Fast SPECT/CT planar bone imaging enabled by deep learning enhancement

作者：Zhenglin Pan, Na Qi, Qingyuan Meng, Boyang Pan, Tao Feng, Jun Zhao, Nan‐Jie Gong · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17094 · 被引用次数：7 · 研究领域：Medical Imaging Techniques and Applications、Advanced Radiotherapy Techniques、Dental Radiography and Imaging

BACKGROUND: The application of deep learning methods in rapid bone scintigraphy is increasingly promising for minimizing the duration of SPECT examinations. Recent works showed several deep learning models based on simulated data for the synthesis of high-count bone scintigraphy images from low-count counterparts. Few studies have been conducted and validated on real clinical pairs due to the misalignment inherent in multiple scan procedures. PURPOSE: To generate high quality whole-body bone images from 2× and 3× fast scans using deep learning based enhancement method. MATERIALS AND METHODS: Seventy-six cases who underwent whole-body bone scans were enrolled in this prospective study. All patients went through a standard scan at a speed of 20 cm/min, which followed by fast scans consisting of 2× and 3× accelerations at speeds of 40 and 60 cm/min. A content-attention image restoration approach based on Residual-in-Residual Dense Block (RRDB) is introduced to effectively recover high-quality images from fast scans with fine-details and less noise. Our approach is robust with misalignment introduced from patient's metabolism, and shows valid count-level consistency. Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID) are employed in evaluating the similarity to the standard bone images. To further prove our method practical in clinical settings, image quality of the anonymous images was evaluated by two experienced nuclear physicians on a 5-poi...