Long Zaiyang, Zhou Wei, Tradup Donald J, Stekel Scott F, Callstrom Matthew R, Hangiandreou Nicholas J
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
J Appl Clin Med Phys. 2020 Oct;21(10):192-199. doi: 10.1002/acm2.13000. Epub 2020 Aug 11.
Ultrasound grayscale imaging preset optimization has often been qualitative and dependent upon vendor application specialists. This study aimed to propose a systematic approach for grayscale imaging preset optimization and apply the approach in a clinical abdominal scan setting.
A six-step approach was detailed including identification of clinical task, adjustment of basic parameters, fine-tuning of advanced parameters, image performance metrics confirmation, clinical evaluation and data analysis, and implementation of new presets and monitoring of clinical usage. Its application in an abdominal scanning task was described for each step with phantoms, volunteers, and software tools.
Clinical image data analytics facilitated the understanding of the imaging task, relevant transducers, and target characteristics, in addition to specific requests from radiologists. Quantitative measurements were made on global image contrast and gray map function. In addition, clinically relevant phantoms and volunteer scans without and with acoustic distortion layers were involved to determine the new presets. Furthermore, phantom signal to noise ratio study and clinical evaluation using volunteers with different body habitus were utilized to confirm the superiority of the new presets. Quantitative clinical usage monitoring demonstrated successful implementation of the new presets.
A systematic approach for grayscale imaging preset optimization has been proposed and successfully applied for a specific clinical task. This approach was designed to be generalizable and relatively flexible, which would facilitate movement away from previous qualitative and subjective approaches.
超声灰阶成像预设优化通常是定性的，且依赖于设备供应商的应用专家。本研究旨在提出一种系统的灰阶成像预设优化方法，并将该方法应用于临床腹部扫描场景。
详细介绍了一种六步法，包括确定临床任务、调整基本参数、微调高级参数、确认图像性能指标、进行临床评估和数据分析，以及实施新预设并监测临床使用情况。通过体模、志愿者和软件工具，对每一步在腹部扫描任务中的应用进行了描述。
临床图像数据分析有助于理解成像任务、相关换能器和目标特征，以及放射科医生的特定要求。对整体图像对比度和灰度图功能进行了定量测量。此外，还使用了具有和不具有声学失真层的临床相关体模和志愿者扫描来确定新预设。此外，利用体模信噪比研究和对不同体型志愿者的临床评估来确认新预设的优越性。定量临床使用监测表明新预设得到了成功实施。
提出了一种系统的灰阶成像预设优化方法，并成功应用于特定临床任务。该方法设计得具有通用性和相对灵活性，这将有助于摆脱以往定性和主观的方法。
