Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes

Abstract

Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 104, and so permits real-time VCT studies for X-ray breast imaging.

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

“…A noteworthy example is the recently completed VICTRE project (Virtual Imaging Clinical Trials for Regulatory Evaluation) (Badano et al 2018, Badal et al 2021, a large VCT study started by FDA in USA to virtually replicate the clinical trial of an FDAapproved clinical DBT system (FDA 2019). Our team is involved in the VCT-BREAST project, an ongoing MCbased VCT project for performing in silico x-ray examinations of the breast (Mettivier et al 2022). Simulations replicate the 2D or 3D exams and provide absorbed dose maps in the phantom (as well as image projections); they run on a GPU OpenCL based cross-platform adopting GPU accelerating engines for simulation of the imaging datasets and 3D maps of the glandular dose.…”

Patient-specific Monte Carlo-based organ dose estimates in spiral CT via optical 3D body scanning and adaptation of a voxelized phantom dataset: proof-of-principle

“…We point out that, in a recent work, we have demonstrated Graphics Processing Unit (GPU) accelerated fast MC simulations for processing times in the order of 10 9 photon histories s −1 (Mettivier et al 2022), with a code running on a single NVIDIA GeForce RTX 3090 GPU card (10496 NVIDIA CUDA cores, 24 GB GDDR6X RAM). This GPU code (initially developed by, and in collaboration with, the group of Prof X Jia at University Texas Southwestern) was intended for dedicated cone-beam breast CT, digital mammography, and digital breast tomosynthesis imaging and dosimetry.…”

Performance evaluation of digital breast tomosynthesis systems: comparison of current virtual clinical trial methods

“…A recent paper by Mettivier et al (2022) has examined the potential for speed up in breast dosimetry when switching to GPU-based simulation. Three VCT platforms were compared: Agata from the University & INFN Napoli, which is a central processing unit (CPU) implementation of Geant4, XRMC MC code developed by the University of Cagliari (U Cagliari), which also runs on CPU hardware but uses variance reduction techniques and finally the gCTD MC code from the University of Texas Southwestern (Jia et al 2012), which is written in CUDA and runs on a GPU.…”

“…Three VCT platforms were compared: Agata from the University & INFN Napoli, which is a central processing unit (CPU) implementation of Geant4, XRMC MC code developed by the University of Cagliari (U Cagliari), which also runs on CPU hardware but uses variance reduction techniques and finally the gCTD MC code from the University of Texas Southwestern (Jia et al 2012), which is written in CUDA and runs on a GPU. Computation time was reduced by a factor of up to 10 4 , which may ultimately enable real-time patient dosimetry for volumetric breast datasets (Mettivier et al 2022). Future large scale VCTs may benefit from such methods.…”