Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

Abstract

Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

“…These findings are in line with the current literature which shows that DLIR algorithms achieve superior image quality for head CT compared to IR-based algorithms [23][24][25]29]. We confirm the findings from a similar study by Kim et al [23].…”

“…Our study confirms the findings by Oostveen et al, who detected lower image noise and improved gray-white matter differentiation of DLIR compared to IR-based image reconstruction algorithms for noncontrast head CT [ 25 ]. However, they included patients who underwent a head CT scan for various indications and did not incorporate intracranial pathology in their image quality assessment.…”

“…As novel image reconstruction algorithms are put on the market, it is important to conduct an independent assessment of their effect on image quality. Recent studies have shown that DLIR algorithms improve head CT image quality compared to IR [23][24][25]. However, none of these studies has evaluated how a DLIR algorithm performs compared to an IR algorithm on noncontrast head CT from a cohort of multitrauma patients presenting to a level 1 trauma unit.…”

Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction

“…There are several studies on other anatomic parts and the phantom. The results of this study are consistent with those of previous studies on cerebral non-contrast CT [ 12 ], coronary CTA [ 20 ], pulmonary CTA [ 21 ] abdominal CT [ 11 , 22 ], and phantom [ 8 ]. The image noise, CNR, and/or SNR of DLR were improved compared with those of HIR and/or MBIR, while none of the previous studies compared DLR with FBP in vivo or discussed the blur metrics of sharpness.…”

First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels

“…[32][33][34] Therefore, it is hoped that the negative aspects of iterative image reconstruction in EID-CT can be overcome with the use of AI-based image reconstruction techniques in the future. [35][36][37] Overall, the AI-CAD system proved to be an applicable tool in nodule detection in PCD-CT showing comparable sensitivity for pulmonary nodule detection to EID-CT with the HR-mode. As expected, the false-positive rate increased at lower radiation dose levels.…”