely time-consuming. This is especially true in diffusion MRI (DW-MR) where many volumes might be acquired for every subject, and there are numerous artefacts that each volume must be screened for, such as intravolume movement, radiofrequency spikes, chemical shifts, and ghosting. Some artefacts can be hard to detect with manual QC, such as ghosting artefacts which require the careful examination of every slice in a volume. These challenges have led to an increased interest in automated methods for QC

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