成像体模
生物医学中的光声成像
计算机科学
质量保证
医学物理学
集合(抽象数据类型)
数据采集
数据集
人工智能
医学
核医学
光学
病理
外部质量评估
操作系统
物理
程序设计语言
作者
Sarah E. Bohndiek,Joanna Brunker,Janek Gröhl,Lina Hacker,James Joseph,William C. Vogt,Paolo Armanetti,Hisham Assi,Jeffrey C. Bamber,Paul C. Beard,Thomas Berer,Richard R. Bouchard,Kimberly A. Briggman,Lucia Cavigli,Bryan Clingman,Ben Cox,Adrien E. Desjardins,Andrew Heinmiller,Jeesong Hwang,Eno Hysi
出处
期刊:Photons Plus Ultrasound: Imaging and Sensing 2019
日期:2019-03-04
卷期号:: 57-57
被引量:4
摘要
The International Photoacoustic Standardisation Consortium (IPASC) emerged from SPIE 2018, established to drive consensus on photoacoustic system testing. As photoacoustic imaging (PAI) matures from research laboratories into clinical trials, it is essential to establish best-practice guidelines for photoacoustic image acquisition, analysis and reporting, and a standardised approach for technical system validation. The primary goal of the IPASC is to create widely accepted phantoms for testing preclinical and clinical PAI systems. To achieve this, the IPASC has formed five working groups (WGs). The first and second WGs have defined optical and acoustic properties, suitable materials, and configurations of photoacoustic image quality phantoms. These phantoms consist of a bulk material embedded with targets to enable quantitative assessment of image quality characteristics including resolution and sensitivity across depth. The third WG has recorded details such as illumination and detection configurations of PAI instruments available within the consortium, leading to proposals for system-specific phantom geometries. This PAI system inventory was also used by WG4 in identifying approaches to data collection and sharing. Finally, WG5 investigated means for phantom fabrication, material characterisation and PAI of phantoms. Following a pilot multi-centre phantom imaging study within the consortium, the IPASC settled on an internationally agreed set of standardised recommendations and imaging procedures. This leads to advances in: (1) quantitative comparison of PAI data acquired with different data acquisition and analysis methods; (2) provision of a publicly available reference data set for testing new algorithms; and (3) technical validation of new and existing PAI devices across multiple centres.
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