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

Abstract

Purpose To compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT. Methods Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa. Results DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader. Conclusion The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography

“…Two major CT vendors, GE Healthcare and Canon Medical System, had their DLIR algorithms cleared by the FDA, both based on a deep neural network, respectively, trained with highquality FBP images [13] and model-based IR datasets [12]. Both algorithms are under extensive investigation and are achieving promising results: Recent clinical studies have documented DLIR capability of generating images with lower image noise and superior image quality compared to IR; favorable results have been obtained in CCTA [17,[25][26][27], abdominal CT [28][29][30][31][32], chest CT examinations [33][34][35], and brain CT scans [36,37].…”

Value of vendor-agnostic deep learning image denoising in brain computed tomography: A multi-scanner study

“…Due to fast accessibility and short examination time, non-contrast computed tomography (ncCT) has an outstanding importance, especially in the field of head trauma diagnostics because of its high sensitivity for the detection of intracranial hemorrhage [1,2]. Yet, satisfying image quality, depiction of the gray and white matter, and thus reliable detection of small pathologies and lesions in cranial ncCT is rendered more difficult due to image noise and limited intrinsic differences in the brain parenchyma [3], especially while trying to leave the radiation dose at a reasonable level. With a constantly rising number of CT scans nationally and internationally [4,5,6], and thus increased accumulated radiation exposure and the risks of neoplasia that arise from it [7], new methods to reduce image noise, to increase image quality, and to enable a reduction in dose exposure for the patients have been introduced.…”

“…However, it is limited in its potential for the improvement of image quality, dose, and noise reduction because of an increase in noise proportional to the inverse square root of the radiation dose [10]. A further step in attempts to improve CT imaging is iterative reconstruction (IR), which renders higher image quality and significant noise reduction compared to FBP [3,11,12] but has recently been challenged by new deep learning denoising (DLD) algorithms for CT image reconstruction, which are already showing superior results compared to FBP as well as IR [8]. Several studies have examined the effect of these new algorithms compared to conventional reconstruction methods, concentrating on specific body regions (e. g. head, lung, liver) using vendor-specific solutions [3,13,14,15,16,17].…”

“…A further step in attempts to improve CT imaging is iterative reconstruction (IR), which renders higher image quality and significant noise reduction compared to FBP [3,11,12] but has recently been challenged by new deep learning denoising (DLD) algorithms for CT image reconstruction, which are already showing superior results compared to FBP as well as IR [8]. Several studies have examined the effect of these new algorithms compared to conventional reconstruction methods, concentrating on specific body regions (e. g. head, lung, liver) using vendor-specific solutions [3,13,14,15,16,17]. Yet, these denoising algorithms are limited to certain scanners from the same vendor and thereby exclude older scanners and scanners by others.…”

Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses

“…By improving the ability to identify anatomical structures, deep learning-based image reconstruction (DLIR; TrueFidelity, GE Healthcare) can significantly improve image quality compared with traditional model-based iterative reconstruction methods (5,7,8). DLIR has been applied to some phantom and clinical studies, providing satisfactory noise reduction and better image quality than those of ASiR-V (9)(10)(11). This study evaluated the impact of DLIR algorithms on renal CT image quality and lesions at a routine dose (120 kV) and a low dose (80 kV).…”