The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (kappa = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.

Automated summary

This study evaluated the effect of the deep learning model reconstruction (DLM) method on image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD). The findings showed that DLM improved image quality compared to ASiR-V and FBP, and there was almost perfect agreement in the diagnostic categorization of idiopathic pulmonary fibrosis (IPF) between the three reconstruction methods.