Key Points
Question
Can in silico imaging trials play a role in the evaluation of new medical imaging systems?
Findings
This diagnostic study used computer-simulated imaging of 2986 synthetic image–based virtual patients to compare digital mammography and digital breast tomosynthesis and found an improved lesion detection performance favoring tomosynthesis for all breast sizes and lesion types. The increased performance for tomosynthesis was consistent with results from a comparative trial using human patients and radiologists.
Meaning
The study’s findings suggest that in silico imaging trials and imaging system computer simulation tools can in some cases be considered viable sources of evidence for the regulatory evaluation of imaging devices.
Purpose
In silico imaging clinical trials are emerging alternative sources of evidence for regulatory evaluation and are typically cheaper and faster than human trials. In this Note, we describe the set of in silico imaging software tools used in the VICTRE (Virtual Clinical Trial for Regulatory Evaluation) which replicated a traditional trial using a computational pipeline.
Materials and methods
We describe a complete imaging clinical trial software package for comparing two breast imaging modalities (digital mammography and digital breast tomosynthesis). First, digital breast models were developed based on procedural generation techniques for normal anatomy. Second, lesions were inserted in a subset of breast models. The breasts were imaged using GPU‐accelerated Monte Carlo transport methods and read using image interpretation models for the presence of lesions. All in silico components were assembled into a computational pipeline. The VICTRE images were made available in DICOM format for ease of use and visualization.
Results
We describe an open‐source collection of in silico tools for running imaging clinical trials. All tools and source codes have been made freely available.
Conclusion
The open‐source tools distributed as part of the VICTRE project facilitate the design and execution of other in silico imaging clinical trials. The entire pipeline can be run as a complete imaging chain, modified to match needs of other trial designs, or used as independent components to build additional pipelines.
The computational modeling of medical imaging systems often requires obtaining a large number of simulated images with low statistical uncertainty which translates into prohibitive computing times. We describe a novel hybrid approach for Monte Carlo simulations that maximizes utilization of CPUs and GPUs in modern workstations. We apply the method to the modeling of indirect x-ray detectors using a new and improved version of the code MANTIS, an open source software tool used for the Monte Carlo simulations of indirect x-ray imagers. We first describe a GPU implementation of the physics and geometry models in fastDETECT2 (the optical transport model) and a serial CPU version of the same code. We discuss its new features like on-the-fly column geometry and columnar crosstalk in relation to the MANTIS code, and point out areas where our model provides more flexibility for the modeling of realistic columnar structures in large area detectors. Second, we modify PENELOPE (the open source software package that handles the x-ray and electron transport in MANTIS) to allow direct output of location and energy deposited during x-ray and electron interactions occurring within the scintillator. This information is then handled by optical transport routines in fastDETECT2. A load balancer dynamically allocates optical transport showers to the GPU and CPU computing cores. Our hybridMANTIS approach achieves a significant speed-up factor of 627 when compared to MANTIS and of 35 when compared to the same code running only in a CPU instead of a GPU. Using hybridMANTIS, we successfully hide hours of optical transport time by running it in parallel with the x-ray and electron transport, thus shifting the computational bottleneck from optical tox-ray transport. The new code requires much less memory than MANTIS and, asa result, allows us to efficiently simulate large area detectors.
hybridMANTIS is a useful tool for improved description and optimization of image acquisition stages in medical imaging systems and for modeling the forward problem in iterative reconstruction algorithms.
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