Comparison of Deep-Learning Image Reconstruction With Hybrid Iterative Reconstruction for Evaluating Lung Nodules With High-Resolution Computed Tomography

Abstract

ObjectiveThis study aimed to investigate the impact of deep-learning reconstruction (DLR) on the detailed evaluation of solitary lung nodule using high-resolution computed tomography (HRCT) compared with hybrid iterative reconstruction (hybrid IR).MethodsThis retrospective study was approved by our institutional review board and included 68 consecutive patients (mean ± SD age, 70.1 ± 12.0 years; 37 men and 31 women) who underwent computed tomography between November 2021 and February 2022. High-resolution computed tomography images with a targeted field of view of the unilateral lung were reconstructed using filtered back projection, hybrid IR, and DLR, which is commercially available. Objective image noise was measured by placing the regions of interest on the skeletal muscle and recording the SD of the computed tomography attenuation. Subjective image analyses were performed by 2 blinded radiologists taking into consideration the subjective noise, artifacts, depictions of small structures and nodule rims, and the overall image quality. In subjective analyses, filtered back projection images were used as controls. Data were compared between DLR and hybrid IR using the paired t test and Wilcoxon signed-rank sum test.ResultsObjective image noise in DLR (32.7 ± 4.2) was significantly reduced compared with hybrid IR (35.3 ± 4.4) (P < 0.0001). According to both readers, significant improvements in subjective image noise, artifacts, depictions of small structures and nodule rims, and overall image quality were observed in images derived from DLR compared with those from hybrid IR (P < 0.0001 for all).ConclusionsDeep-learning reconstruction provides a better high-resolution computed tomography image with improved quality compared with hybrid IR.

Deep learning reconstruction for high-resolution computed tomography images of the temporal bone: comparison with hybrid iterative reconstruction

“…One reason for the improved depiction of temporal bone structures with DLR is likely related to reduced image noise. Several studies of DLR reconstruction in other body regions have found that image noise is significantly reduced in DLR images compared with HIR images [ 9 – 11 , 19 – 25 ]. Our study, which assessed image noise in the temporal bone region, accords with those earlier studies.…”

“…Full iterative reconstruction reduced image noise considerably and improved the quality of CT images [ 3 – 8 ]; however, the associated prolonged image reconstruction time hampered the use of this algorithm in daily clinical practice. An alternative hybrid iterative reconstruction (HIR) algorithm that provides some reduction in image noise within a clinically feasible reconstruction time was subsequently widely accepted [ 9 – 11 ].…”

“…In supervised training of DLR, low-quality data and high-quality images are respectively used as the input and reference data. A trained DLR is known to reduce image noise [ 19 ], thereby improving the quality of CT images in regions as varied as the head [ 20 , 21 ], chest [ 9 ], abdomen [ 10 , 19 , 22 , 23 ], and vertebrae [ 11 , 24 , 25 ]. We therefore hypothesized that DLR can also improve the quality of high-resolution CT of the temporal bone.…”

Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation

“…To our best knowledge, no study has evaluated whether DLR could reduce artifacts caused by arms positioned alongside the torso. As there has been a previous report that artifacts of the lung could be reduced with DLR, 14 we hypothesized that armrelated artefacts could also be reduced with DLR.…”

Deep-Learning Reconstruction of High-Resolution CT Improves Interobserver Agreement for the Evaluation of Pulmonary Fibrosis

“…It was trained with paired data, one with low noise and high spatial resolution and another with noise contamination and learned the difference between noise and signal. 17,18 It has been reported that the appropriate algorithm of DLR can improve image quality of CT images in various body regions [19][20][21][22][23][24] including the lung, 25 we hypothesized that this reconstruction algorithm could also potentially improve the interobserver agreement in the evaluation of honeycombing in patients with ILD.…”