Department of Radiology, University of Washington School of Medicine, Seattle, WA.
Present address: Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
AJR Am J Roentgenol. 2021 Sep;217(3):651-663. doi: 10.2214/AJR.20.25093. Epub 2020 Dec 30.
Dual-energy CT (DECT) overcomes several limitations of conventional single-energy CT (SECT) for the evaluation of gastrointestinal diseases. This article provides an overview of practical aspects of the DECT technology and acquisition protocols, reviews existing clinical applications, discusses current challenges, and describes future directions, with a focus on gastrointestinal imaging. A head-to-head comparison of technical specifications among DECT scanner implementations is provided. Energy- and material-specific DECT image reconstructions enable retrospective (i.e., after examination acquisition) image quality adjustments that are not possible using SECT. Such adjustments may, for example, correct insufficient contrast bolus or metal artifacts, thereby potentially avoiding patient recalls. A combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can be included in protocols to improve lesion detection and disease characterization. Relevant literature is reviewed regarding use of DECT for evaluation of the liver, gallbladder, pancreas, and bowel. Challenges involving cost, workflow, body habitus, and variability in DECT measurements are considered. Artificial intelligence and machine-learning image reconstruction algorithms, PACS integration, photon-counting hardware, and novel contrast agents are expected to expand the multienergy capability of DECT and further augment its value.
双能 CT(DECT)克服了传统单能 CT(SECT)在胃肠道疾病评估方面的一些局限性。本文概述了 DECT 技术和采集方案的实际方面,综述了现有的临床应用,讨论了当前的挑战,并描述了未来的发展方向,重点是胃肠道成像。提供了 DECT 扫描仪实施之间的技术规格的直接比较。基于能量和材料的 DECT 图像重建可实现回顾性(即在检查采集后)图像质量调整,这是 SECT 不可能实现的。例如,这些调整可以纠正对比剂团注不足或金属伪影,从而可能避免患者召回。可以在方案中结合低能单能图像、碘图和虚拟非增强图像,以提高病变检测和疾病特征描述的能力。回顾了有关 DECT 在肝脏、胆囊、胰腺和肠道评估中的应用的相关文献。考虑了涉及成本、工作流程、体型和 DECT 测量变异性的挑战。人工智能和机器学习图像重建算法、PACS 集成、光子计数硬件和新型对比剂有望扩展 DECT 的多能量能力,并进一步提高其价值。