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


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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.HighlightsWe demonstrate a classifier for the quality control of DW-MRI data.The classifier is trained to spot movement-corrupted volumes.It greatly reduces the need for labelled training data by making use of simulation.The classifier performance is similar to a classifier trained entirely on real data.

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