Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction.

Chuluunbaatar Otgonbaatar,Dae Hyun Hwang,Hackjoon Shim,Jae-Kyun Ryu,Jung Wook Seo,Seonkyu Kim

doi:10.31083/j.jin2004097

Abstract

To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions. The subjective image quality score was significantly higher for the deep learning reconstruction than hybrid iterative reconstruction and filtered-back-projection images. The deep learning reconstruction markedly improved the reduction of blooming artifacts in surgical clips and coiled aneurysms. The deep learning reconstruction method generally improves the image quality of brain computed tomography angiography in terms of objective measurement and subjective grading compared with filtered-back-projection and hybrid iterative reconstruction. Especially, deep learning reconstruction is deemed advantageous for better depiction of small vessels compared to filtered-back projection and hybrid iterative reconstruction.

Highlights

Brain multidetector-row Computed Tomography Angiography (CTA) is a suitable noninvasive imaging modality frequently used in cases of vascular diseases including aneurysm, vessel dissection, vascular malformations, stroke, and tumors [1]
Several studies [3,4,5] reported that model-based iterative reconstruction (MBIR) improves the delineation of small vascular structures with high image quality and spatial resolution compared to Hybrid Iterative Reconstruction (IR)
Clinical studies have confirmed that the low radiation dose used during hybrid iterative reconstruction and deep learning reconstruction (DLR) implies patient safety while improving image quality; lower image noise, higher contrast-to-noise ratio (CNR), and lower blooming artifacts were observed [6,7,8,9]

Summary

Introduction

Brain multidetector-row Computed Tomography Angiography (CTA) is a suitable noninvasive imaging modality frequently used in cases of vascular diseases including aneurysm, vessel dissection, vascular malformations, stroke, and tumors [1]. The technical improvements with high spatial and temporal resolution enabled CTA to produce comparable image quality to conventional angiography and evaluate cerebrovascular diseases, especially intracranial aneurysms [2]. To visualize the intracranial vessels clearly and accurately, there should be a great deal to improve the image quality by developing image reconstruction algorithms. Several studies [3,4,5] reported that MBIR improves the delineation of small vascular structures with high image quality and spatial resolution compared to Hybrid IR. Clinical studies have confirmed that the low radiation dose used during hybrid iterative reconstruction and deep learning reconstruction (DLR) implies patient safety while improving image quality; lower image noise, higher contrast-to-noise ratio (CNR), and lower blooming artifacts were observed [6,7,8,9]

Methods

Results

Discussion

Conclusion