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

Autor: Pan Z; RadioDynamic Healthcare, Shanghai, China., Qi N; Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Meng Q; Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Pan B; RadioDynamic Healthcare, Shanghai, China., Feng T; Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Beijing, China., Zhao J; Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China., Gong NJ; Laboratory for Intelligent Medical Imaging, Tsinghua Cross-Strait Research Institute, Beijing, China.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2024 Aug; Vol. 51 (8), pp. 5414-5426. Date of Electronic Publication: 2024 Apr 23.
DOI: 10.1002/mp.17094
Abstrakt: 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-point Likert scale (5 =  excellent) .
Results: The proposed method reaches the state-of-the-art performance on FID and LPIPS with 0.583 and 0.176 for 2× fast scans and 0.583 and 0.185 for 3× fast scans. Clinic evaluation further demonstrated the restored images had a significant improvement compared to fast scan in image quality, technetium 99m-methyl diphosphonate (Tc-99 m MDP) distribution, artifacts, and diagnostic confidence.
Conclusions: Our method was validated for accelerating whole-body bone scans by introducing real clinical data. Confirmed by nuclear medicine physicians, the proposed method can effectively enhance image diagnostic value, demonstrating potential for efficient high-quality fast bone imaging in practical settings.
(© 2024 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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