Abstract

Photon-counting computed tomography (PCCT) has emerged as a promising imaging modality with improved spatial resolution and contrast capabilities compared to conventional energy-integrating detector systems. These advantages present an opportunity for opportunistic detection of breast cancer in routine thoracic CT scans that include breast tissue. In this pilot study, we evaluated the feasibility of opportunistic breast cancer detection using clinical PCCT data through quantitative image analysis. Two patients with confirmed breast malignancies and corresponding thoracic PCCT scans were retrospectively analyzed. Dedicated reconstructions were performed with varying convolution kernels, field-of-view sizes, and virtual monoenergetic spectra. Image quality was assessed using signal-to-noise ratio (SNR), dose-normalized contrast-to-noise ratio (CNRD), and spatial resolution quantified via full width at half-maximum (FWHM). Results demonstrate a tradeoff between noise and spatial resolution across reconstruction parameters. While sharper kernels and lower monoenergetic bins increased noise and reduced CNRD, they significantly improved spatial resolution, achieving sub-millimeter feature detectability. In one patient, CNRD values remained above clinically acceptable thresholds across most reconstructions, supporting feasible lesion detectability under these conditions. These findings suggest that PCCT enables opportunistic visualization of breast cancer features in routine clinical scans, particularly when optimized reconstruction parameters are applied. Despite limitations including small sample size and substantial noise in reconstructions, this study provides proof-of-concept evidence supporting further investigation of PCCT for early breast cancer detection in clinical settings.

Committee Chair

Michelle Lee

Committee Members

Adrian Sanchez, Christine O'Brien

Degree

Master of Science (MS)

Author's Department

Biomedical Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Spring 4-21-2026

Language

English (en)

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