Extending Regression Without Truth to Integrate Ground-Truth Measurements for Evaluating Quantitative Imaging Methods with Patient Data

TL;DR

This paper proposes a method to evaluate quantitative imaging techniques by combining limited patient data without ground truth and datasets with known ground truth, improving evaluation accuracy.

Contribution

It introduces an approach that integrates ground-truth datasets with patient data lacking true values to better evaluate imaging methods with limited samples.

Findings

The integrated approach improves ranking of quantitative imaging methods.

Numerical studies show enhanced evaluation performance over traditional RWT techniques.

Results suggest potential for clinical application with limited patient data.

Abstract

Objective evaluation of quantitative imaging (QI) methods with patient data is often hindered by the lack of gold standards. To address this challenge, a class of regression-without-truth (RWT) techniques have been developed. These techniques assume that the true and measured values are linearly related and estimate the linear-relationship parameters without access to true values. However, reliable estimation of these parameters typically requires many patient samples, which can be expensive and time consuming to obtain, and even impossible in settings such as studies with rare diseases or with new clinical imaging procedures. Thus, there is an important need for strategies to perform evaluation of quantitative imaging methods with a small number of patient samples. In this context, we note that datasets with known ground truth, such as physical phantom studies, could be available. In this manuscript, we propose an approach that integrates information from both patient data without ground truth and known-ground-truth datasets to perform objective evaluation of QI methods. We validated the proposed approach using numerical studies, which showed that the proposed approach yielded improved performance in ranking QI methods compared with RWT technique. The results demonstrate the potential of the proposed approach for evaluating QI methods when patient data are limited and motivate further validation with clinically realistic simulation studies and clinical data.