VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.
VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.
复制标题
医学成像系统评估和优化中的虚拟临床试验。
DOI：
10.1093/rpd/ncab080
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发表时间：
2021-10-12
影响因子：
1
通讯作者：
Bakic PR
中科院分区：
文献类型：
--
作者：
Barufaldi B;Maidment ADA;Dustler M;Axelsson R;Tomic H;Zackrisson S;Tingberg A;Bakic PR
虚拟临床试验（vct）可用于评估和优化医学成像系统。vct是基于人体解剖、成像模式和图像解释的计算机模拟。OpenVCT是一个进行医学成像vct的开源框架，特别侧重于乳房成像。本文的目的是评估OpenVCT框架在涉及数字乳房断层合成（DBT）的两个任务中的应用。首先，vct被用于对数字乳房x线摄影和DBT中病变检测的虚拟和临床阅读研究进行详细的比较。然后，将该框架扩展到包括机械成像（MI），并用于优化同时DBT和MI的新组合。第一次实验显示临床和虚拟研究之间的密切一致，证实了vct可以准确预测DBT性能的变化。同时在DBT和MI系统中的工作表明，该系统可以在DBT图像质量方面进行优化。我们目前正在努力扩展OpenVCT软件，以更准确地模拟心肌采集，并包括肿瘤生长模型。根据我们迄今为止的经验，我们设想未来vct在医学成像中发挥重要作用，包括支持更多的成像模式，用于罕见疾病以及在人工智能（AI）系统的培训和测试中发挥作用。
Virtual clinical trials (VCTs) can be used to evaluate and optimise medical imaging systems. VCTs are based on computer simulations of human anatomy, imaging modalities and image interpretation. OpenVCT is an open-source framework for conducting VCTs of medical imaging, with a particular focus on breast imaging. The aim of this paper was to evaluate the OpenVCT framework in two tasks involving digital breast tomosynthesis (DBT). First, VCTs were used to perform a detailed comparison of virtual and clinical reading studies for the detection of lesions in digital mammography and DBT. Then, the framework was expanded to include mechanical imaging (MI) and was used to optimise the novel combination of simultaneous DBT and MI. The first experiments showed close agreement between the clinical and the virtual study, confirming that VCTs can predict changes in performance of DBT accurately. Work in simultaneous DBT and MI system has demonstrated that the system can be optimised in terms of the DBT image quality. We are currently working to expand the OpenVCT software to simulate MI acquisition more accurately and to include models of tumour growth. Based on our experience to date, we envision a future in which VCTs have an important role in medical imaging, including support for more imaging modalities, use with rare diseases and a role in training and testing artificial intelligence (AI) systems.
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DOI：
10.1073/pnas.90.21.9758
发表时间：
1993-11-01
影响因子：
11.1
作者：
BARRETT, HH;YAO, J;MYERS, KJ
通讯作者：
MYERS, KJ
影响因子：
13.8
作者：
Badano, Aldo;Graff, Christian G.;Myers, Kyle J.
通讯作者：
Myers, Kyle J.
DOI：
10.1115/1.4005694
发表时间：
2012-01-01
影响因子：
1.7
作者：
Maas, Steve A.;Ellis, Benjamin J.;Weiss, Jeffrey A.
通讯作者：
Weiss, Jeffrey A.
影响因子：
3.5
作者：
Mackenzie, Alistair;Marshall, Nicholas W.;Young, Kenneth C.
通讯作者：
Young, Kenneth C.
影响因子：
1
作者：
Hunt, RA;Dance, DR;Carlsson, GA
通讯作者：
Carlsson, GA