Contrast-enhanced mammography (CEM) has shown increased sensitivity for detecting breast cancer when compared to traditional full-field digital mammography with performance comparable to MRI. While all current CEM systems use a dual-energy approach, photon-counting detectors can similarly be used by acquiring two or more energy bins to subtract anatomical noise and highlight iodine uptake. Photon-counting detectors have several advantages over dual-energy such as the simultaneous acquisition of multiple energy bins and the potential for electronic noise rejection. However, photon-counting detectors suffer from several physical phenomena such as charge sharing and k-shell fluorescence that degrade its spatial and spectral response. Solving for true counts given measured photon counts constitutes an inverse problem. While, analytically difficult to solve, machine learning techniques can be an alternative method. In this simulated study, we investigated the use a light-weight convolutional neural network to correct for spatial and spectral degradations in CEM acquisitions using a photon-counting detector.
PurposeMost photon-counting detectors (PCDs) being developed use cadmium telluride (CdTe), which has nonoptimal characteristic x-ray emission with energies in the range used for breast imaging. New PCD using a gallium arsenide (GaAs) has been developed. Since GaAs has characteristic x-rays lower in energy than those of CdTe, it is hypothesized that this new PCD might be beneficial for spectral x-ray breast imaging.ApproachWe performed simulations using realistic mammography x-ray spectra with both CdTe and GaAs PCDs. Five different experiments were conducted, each comparing the performance of CdTe and GaAs: (1) sensitivity of iodine quantification to charge cloud size and electronic noise, (2) effect of photon spectrum on iodine quantification, (3) effect of varying the number of energy bins, (4) a dose analysis to assess any possible dose reduction from using either detector, and (5) spectral performance of ideal CdTe and GaAs PCDs. For each study, 3 sets of 5000 noise realizations were used to calculate the Cramer–Rao lower bound (CRLB) of iodine quantification.ResultsFor all spectra studied, GaAs gave a lower CRLB for iodine quantification, with 10 of the 12 spectra showing a statistically significant difference (p ≤ 0.05). The photon energy spectrum that optimized iodine detection for both detector materials was the 40 kVp beam with 2-mm Al filtration, which produced CRLBs of 0.282 cm2 and 0.257 cm2 for CdTe and GaAs, respectively, when using five energy bins.ConclusionGaAs is a promising detector material for contrast-enhanced spectral mammography that offers better spectral performance than CdTe.
There has recently been renewed interest in quantitative iodinated contrast-enhanced breast imaging, sometimes referred to as contrast-enhanced spectral mammography (CESM). Photon-counting detectors (PCDs) have a number of benefits for iodinated contrast-enhanced imaging over dual-energy systems (using energy integrating detectors), one of which is the capability of acquiring multi-bin data with one exposure. Most PCDs and prototype systems being developed are using CdTe or CZT sensor material which have non-optimal characteristic X-ray emission with energies in the range used for breast imaging. This increases charge sharing and, hence, spectral degradation. Recently, a new PCD has been developed using a GaAs sensor. Since GaAs has lower energy characteristic x-rays (lower than CdTe), it is expected that this new PCD detector might be beneficial for spectral x-ray breast imaging. In this work, we have theoretically compared the two detector materials in terms of iodine quantification using the Cramer-Rao lower bound (CRLB) as a figure of merit. Four different experiments were performed, each comparing the performance of CdTe and GaAs: 1) sensitivity of iodine quantification to charge cloud size and electronic noise, 2) effect of dose and photon spectrum on iodine quantification, 3) how the CRLB changes with the number of energy bins, and 4) a dose analysis study to assess any possible dose reduction offered by either detector. Simulations of both the CdTe and GaAs PCDs were performed using the Photon Counting Toolkit (PcTK) software. Three sets of 5000 noise realizations were used to calculate the CRLB of iodine quantification in each study. Results of these studies suggest that GaAs is a promising detector material for contrast-enhanced spectral mammography
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