Clinical Value of Ultra-Low-Dose High-Resolution CT Combined with Deep Learning Image Reconstruction in CT-Guided Percutaneous Lung Biopsy

Other scan parameters were kept identical across groups. CT attenuation values and standard deviations (SD) of the ascending aorta and chest wall fat were measured to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality, including lesion margin delineation, density homogeneity, and artifact severity, was independently assessed using a five-point Likert scale.

CT dose index volume (CTDIvol), dose–length product (DLP), effective dose (ED), and size-specific dose estimate (SSDE) were recorded and analyzed. Intergroup comparisons were performed using one-way ANOVA or non-parametric tests as appropriate.Results Inter-observer agreement for subjective image assessment was good (Kappa=0.705~0.865，P<0.05). Compared with the conventional-dose protocol, CTDIvol and SSDE were reduced by 61.8% and 80.0% in Groups B and C, respectively.

In both low- and ultra-low-dose protocols, images reconstructed with ClearInfinity at 60% and 80% demonstrated significantly higher SNR and CNR than those of the conventional protocol (P < 0.05). Image quality reconstructed with ClearInfinity at 60% was comparable to that of the conventional-dose group in terms of lesion margin visibility, density uniformity, and artifact control (P > 0.05) and was superior to other reconstruction strengths within the same dose group.Conclusion With an approximately 80% reduction in radiation dose, high-resolution reconstruction using a 1024 matrix combined with ClearInfinity deep learning reconstruction at 60% strength provides image quality sufficient for CT-guided lung biopsy.

This technique represents a safe and feasible imaging solution for ultra-low-dose CT-guided percutaneous lung biopsy.