Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.
Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.
复制标题
光声超声交织成像的深度学习方法综述。
DOI：
10.1109/tuffc.2023.3329119
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发表时间：
2023-12
影响因子：
3.6
通讯作者：
O'Donnell, Matthew
中科院分区：
文献类型：
--
作者：
Kim, Minwoo;Pelivanov, Ivan;O'Donnell, Matthew
关键词：
与其他光学方法相比，光声（PA）成像以超声（US）空间分辨率在人体内相对大的深度处提供光学对比度。通过集成实时PA和US（PAUS）模式，PAUS成像有可能成为一种常规的临床模式，将光学的分子灵敏度引入医学US成像。对于必须在PAUS中保持临床US扫描仪的全部功能的应用，必须使用传统的有限视图和带宽换能器。然而，这种方法不能提供PA源的高质量图，特别是血管结构。深度学习（DL）使用数据驱动的建模和最少的人类设计，在医学成像，医学数据分析和疾病诊断方面非常有效，并且有可能克服当前PAUS成像系统的许多技术限制。本文的主要目的是总结DL在PAUS成像中的应用背景和现状。它还超越了目前的方法，以确定剩余的挑战和机会，强大的翻译PAUS技术的诊所。
Photoacoustic (PA) imaging provides optical contrast at relatively large depths within the human body, compared to other optical methods, at ultrasound (US) spatial resolution. By integrating real-time PA and US (PAUS) modalities, PAUS imaging has the potential to become a routine clinical modality bringing the molecular sensitivity of optics to medical US imaging. For applications where the full capabilities of clinical US scanners must be maintained in PAUS, conventional limited view and bandwidth transducers must be used. This approach, however, cannot provide high-quality maps of PA sources, especially vascular structures. Deep learning (DL) using data-driven modeling with minimal human design has been very effective in medical imaging, medical data analysis, and disease diagnosis, and has the potential to overcome many of the technical limitations of current PAUS imaging systems. The primary purpose of this article is to summarize the background and current status of DL applications in PAUS imaging. It also looks beyond current approaches to identify remaining challenges and opportunities for robust translation of PAUS technologies to the clinic.
DOI：
10.14366/usg.14062
发表时间：
2015-04
期刊：
Ultrasonography (Seoul, Korea)
影响因子：
--
作者：
Kim J;Lee D;Jung U;Kim C
通讯作者：
Kim C