基于超声波的断层成像重建和合成方法:一个范围审查
Daniel de Wilde1, Aron Alakmeh1, Olivier Zanier1
1Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Journal of imaging informatics in medicine
|October 14, 2025
概括
基于超声波的断层成像将安全与高分辨率相结合,推进医学诊断. 深度学习推动了进步,但临床采用需要标准化和验证以实现更广泛的可访问性.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 计算成像技术的成像
背景情况:
- 超声波 (美国) 图像提供安全性和可访问性,但缺乏空间分辨率和操作员独立性.
- 像CT和MRI这样的先进模式提供了高分辨率的3D成像,但成本昂贵且复杂.
- 基于超声波的断层成像试图将美国的优势与高分辨率能力相结合.
研究的目的:
- 对基于超声波的断层成像技术进行范围审查.
- 识别和分析现有的方法,应用和现场挑战.
- 突出像深度学习这样的新兴技术在推进这种成像模式中的作用.
主要方法:
- 根据PRISMA-SR指南进行范围审查.
- 在PubMed MEDLINE,Embase,Scopus和arXiv中从创立到2025年7月进行的搜索.
- 包含基于超声波的断层成像生成或重建的全文原始研究.
主要成果:
- 86项研究在8256个已识别的文章中满足了纳入标准.
- 关键的方法包括光声断层扫描 (36%) 和超声波计算机断层扫描 (36%).
- 深度学习算法占主导地位 (67%),主要用于乳房,大脑和血管成像.
结论:
- 基于超声波的断层成像显示了显著的进步,主要是由于深度学习.
- 该领域还处于芽阶段,在临床标准化和性能验证方面面临着挑战.
- 未来的研究应该优先考虑算法效率,概括性和对临床翻译的强有力的验证.
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