VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION

• Published in: Radiation protection dosimetry Vol. 195; no. 3-4; pp. 363 - 371
• Main Authors: Barufaldi, Bruno, Maidment, Andrew D A, Dustler, Magnus, Axelsson, Rebecca, Tomic, Hanna, Zackrisson, Sophia, Tingberg, Anders, Bakic, Predrag R
• Format: Journal Article
• Language: English
• Published: Oxford University Press 12.10.2021
• Subjects: Cancer and Oncology; Cancer och onkologi; Clinical Medicine; Klinisk medicin; Medical and Health Sciences; Medicin och hälsovetenskap; Radiologi och bildbehandling; Radiology and Medical Imaging; Radiology, Nuclear Medicine and Medical Imaging
• DOI: 10.1093/rpd/ncab080
• ISSN: 0144-8420; 1742-3406

Abstract 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.