图像质量
迭代重建
对比度(视觉)
图像分辨率
人工智能
计算机科学
重建算法
氡变换
算法
相位对比成像
噪音(视频)
乳房成像
图像噪声
计算机视觉
医学
乳腺摄影术
物理
光学
图像(数学)
相衬显微术
内科学
乳腺癌
癌症
作者
Sandro Donato,Simone Caputo,Luca Brombal,Bruno Golosio,Renata Longo,Giuliana Tromba,Raffaele G. Agostino,Gianluigi Greco,Benedicta D. Arhatari,Chris Hall,Anton Maksimenko,Daniel M. Häusermann,Darren Lockie,Jane Fox,Beena Kumar,Sarah Lewis,Patrick Brennan,Harry M. Quiney,Seyedamir Tavakoli Taba,Timur E. Gureyev
出处
期刊:Medical Physics
[Wiley]
日期:2025-07-01
卷期号:52 (7): e17950-e17950
被引量:2
摘要
BACKGROUND: Phase-contrast breast CT imaging holds promise for improved diagnostic accuracy, but an optimal reconstruction algorithm must balance objective image quality metrics with subjective radiologist preferences. PURPOSE: This study systematically compares three reconstruction algorithms-filtered back projection (FBP), unified tomographic reconstruction (UTR), and customized simultaneous algebraic reconstruction technique (cSART)-to identify the most suitable approach for phase-contrast breast CT imaging. METHODS: Fresh mastectomy samples were scanned at the Australian synchrotron using monochromatic 32 keV X-rays, a mean glandular dose of 2 mGy, flat-panel detectors with 0.1 mm pixels, and 6-m distance between the rotation stage and the detector. Paganin's phase retrieval method was used in conjunction with all three CT reconstruction algorithms. Objective metrics, including spatial resolution, contrast, signal-to-noise, and contrast-to-noise, were evaluated alongside subjective assessments by seven experienced radiologists. Ratings included perceptible contrast, sharpness, noise, calcification visibility, and overall quality. RESULTS: cSART excelled in objective metrics, outperforming UTR and FBP. However, subjective evaluations favored FBP due to its higher image contrast, revealing a discrepancy between objective and subjective assessments. CONCLUSIONS: The findings highlight the contrast-focused nature of radiologists' subjective assessments and the potential of cSART for delivering superior objective image quality. These insights inform the development of hybrid evaluation tools and guide clinical translation for future live patient imaging studies.
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