# Comparing energy-integrating detector and photon-counting detector-based breast cone beam CTs for microcalcification detection via Monte Carlo simulation

## TL;DR

This study compares two types of detectors in breast CT scans for detecting tiny calcium deposits, finding that photon-counting detectors may offer better image quality and lower noise.

## Contribution

The paper introduces a direct comparison of energy-integrating and photon-counting detectors in breast CT using realistic GPU-based Monte Carlo simulations.

## Key findings

- Photon-counting detectors (PCD) showed lower image noise and higher spatial resolution compared to energy-integrating detectors (EID).
- PCD-based bCBCT provided better visibility of microcalcifications and breast anatomy like ligaments.
- Contrast-to-noise ratios were significantly higher for PCD-bCBCT across different microcalcification sizes.

## Abstract

Objective. Microcalcification (µCalc) detection plays an important role in breast cancer screening. Electronic noise in energy-integrating detectors (EIDs) is the major challenge for this task in current breast cone-beam CT (bCBCT) due to the tight dose constraint for breast imaging. bCBCT with a photon counting detector (PCD) can potentially offer a higher spatial resolution and lower noise. This study performed a direct comparison of bCBCTs with the two detector types via GPU-based Monte Carlo (MC) simulation. Approach. We employed Virtual Clinical Trial for Regulatory Evaluation toolkit to generate a realistic breast phantom with a 0.25 \documentclass[12pt]{minimal}
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$\mathrm{mm}^3$\end{document}mm3 voxel size, 80% fat fraction and 14 cm diameter. We considered a bCBCT system with a 60 kV x-ray source filtered with 0.3 mm Cu and detector response functions for PCD and EID. A total of 360 projections were simulated with a total number of \documentclass[12pt]{minimal}
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$3.15\times10^{12}$\end{document}3.15×1012 photons, corresponding to ∼4 mGy mean glandular dose, comparable to a two-view mammography. We modified our GPU-based MC simulation code to incorporate analytical descriptions of µCalcs of spherical shapes with diameters ranging from 0.1 to 0.4 mm, in 0.1 mm increments, into the voxelized phantom. A nichrome wire with 0.07 mm diameter was simulated to calculate the modulation transfer functions (MTFs). bCBCT images were reconstructed with the Feldkamp–Davis–Kress algorithm, and image quality and µCalc detection performance were evaluated. Main results. EID-bCBCT had more profound image noise due to electronic noise. The image intensity standard deviations estimated within a region of interest were 0.055 cm−1 for EID-bCBCT and 0.038 cm−1 for PCD-bCBCT, respectively. µCalcs and breast anatomy such as ligaments were more visible in the PCD-bCBCT images. The 10% MTF cutoffs were 5.5 and 9.5 lp mm−1 for EID-bCBCT and PCD-bCBCT, respectively. Contrast-to-noise ratio ranged in 1.20–9.13 for EID-bCBCT and 3.07–14.74 for PCD-bCBCT, depending on µCalc sizes. Significance. We compared EID- and PCD-based bCBCT for µCalc detection using GPU-based MC simulations in a clinically realistic setting. Our results demonstrate a potential advantage of PCD-bCBCT for this detection task.