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

Zlatan Alagic,Chikako Suzuki,Stig Wallgren,Seppo K Koskinen,Kolbeinn Halldorsson,Johan Helmenkamp,Jacqueline Diaz Cardenas,Vitali Grozman

doi:10.1007/s10140-021-02012-2

Abstract

PurposeTo 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.MethodsHead 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.ResultsDLIR-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.ConclusionThe 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.

Highlights

There is wide consensus that noncontrast head CT is the initial imaging modality of choice for acute moderate to severe traumatic brain injury
We have demonstrated that TrueFidelity outperforms adaptive statistical iterative reconstruction-Veo (ASiR-V) regarding both quantitative and qualitative image quality parameters
The image quality increased with higher deep learning–based image reconstruction algorithm (DLIR) strengths

Summary

Introduction

There is wide consensus that noncontrast head CT is the initial imaging modality of choice for acute moderate to severe traumatic brain injury. CT without intravenous contrast agents has intrinsically relatively limited soft tissue contrast resolution [5, 6], e.g., in the brain parenchyma where the difference in CT attenuation between gray and white matter is relatively small. The diagnosis of some intracranial pathologies relies on the detection of the discreet alteration in attenuation that they cause between the gray and white matter [5]. These factors can make the interpretation of noncontrast trauma head CT challenging

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