Clinical Imaging Physics Group, Medical Physics Graduate Program, Carl E. Ravin Advanced Imaging Laboratories, Duke University, 2424 Erwin Rd, Suite 302, Durham, NC 27705.
Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Acad Radiol. 2020 Jun;27(6):847-855. doi: 10.1016/j.acra.2019.07.015. Epub 2019 Aug 22.
Clinically-relevant quantitative measures of task-based image quality play key roles in effective optimization of medical imaging systems. Conventional phantom-based measures do not adequately reflect the real-world image quality of clinical Computed Tomography (CT) series which is most relevant for diagnostic decision-making. The assessment of detectability index which incorporates measurements of essential image quality metrics on patient CT images can overcome this limitation. Our current investigation extends and validates the technique on standard-of-care clinical cases.
We obtained a clinical CT image dataset from an Institutional Review Board-approved prospective study on colorectal adenocarcinoma patients for detecting hepatic metastasis. For this study, both perceptual image quality and lesion detection performance of same-patient CT image series with standard and low dose acquisitions in the same breath hold and four processing algorithms applied to each acquisition were assessed and ranked by expert radiologists. The clinical CT image dataset was processed using the previously validated method to estimate a detectability index for each known lesion size in the size distribution of hepatic lesions relevant for the imaging task and for each slice of a CT series. We then combined these lesion-size-specific and slice-specific detectability indexes with the size distribution of hepatic lesions relevant for the imaging task to compute an effective detectability index for a clinical CT imaging condition of a patient. The assessed effective detectability indexes were used to rank task-based image quality of different imaging conditions on the same patient for all patients. We compared the assessments to those by expert radiologists in the prospective study in terms of rank order agreement between the rankings of algorithmic and visual assessment of lesion detection and perceptual quality.
Our investigation indicated that algorithmic assessment of lesion detection and perceptual quality can predict observer assessment for detecting hepatic metastasis. The algorithmic and visual assessment of lesion detection and perceptual quality are strongly correlated using both the Kendall's Tau and Spearman's Rho methods (perfect agreement has value 1): for assessment of lesion detection, 95% of the patients have rank correlation coefficients values exceeding 0.87 and 0.94, respectively, and for assessment of perceptual quality, 0.85 and 0.94, respectively.
This study used algorithmic detectability index to assess task-based image equality for detecting hepatic lesions and validated it against observer rankings on standard-of-care clinical CT cases. Our study indicates that detectability index provides a robust reflection of overall image quality for detecting hepatic lesions under clinical CT imaging conditions. This demonstrates the concept of utilizing the measure to quantitatively assess the quality of the information content that different imaging conditions can provide for the same clinical imaging task, which enables targeted optimization of clinical CT systems to minimize clinical and patient risks.
基于任务的图像质量的临床相关定量测量在医学成像系统的有效优化中起着关键作用。传统的基于体模的测量方法不能充分反映与诊断决策最相关的临床计算机断层扫描 (CT) 系列的实际图像质量。纳入基本图像质量指标测量的检测指数评估可以克服这一局限性。我们当前的研究扩展并验证了该技术在标准临床病例中的应用。
我们从一个机构审查委员会批准的前瞻性研究中获得了一组临床 CT 图像数据集，该研究用于检测结直肠癌患者的肝转移。在这项研究中，同一位放射科医生评估和排名了同一位患者 CT 图像系列的标准和低剂量采集的感知图像质量和病变检测性能，以及应用于每个采集的四种处理算法。使用之前验证的方法对临床 CT 图像数据集进行处理，以估计在与成像任务相关的肝病变大小分布中每个已知病变大小的检测指数，以及 CT 系列的每个切片。然后，我们将这些病变大小特异性和切片特异性检测指数与与成像任务相关的肝病变的大小分布相结合，以计算患者临床 CT 成像条件的有效检测指数。评估的有效检测指数用于对同一位患者的不同成像条件进行基于任务的图像质量排名，适用于所有患者。我们比较了评估结果与前瞻性研究中放射科医生的评估结果，比较了算法评估和视觉评估的病变检测和感知质量之间的排名顺序一致性。
我们的研究表明，病变检测和感知质量的算法评估可以预测观察者对肝转移的检测。使用 Kendall's Tau 和 Spearman's Rho 方法（完美一致的值为 1），病变检测和感知质量的算法评估和视觉评估具有很强的相关性：对于病变检测评估，95%的患者的等级相关系数值超过 0.87 和 0.94，分别为 0.85 和 0.94，分别用于感知质量评估。
本研究使用算法检测指数评估了检测肝病变的基于任务的图像均等性，并通过对标准临床 CT 病例的观察者排名进行了验证。我们的研究表明，检测指数为临床 CT 成像条件下检测肝病变提供了整体图像质量的稳健反映。这证明了利用该测量方法定量评估不同成像条件为同一临床成像任务提供的信息内容质量的概念，从而能够有针对性地优化临床 CT 系统，最大限度地降低临床和患者风险。
