Deep learning‑based ultra-high-resolution CT imaging of viral pneumonia at admission and after discharge

Background: Advancement in deep learning has introduced significant potential for enhancing CT image quality without increasing patient radiation exposure. In this study, we sought to compare deep learning‑based ultra-high-resolution CT (UHRCT-DL) findings of viral pneumonia at admission and after discharge with that of HRCT images. Methods: A total of 51 inpatients (mean age 66.78 years; 33 males) of viral pneumonia underwent 102 CT scans at admission and after discharge. A deep learning-based super-resolution model, incorporating a dual-branch architecture for super-resolution and gradient guidance, was used to generate UHRCT-DL. UHRCT-DL and HRCT images were systematically reviewed by two radiologists for viral pneumonia CT findings, including ground-glass opacity (GGO), reticulation, tree-in-bud opacities, consolidation, linear bands, bronchiectasis, and bronchiectasis. Subjective CT image quality was evaluated using a five-point Likert scale (− 2 to 2) by the two radiologists and objective CT image quality was measured by lung signal-to-noise ratios (SNRs). Results: The score of clarity of CT findings was significantly higher on UHRCT-DL for all CT findings at admission and after discharge. Compared with HRCT as reference image, the most frequently observed additional/different CT findings on UHRCT-DL at admission were crazy paving pattern (14/51, 27%) and tree-in-bud opacities (8/31, 26%), whereas reticulations (15/51, 29%) and bronchiolectasis (12/44, 27%) were most observed additional/different CT findings after discharge. The subjective and objective image quality of UHRCT-DL was superior to that of HRCT. UHRCT-DL algorithm significantly lowered the level of image noise and improved SNR (19.96 ± 6.46 vs. 41.35 ± 11.49, p < 0.001). Conclusions: The deep learning‑based UHRCT provided a more precise depiction of CT features of viral pneumonia, that better reflects the inflammatory changes during acute phase and early fibrotic changes during recovery.