Objective This study entailed a comparative analysis of the image quality of lung computed tomography (CT) reconstructed using the Precise Image (PI) algorithm and the filtered back projection (FBP) reconstruction algorithm. Further, the clinical value of the PI algorithm in lung CT imaging was evaluated.

Methods A total of 47 lung CT scans were retrospectively collected, and plain scan images were reconstructed using both the PI and FBP algorithms. The PI algorithm was applied using three reconstruction settings, namely, low (sharp), medium (standard), and high (smooth). Lung and soft tissue window images were extracted from both reconstruction algorithms, with low and medium settings, respectively, used for the PI algorithm. Two radiologists evaluated the four sets of lung window images and the two sets of soft tissue window images, assigned scores, and collected the image data extracted from each group. Statistical analysis was performed using SPSS.

Results Significant differences were observed among the four sets of lung window images in terms of standard deviation (SD), contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR). Similarly, CT value, SD, CNR, and SNR of the soft tissue window images differed significantly between the two groups. In the lung window, the low-smooth group exhibited the lowest SD and the highest SNR and CNR values, while the low-FBP group showed the highest SD and the lowest SNR and CNR values. In the soft tissue window, the SD of the medium-standard group was significantly lower than that of the medium-FBP group. Subjective evaluation indicated that the medium-standard group achieved the highest image quality, while the low-standard group demonstrated superior lesion visibility and diagnostic confidence. The low-FBP group received the lowest subjective scores.

Conclusion Compared with FBP, the PI algorithm significantly reduces image noise and artifacts, resulting in improved image quality. Among the three reconstruction settings, the standard mode yielded the highest subjective scores, highlighting its clinical value in lung CT imaging.