Benchmarking the Quality of Diffusion-Weighted Images

TL;DR

This paper introduces a new automated method for assessing the quality of diffusion-weighted MR images by estimating noise and signal from a single dataset, enabling resolution-independent quality comparisons across images from different vendors.

Contribution

A novel thresholding technique for automatic noise and signal estimation in diffusion-weighted images that does not require user interaction or multiple acquisitions.

Findings

Automatically computed SNR matches manual measurements.

The method enables resolution-independent quality assessment.

Effective across images from different vendors.

Abstract

We present a novel method that allows for measuring the quality of diffusion-weighted MR images dependent on the image resolution and the image noise. For this purpose, we introduce a new thresholding technique so that noise and the signal can automatically be estimated from a single data set. Thus, no user interaction as well as no double acquisition technique, which requires a time-consuming proper geometrical registration, is needed. As a coarser image resolution or slice thickness leads to a higher signal-to-noise ratio (SNR), our benchmark determines a resolution-independent quality measure so that images with different resolutions can be adequately compared. To evaluate our method, a set of diffusion-weighted images from different vendors is used. It is shown that the quality can efficiently be determined and that the automatically computed SNR is comparable to the SNR which is measured manually in a manually selected region of interest.