Abstract
PURPOSE: Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to prospectively evaluate the clinical utility of the unified data-driven respiratory motion correction (uRMC) algorithm utilizing deep learning neural networks for diagnosing upper abdominal lesions. METHODS: A total of 100 patients with suspected upper abdominal lesions who underwent (18)F-FDG PET/CT were enrolled. Two senior physicians independently conducted subjective visual assessments and semi-quantitative analyses of the PET/CT images before and after applying uRMC. Subjective visual evaluation parameters included overall image quality, PET-CT misalignment, and lesion distortion. Additionally, physicians identified involved upper-abdominal lesions in both images. Semi-quantitative metrics recorded for each lesion included maximum standardized uptake value (SUV(max)), metabolic tumor volume (MTV), tumor-to-background ratio (TBR), and horizontal-to-vertical ratio (HV_ratio) before and after correction. Percentage changes in lesion SUV(max) and MTV were calculated, and subgroup analyses were performed to assess the impact of lesion uptake, volume, and displacement on the performance of the uRMC algorithm. RESULTS: Compared to no motion correction (NMC) images, 78% (78/100) of patients demonstrated improved overall image quality after uRMC reconstruction, with 68.9% (155/225) of lesion showing improved PET-CT alignment and 64.0% (144/225) demonstrating reduced lesion distortion (all p < 0.001). The RM-corrected images exhibited a significantly higher SUV(max) (9.07 [6.45, 11.79] vs.7.46 [5.69, 10.00], p < 0.001) and TBR (3.65 [2.54, 4.98] vs. 3.17 [2.40, 4.38], p < 0.001). The number of detected lesions increased from 171 (NMC) to 181 (uRMC) in 62 patients, with 10 additional suspicious lesions identified in 14.5% (9/62) of cases. Moreover, 7 lesions in 9.7% (6/62) of patients exhibited improved PET-CT alignment after uRMC correction. The uRMC algorithm also significantly reduced lesion MTV (1359.6 [690.8, 3837.6] mm(3) vs.1710.5 [899.1, 4013.0] mm(3), p < 0.01) and HV_ratio (0.99 [0.82, 1.09] vs. 1.16 [1.02, 1.44], p < 0.01). Subgroup-based analyses revealed that uRMC outperformed NMC in detecting low-uptake and small-volume lesions, with SUV(max) improvements being more pronounced in lesions with larger displacement (17.8% vs. 9.8%, p < 0.001). CONCLUSION: Compared with conventional NMC reconstruction, the uRMC algorithm significantly enhances overall image quality, PET-CT alignment, and lesion delineation. Furthermore, it improves the detection of low-uptake and small-volume lesions in the upper abdomen, thereby increasing the accuracy and reliability of clinical diagnoses and supporting more informed therapeutic decision-making.