衰减
衰减校正
核医学
Spect成像
窗口(计算)
计算机科学
数学
医学
物理
光学
操作系统
作者
Yuan Chen,P. Hendrik Pretorius,Yongyi Yang,Michael A. King,Clifford Lindsay
标识
DOI:10.1088/1361-6560/ad8b09
摘要
Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate attenuation correction (AC) in cardiac perfusion SPECT imaging. Typically, DL models take inputs from initial reconstructed SPECT images, which are performed on the photopeak window and often also on scatter windows. While prior studies have demonstrated improvements in DL performance when scatter window images are incorporated into the DL input, the comprehensive analysis of the impact of employing different scatter windows remains unassessed. Additionally, existing research mainly focuses on applying DL to SPECT scans obtained at clinical standard count levels. This study aimed to assess utilities of DL from two aspects: (1) investigating the impact when different scatter windows were used as input to DL, and (2) evaluating the performance of DL when applied on SPECT scans acquired at a reduced count level.
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