Theoretical comparison and optimization of CdTe and GaAs photon-counting detectors for contrast-enhanced spectral mammography

Schaeffer, Colin; Ghammraoui, Bahaa; Taguchi, Katsuyuki; Glick, Stephen J.

doi:10.1117/12.2611134

Cited by 2 publications

(15 citation statements)

References 7 publications

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“…The inclusion of a third energy bin may improve image quality. 10 Another limitation of slot-scanning PCD technology is that its incorporation into digital mammography systems requires extensive modification of existing systems, which are primarily based on flat-panel x-ray detectors, for example, amorphous selenium or cesium iodide. In contrast, cadmium telluride (CdTe) PCDs have a higher atomic number than silicon PCDs and, as such, can be implemented as large-area flat-panel detectors.…”

Section: Introductionmentioning

confidence: 99%

“…The most substantial work on CdTe for CESM is a recent paper by Schaeffer et al 10 who optimized CdTe PCDs for CESM and compared with gallium arsenide (GaAs) PCDs. Using the Cramer Rao lower bound as a figure of merit (FOM), they showed that GaAs generally outperforms CdTe for iodine quantification tasks when both systems are optimized.…”

Section: Introductionmentioning

confidence: 99%

“…While the findings of this study are novel and aid in the optimization of CdTe for CESM and the identification of the optimal PCD for CESM, there are a number of limitations. A primary limitation is that Schaeffer et al 14 did not consider the frequency content of the image signal and image noise but instead used FOM related to the signal-to-noise ratio of a single pixel in a large, uniform iodine region embedded in a uniform background under the assumption that the iodine content in each pixel has been quantified using a minimum-variance unbiased estimator. The pixel signalto-noise-ratio can lead to biases when comparing systems or techniques that lead to different spatial resolutions and/or noise texture, for example, when comparing two different detector technologies or two different tube voltages.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, state-of -the-art image-quality assessment of breast-imaging systems accounts for the modulation transfer function (MTF), the noise power spectrum (NPS), and the frequency content of the signal to be detected. 6,[15][16][17] In addition, Schaeffer et al 10 did not consider the effect of analog charge summing (ACS) for charge-sharing correction, which partially, but not entirely, corrects for the degrading effects of charge sharing. They also did not consider the effect of pixel size on image quality, nor did they investigate the effect of x-ray tube anode material.…”

Section: Introductionmentioning

confidence: 99%

“…Another limitation of previous CESM studies, not just that of Schaeffer et al 10 is that they neglect to consider anatomic noise due to background parenchymal enhancement. Hill et al 18 quantified the anatomic NPS associated with background parenchymal enhancement in contrast-enhanced dual-energy mammography but did not perform a detectability analysis.…”

Section: Introductionmentioning

confidence: 99%

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Monte‐Carlo study of contrast‐enhanced spectral mammography with cadmium telluride photon‐counting x‐ray detectors

Day,

Tanguay

2023

Medical Physics

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BackgroundContrast‐enhanced spectral mammography (CESM) with photon‐counting x‐ray detectors (PCDs) can be used to improve the classification of breast cancers as benign or malignant. Commercially‐available PCD‐based mammography systems use silicon‐based PCDs. Cadmium‐telluride (CdTe) PCDs may provide a practical advantage over silicon‐based PCDs because they can be implemented as large‐area detectors that are more easily adaptable to existing mammography systems.PurposeThe purpose of this work is to optimize CESM implemented with CdTe PCDs and to investigate the influence of the number of energy bins, electronic noise level, pixel size, and anode material on image quality.MethodsWe developed a Monte Carlo model of the energy‐bin‐dependent modulation transfer functions (MTFs) and noise power spectra, including spatioenergetic noise correlations. We validated model predictions using a CdTe PCD with analog charge summing for charge‐sharing suppression. Using the ideal‐observer detectability, we optimized CESM for the task of detecting a 7‐mm‐diameter iodine nodule embedded in a breast with 50% glandularity. We optimized the tube voltage, beam filtration, and the location of energy thresholds for 50 and 100‐μm pixels, tungsten and molybdenum anodes, and two electronic noise levels. One of the electronic noise levels was that of the experimental system; the other was half that of the experimental system. Optimization was performed for CdTe PCDs with two or three energy bins. We also estimated the impact of anatomic noise due to background parenchymal enhancement and computed the minimum detectable iodine area density in the presence of quantum and anatomic noise.ResultsModel predictions of the MTFs and noise power spectra agreed well with experiment. For optimized systems, adding a third energy bin increased quantum noise levels and reduced detectability by ∼55% compared to two‐bin approaches that simply suppress contrast between fibroglandular and adipose tissue. Decreasing the electronic noise standard deviation from 3.4 to 1.7 keV increased iodine detectability by ∼5% and ∼30% for two‐bin imaging and three‐bin imaging, respectively. After optimizing for tube voltage, beam filtration, and the location of energy thresholds, there was ∼a 3% difference in iodine detectability between molybdenum and tungsten anodes for two‐bin imaging, but for three‐bin imaging, molybdenum anodes provided up to 14% increase in detectability relative to tungsten anodes. Anatomic noise decreased iodine detectability by 15% to 40%, with greater impact for lower electronic noise settings and larger pixel sizes.ConclusionsFor CESM implemented with CdTe PCDs, (1) quantitatively‐accurate three‐material decompositions using three energy bins are associated with substantial increases in quantum noise relative to two‐energy‐bin approaches that simply suppress contrast between fibroglandular and adipose tissues; (2) tungsten and molybdenum anodes can provide nearly equal iodine detectability for two‐bin imaging, but molybdenum provides a modest detectability advantage for three‐bin imaging provided that all other technique parameters are optimized; (3) reducing pixel sizes from 100 to 50 μm can reduce detectability by up to 20% due to charge sharing; (4) anatomic noise due to background parenchymal enhancement is estimated to have a substantial impact on lesion visibility, reducing detectability by approximately 30%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monte‐Carlo study of contrast‐enhanced spectral mammography with cadmium telluride photon‐counting x‐ray detectors

Day,

Tanguay

2023

Medical Physics

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Spectral correction of photon-counting detectors in contrast-enhanced spectral mammography using a light-weight convolutional neural network

Schaeffer,

Glick,

Ghammraoui

2024

Medical Imaging 2024: Physics of Medical Imaging

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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 lightweight convolutional neural network to correct for spatial and spectral degradations in CEM acquisitions using a photon-counting detector.

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Theoretical comparison and optimization of CdTe and GaAs photon-counting detectors for contrast-enhanced spectral mammography

Cited by 2 publications

References 7 publications

Monte‐Carlo study of contrast‐enhanced spectral mammography with cadmium telluride photon‐counting x‐ray detectors

Monte‐Carlo study of contrast‐enhanced spectral mammography with cadmium telluride photon‐counting x‐ray detectors

Spectral correction of photon-counting detectors in contrast-enhanced spectral mammography using a light-weight convolutional neural network

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