Deep-learning reconstruction for the evaluation of lumbar spinal stenosis in computed tomography

Abstract

To compare the quality and interobserver agreement in the evaluation of lumbar spinal stenosis (LSS) on computed tomography (CT) images between deep-learning reconstruction (DLR) and hybrid iterative reconstruction (hybrid IR). This retrospective study included 30 patients (age, 71.5 ± 12.5 years; 20 men) who underwent unenhanced lumbar CT. Axial and sagittal CT images were reconstructed using hybrid IR and DLR. In the quantitative analysis, a radiologist placed regions of interest within the aorta and recorded the standard deviation of the CT attenuation (i.e., quantitative image noise). In the qualitative analysis, 2 other blinded radiologists evaluated the subjective image noise, depictions of structures, overall image quality, and degree of LSS. The quantitative image noise in DLR (14.8 ± 1.9/14.2 ± 1.8 in axial/sagittal images) was significantly lower than that in hybrid IR (21.4 ± 4.4/20.6 ± 4.0) (P < .0001 for both, paired t test). Subjective image noise, depictions of structures, and overall image quality were significantly better with DLR than with hybrid IR (P < .006, Wilcoxon signed-rank test). Interobserver agreements in the evaluation of LSS (with 95% confidence interval) were 0.732 (0.712–0.751) and 0.794 (0.781–0.807) for hybrid IR and DLR, respectively. DLR provided images with improved quality and higher interobserver agreement in the evaluation of LSS in lumbar CT than hybrid IR.

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

“…A higher interobserver agreement in the evaluation of ILD for DLR would have been resulted from an improved image quality. A better image noise reduction can be achieved compared to HIR in various regions such as the head, 19,27 vertebra, 21,23,24 and abdomen 20,22 as well as the chest. 25 Our study result corroborates with other studies, which revealed a significantly reduced quantitative image noise in DLR compared to HIR.…”

“…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.…”

Detection of Vertebral Mass and Diagnosis of Spinal Cord Compression in Computed Tomography With Deep Learning Reconstruction: Comparison With Hybrid Iterative Reconstruction

“…Most studies regarding the application of new reconstruction algorithms to spinal CT, have focused on the depiction of high-contrast structures, improving the visualization of the Adamkiewicz artery on CT angiography 6,7 and reducing the radiation dose without compromising the visualization of bone. 16 Recently, studies on the application of DLR for evaluating degenerative changes in the cervical 17 or lumbar 18 spine on unenhanced CT have been published. According to them, noise reduction due to DLR improved visualization of structures in the spinal canal region, which helped improve the interobserver agreement (.739 and .794 for the cervical and lumbar spine, respectively) for the evaluation of spinal canal stenosis due to bulging disc/ degenerative changes compared to hybrid-IR (.704 and .732 for the cervical and lumbar spine, respectively).…”

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

“…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.…”