ArticleRadiation protection dosimetry2021
VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.
Article in Radiation protection dosimetry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07754500 (Measurement and Imaging Capability of a Novel Medical Imaging Method, Ultrasound Optical Tomography, Based on Ultrasound and Laser Light in Human Tissue), which is not on this map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Measurement and Imaging Capability of a Novel Medical Imaging Method, Ultrasound Optical Tomography, Based on Ultrasound and Laser Light in Human Tissue
Who cites it
19 citing papers in PubMed, 41 citations in OpenAlex.
- A Generative Model of Lung CT Conditioned on Radiomics Features.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- A framework for simulation of temporal evolution and longitudinal studies of breast anatomy in radiology.Medical physics · 2025Article
- An end-to-end CT simulation framework with graphical user interface and sample scanner models.Medical physics · 2025Article
- XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.Medical image analysis · 2025Article
- Optimization and validation of a mechanical compression model for digital breast phantoms in mammography and tomosynthesis simulations.Medical physics · 2025Article
- A virtual imaging study of microcalcification detection performance in digital breast tomosynthesis: Patients versus 3D textured phantoms.Medical physics · 2025Article
- Container applications for the development and integration of virtual imaging platforms.Medical physics · 2025Article
- Black-box Optimization of CT Acquisition and Reconstruction Parameters: A Reinforcement Learning Approach.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Examining the influence of digital phantom models in virtual imaging trials for tomographic breast imaging.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Toward widespread use of virtual trials in medical imaging innovation and regulatory science.Medical physics · 2024Review
- Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.European radiology · 2024Article
- Assessment of Volumetric Dense Tissue Segmentation in Tomosynthesis Using Deep Virtual Clinical Trials.Pattern recognition · 2024Article
- Line-based iterative geometric calibration method for a tomosynthesis system.Medical physics · 2024Article
- Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis.European radiology · 2024Article
- Impact of Tomosynthesis Acquisition on 3D Segmentations of Breast Outline and Adipose/Dense Tissue with AI: A Simulation-Based Study.Tomography (Ann Arbor, Mich.) · 2023Article
- Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis.Tomography (Ann Arbor, Mich.) · 2023Article
- Development of scalable lymphatic system in the 4D XCAT phantom: Application to quantitative evaluation of lymphoma PET segmentations.Medical physics · 2022Article
- Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions.EJNMMI physics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.