ArticleActa oncologica (Stockholm, Sweden)2025
Image quality assessment of photon-counting CT for patients with prostate cancer receiving radiotherapy.
Article in Acta oncologica (Stockholm, Sweden), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
background and purposePhoton-counting computed tomography (PCCT) offers enhanced image quality, including improvements in contrast, spatial resolution, and noise reduction. In radiotherapy (RT), optimal image quality is critical for accurate tumor and organ-at-risk delineation. However, reconstruction parameter selection often relies on subjective assessment. This study investigates whether quantitative image quality metrics, particularly contrast-to-noise ratio (CNR), can systematically guide PCCT reconstruction parameter optimization for prostate cancer RT planning. MATERIAL AND
methodsAn anthropomorphic abdomen phantom (QRM, Möhrendorf, Germany) and five patients with prostate cancer undergoing RT were scanned on a Naeotom Alpha PCCT (Siemens Healthineers, Forchheim, Germany). Reconstructions were performed across a range of kernel types, sharpness levels, and virtual monoenergetic image (VMI) energies, with the CNR calculated for each reconstruction. Additionally, a multidisciplinary expert panel qualitatively assessed a subset of reconstructions for two patients to compare with the quantitative findings.
resultsSofter kernels, particularly Br36 and Qr36, combined with lower VMI energies of 40 keV, consistently produced the highest CNR values in both phantom and patient datasets. The qualitative assessment generally supported the quantitative results, with minor deviations likely reflecting the experts' preference for a more familiar image appearance.
interpretationQuantitative metrics such as CNR can reliably identify optimal PCCT reconstruction settings for prostate cancer RT, favoring lower VMI energies and softer reconstruction kernels. These findings were consistent across phantom and patient data and were supported by expert evaluations, indicating that a quantitative approach can effectively guide protocol development and reduce reliance on subjective image assessment.
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