Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

TL;DR

This paper introduces a novel 'soft null hypothesis' testing approach for analyzing brain lesion enhancement in MS patients using DCE-MRI, transforming qualitative null hypotheses into quantitative, testable ones, demonstrated on a large clinical dataset.

Contribution

It proposes a new statistical testing framework for qualitative null hypotheses, specifically applied to brain lesion enhancement detection in MRI data, addressing a gap in existing methods.

Findings

Analyzed 20 subjects with 63 visits each, totaling ~30Gb of imaging data.

Developed a method to convert qualitative hypotheses into quantitative tests.

Demonstrated the approach on a large clinical dataset.

Abstract

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images.