Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract

Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which ...

A Model for a Linear a-Se Detector in Simulated X-Ray Breast Imaging With Monte Carlo Software

“…They have the potential of reducing the costs and the time needed by horizon technologies to enter the clinical practice, also avoiding issues related to volunteers' exposure to ionizing radiations. Of particular interest is their use in the evaluations of apparatuses for the breast cancer screening and diagnosis, where 3D imaging is strongly proposed as alternative to conventional mammography whose performance are limited by the tissue superimposition in the 2D images [2][3][4][5][6][7][8]. At the today state-of-the art, there is large availability of digital models of the breast for VCT.…”

“…The detector block is usually left out of the simulation platform for the needed computation complexity and its characteristics are modelled via appropriate post processing. For example, in the VICTRE platform [7,8], which relies on Monte Carlo software for images computations, the detector is not simulated, but the energy released in the direct conversion flat-panel detector is converted in charge per pixel. A Swank factor of 0.99 was assumed and at each pixel an additive noise was included summing a value of charges taken from a Poissonian distribution with average values of 5200 [7].…”

“…A Swank factor of 0.99 was assumed and at each pixel an additive noise was included summing a value of charges taken from a Poissonian distribution with average values of 5200 [7]. A more complex model for the electronic noise was adopted in a recent paper [8]. Main simplifications in the used detector model are related to the absence of image blurring introduced by electron-hole transport within the a-Se wafer, absence of tracking of secondary electrons as well as absence of charge drifting.…”

“…Main simplifications in the used detector model are related to the absence of image blurring introduced by electron-hole transport within the a-Se wafer, absence of tracking of secondary electrons as well as absence of charge drifting. These simplifications may lead to a bias between the spatial resolution of the simulated and that of the real images acquired with clinical scanners [8]. Moreover, the theoretical model adopted for the noise is not benchmarked versus that in real images acquired on clinical scanners.…”