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迈向机器人多模态诊断成像系统的性能表征

Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System.

作者信息

Papaioannou George, Mitrogiannis Christos, Schweitzer Mark, Michailidis Nikolaos, Pappa Maria, Khosravi Pegah, Karantanas Apostolos, Starling Sean, Ruberg Christian

机构信息

New Bedford Research & Robotics, Downtown New Bedford, New Bedford, MA 02740, USA.

Office of the Vice President of Health Affairs, School of Medicine, Wayne State University, 1241 Scott Hall, 540 E. Canfield, Detroit, MI 48201, USA.

出版信息

J Imaging. 2025 May 7;11(5):147. doi: 10.3390/jimaging11050147.

Abstract

Characterizing imaging performance requires a multidisciplinary approach that evaluates various interconnected parameters, including dosage optimization and dynamic accuracy. Radiation dose and dynamic accuracy are challenged by patient motion that results in poor image quality. These challenges are more prevalent in the brain/cardiac pediatric patient imaging, as they relate to excess radiation dose that may be associated with various complications. Scanning vulnerable pediatric patients ought to eliminate anesthesia due to critical risks associated in some cases with intracranial hemorrhages, brain strokes, and congenital heart disease. Some pediatric imaging, however, requires prolonged scanning under anesthesia. It can often be a laborious, suboptimal process, with limited field of view and considerable dose. High dynamic accuracy is also necessary to diagnose tissue's dynamic behavior beyond its static structural morphology. This study presents several performance characterization experiments from a new robotic multimodal imaging system using specially designed calibration methods at different system configurations. Additional musculoskeletal imaging and imaging from a pediatric brain stroke patient without anesthesia are presented for comparisons. The findings suggest that the system's large dynamically controlled gantry enables scanning at full patient movement and with important improvements in scan times, accuracy, radiation dose, and the ability to image brain structures without anesthesia. This could position the system as a potential transformative tool in the pediatric interventional imaging landscape.

摘要

表征成像性能需要一种多学科方法,该方法要评估各种相互关联的参数,包括剂量优化和动态准确性。患者运动对辐射剂量和动态准确性构成挑战,会导致图像质量不佳。这些挑战在儿科脑部/心脏成像中更为普遍,因为这涉及到可能与各种并发症相关的过量辐射剂量。由于在某些情况下与颅内出血、脑卒中和先天性心脏病相关的重大风险,对脆弱的儿科患者进行扫描时应避免麻醉。然而,一些儿科成像需要在麻醉下进行长时间扫描。这通常是一个费力且不理想的过程,视野有限且剂量可观。除了静态结构形态外,高动态准确性对于诊断组织的动态行为也很必要。本研究展示了来自一个新型机器人多模态成像系统的多个性能表征实验,该实验采用了不同系统配置下的特殊设计校准方法。还展示了额外的肌肉骨骼成像以及一名未麻醉的儿科脑卒中患者的成像,以供比较。研究结果表明,该系统的大型动态控制龙门架能够在患者完全移动的情况下进行扫描,并在扫描时间、准确性、辐射剂量以及无需麻醉即可成像脑部结构的能力方面有重要改进。这可能使该系统成为儿科介入成像领域潜在的变革性工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3334/12112859/e099584a81c9/jimaging-11-00147-g001.jpg

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