Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis.

PURPOSE: To assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis. METHODS: Simulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction. RESULTS: At the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality ( P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures ( P = 0.002), in comparison with 100 mAs. CONCLUSIONS: Deep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.

Improved efficiency of CT interpretation using an automated lung nodule matching program.

OBJECTIVE: The purpose of this study was to assess the impact of an automated program on improvement in lung nodule matching efficiency. MATERIALS AND METHODS: Four thoracic radiologists independently reviewed two serial chest CT examinations from each of 57 patients. Each radiologist performed timed manual lung nodule matching. After 6 weeks, all radiologists independently repeated the timed matching portion using an automated nodule matching program. The time required for manual and automated matching was compared. The impact of nodule size and number on matching efficiency was determined. RESULTS: An average of 325 (range, 244-413) noncalcified solid pulmonary nodules was identified. Nodule matching was significantly faster with the automated program irrespective of the interpreting radiologist (p < 0.0001 for each). The maximal time saved with automated matching was 11.4 minutes (mean, 2.3 ± 2.0 minutes). Matching was faster in 56 of 57 cases (98.2%) for three readers and in 46 of 57 cases (80.7%) for one reader. There were no differences among readers with respect to the mean time saved per matched nodule (p > 0.5). The automated program achieved 90%, 90%, 79%, and 92% accuracy for the four readers. The improvement in efficiency for a given patient using the automated technique was proportional to the number of matched nodules (p < 0.0001) and inversely proportional to nodule size (p < 0.05). CONCLUSION: Use of the automated lung nodule matching program significantly improves diagnostic efficiency. The time saved is proportionate to the number of nodules identified and inversely proportional to nodule size. Adoption of such a program should expedite CT examination interpretation and improve report turnaround time.