Abstract
Background
To evaluate the clinical performance of two deep learning methods, utilizing real clinical pairs and simulated datasets, for fast whole-body scintigraphy.
Methods
This prospective study enrolled 83 patients with suspected bone metastasis. All patients received SPECT whole-body scintigraphy (WBS) at the speed of 20cm/min (1x), 40cm/min (2x), 60cm/min (3x). Two deep learning models were introduced to generate high-quality images from fast scans, designated as 2x-real, 3x-real (from real model), and 2x-simu, 3x-simu (from simulated model). A 5-point Likert scale was utilized to evaluate the image quality of each acquisition. Accuracy, sensitivity, specificity, and ROC-AUC were used to evaluate the diagnostic efficacy. Learned perceptual image patch similarity (LPIPS) and fréchet inception distance (FID) were used to assess image quality. Additionally, count-level consistency of WBS was also compared.
Results
Subjective assessments indicated that 1x images exhibited the highest general image quality (Likert score: 4.40 ± 0.45). 2x-real, 2x-simu and 3x-real, 3x-simu images displayed significantly superior quality than those of 2x and 3x image respectively (Likert scores: 3.46 ± 0.47, 3.79 ± 0.55 vs. 2.92 ± 0.41, P < 0.0001;. 2.69 ± 0.40, 2.61 ± 0.41 vs. 1.36 ± 0.51, P < 0.0001). Notably, the quality of 2x-real images was inferior to those of 2x-simu (Likert scores: 3.46 ± 0.47 vs. 3.79 ± 0.55, P = 0.001). The diagnostic efficacy of 2x-real, 2x-simu was indistinguishable from 1x image (accuracy: 81.2%, 80.7% vs. 84.3%; sensitivity: 77.27%, 77.27% vs. 87.18%;specificity: 87.18%, 84.63% vs. 87.18%. all P > 0.05), while 3x-real, 3x-simu had better diagnostic efficacy than 3x (accuracy:65.1%, 66.35% vs. 84.3%; sensitivity: 63.64%, 63.64% vs. 87.18%; specificity: 66.67%, 69.23% vs. 87.18%, all P < 0.05).. Objectively, both real model and simulated model significantly enhanced image quality from the accelerated scans (FID: 0.15 ± 0.18, 0.18 ± 0.18 vs. 0.47 ± 0.34, P < 0.05༛LPIPS: 0.17 ± 0.05, 0.16 ± 0.04 vs. 0.19 ± 0.05, P < 0.05). The count-level consistency with the 1x images was excellent for 2x-real, 3x-real, 2x-simu, and 3x-simu (P < 0.0001).
Conclusions
The ultra-fast 2x speed (real and simulated) image could achieve comparable diagnostic value to those of standard acquisition, and the simulation algorithm could not necessarily reflect the real data.