最新进展:造影增强四维超声监测或评估局部治疗

M. Tantawi, S. Shamimi-Noori, C. Shaw, J. Eisenbrey
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引用次数: 0

摘要

局部治疗(LRTs)是原发性肝癌或转移性肝病治疗中必不可少的管理工具。lrt包括治疗和姑息方式。监测lrt的治疗反应对于最大限度地提高疗效和改善临床结果至关重要。对比增强超声(CEUS)的临床应用是在二十多年前引入的。它的便携性、成本效益、无禁忌症和安全性使其成为许多情况下治疗监测的理想工具。二维动态超声造影在指导lrt、评估其充分性、发现早期肿瘤复发等方面已被证明与现行影像学标准相当。超声换能器和图像处理的最新技术进步使得3D超声造影扫描广泛应用于大多数商用超声系统。三维扫描提供了解剖结构的广泛的多平面视图,克服了二维扫描的许多限制。此外,许多超声系统提供实时动态3D超声造影,也称为4D超声造影。容积超声造影在一些lrt的评估和监测方面表现优于二维超声造影。超声造影是目前影像学标准的有效替代方案,成本低,并发症风险低。未来的工作将通过多参数定量分析和机器学习算法等方法来改进4D CEUS的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
State of the Art: Contrast Enhanced 4D Ultrasound to Monitor or Assess Locoregional Therapies
Abstract Locoregional therapies (LRTs) are an essential management tool in the treatment of primary liver cancers or metastatic liver disease. LRTs include curative and palliative modalities. Monitoring treatment response of LRTs is crucial for maximizing benefit and improving clinical outcomes. Clinical use of contrast-enhanced ultrasound (CEUS) was introduced more than two decades ago. Its portability, cost effectiveness, lack of contraindications and safety make it an ideal tool for treatment monitoring in numerous situations. Two-dimensional dynamic CEUS has been proved to be equivalent to the current imaging standard in the guidance of LRTs, assessment of their adequacy, and detection of early tumor recurrence. Recent technical advances in ultrasound transducers and image processing have made 3D CEUS scanning widely available on most commercial ultrasound systems. 3D scanning offers a broad multiplanar view of anatomic structures, overcoming many limitations of two-dimensional scanning. Furthermore, many ultrasound systems provide real-time dynamic 3D CEUS, also known as 4D CEUS. Volumetric CEUS has shown to perform better than 2D CEUS in the assessment and monitoring of some LRTs. CEUS presents a valid alternative to the current imaging standards with reduced cost and decreased risk of complications. Future efforts will be directed toward refining the utility of 4D CEUS through approaches such as multi-parametric quantitative analysis and machine learning algorithms.
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