Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directions

TL;DR

This paper reviews emerging frameworks for objectively evaluating quantitative medical imaging methods, emphasizing their utility, limitations, and future research directions, particularly in the context of PET imaging advancements.

Contribution

It outlines four new evaluation frameworks for QI methods, addressing challenges like lack of ground truth and multi-dimensional outputs, and discusses their applications and limitations.

Findings

Virtual imaging trials (VITs) are useful for QI evaluation.

No-gold-standard frameworks enable clinical assessment without ground truth.

Frameworks for joint detection and multi-dimensional parameters are emerging.

Abstract

Quantitative imaging (QI) is demonstrating strong promise across multiple clinical applications. For clinical translation of QI methods, objective evaluation on clinically relevant tasks is essential. To address this need, multiple evaluation strategies are being developed. In this paper, based on previous literature, we outline four emerging frameworks to perform evaluation studies of QI methods. We first discuss the use of virtual imaging trials (VITs) to evaluate QI methods. Next, we outline a no-gold-standard evaluation framework to clinically evaluate QI methods without ground truth. Third, a framework to evaluate QI methods for joint detection and quantification tasks is outlined. Finally, we outline a framework to evaluate QI methods that output multi-dimensional parameters, such as radiomic features. We review these frameworks, discussing their utilities and limitations. Further, we examine future research areas in evaluation of QI methods. Given the recent advancements in PET, including long axial field-of-view scanners and the development of artificial-intelligence algorithms, we present these frameworks in the context of PET.