技术
对比噪声比
层析合成
数字乳腺摄影术
光传递函数
对比度(视觉)
探测量子效率
图像质量
噪音(视频)
人工智能
计算机科学
计算机视觉
乳腺摄影术
图像分辨率
医学物理学
乳腺癌
医学
光学
图像(数学)
物理
内科学
癌症
作者
Patrizio Barca,Rocco Lamastra,Raffaele M. Tucciariello,Antonio Claudio Traino,Carolina Marini,Giacomo Aringhieri,Davide Caramella,Maria Evelina Fantacci
标识
DOI:10.1007/s13246-020-00948-2
摘要
Digital breast tomosynthesis (DBT) has recently gained interest both for breast cancer screening and diagnosis. Its employment has increased also in conjunction with digital mammography (DM), to improve cancer detection and reduce false positive recall rate. Synthetic mammograms (SMs) reconstructed from DBT data have been introduced to replace DM in the DBT + DM approach, for preserving the benefits of the dual-acquisition modality whilst reducing radiation dose and compression time. Therefore, different DBT models have been commercialized and the effective potential of each system has been investigated. In particular, wide-angle DBT was shown to provide better depth resolution than narrow-angle DBT, while narrow-angle DBT allows better identification of microcalcifications compared to wide-angle DBT. Given the increasing employment of SMs as supplement to DBT, a comparison of image quality between SMs obtained in narrow-angle and wide-angle DBT is of practical interest. Therefore, the aim of this phantom study was to evaluate and compare the image quality of SMs reconstructed from 15° (SM15) and 40° (SM40) DBT in a commercial system. Spatial resolution, noise and contrast properties were evaluated through the modulation transfer function (MTF), noise power spectrum, maps of signal-to-noise ratio (SNR), image contrast, contrast-to-noise ratio (CNR) and contrast-detail (CD) thresholds. SM40 expressed higher MTF than SM15, but also lower SNR and CNR levels. SM15 and SM40 were characterized by slight different texture, and a different behavior in terms of contrast was found. SM15 provided better CD performances than SM40. These results suggest that the employment of wide/narrow-angle DBT + SM images should be optimized based on the specific image task.
科研通智能强力驱动
Strongly Powered by AbleSci AI