提出四种评估医学影像定量方法的新框架，助力临床转化。
Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directions
- 用虚拟成像试验评估方法性能，无需真实金标准。
- 支持联合检测与量化任务的评估，提升临床实用性。
- 适配多维参数输出，适合深度学习和放射组学应用。
定量影像（QI）在多个临床场景中展现出巨大潜力。为推动其临床转化，基于临床相关任务的客观评估至关重要。本文基于前期研究，系统梳理了四种新兴评估框架：首先介绍虚拟成像试验（VITs）用于评估QI方法；其次提出无金标准评估框架，可在无真实标签情况下进行临床评估；第三，构建针对联合检测与定量任务的评估体系；最后，提出适用于输出多维参数（如放射组学特征）的评估方法。我们综述了各框架的适用性与局限性，并展望未来研究方向。结合正电子发射断层扫描（PET）领域进展，包括长轴向视野扫描仪及人工智能算法的发展，本文将上述框架置于PET应用场景中进行阐述。
原文摘要 · Abstract (English)
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.
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