Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography

• Published in: Japanese journal of radiology 2022-05, Vol.40 (5), p.476-483
• Main Authors: Yasaka, Koichiro, Akai, Hiroyuki, Sugawara, Haruto, Tajima, Taku, Akahane, Masaaki, Yoshioka, Naoki, Kabasawa, Hiroyuki, Miyo, Rintaro, Ohtomo, Kuni, Abe, Osamu, Kiryu, Shigeru
• Format: Article
• Language: English
• Subjects: Aged; Deep Learning; Female; Humans; Imaging; Magnetic Resonance Angiography; Male; Medicine; Medicine & Public Health; Middle Aged; Nuclear Medicine; Original; Original Article; Radiology; Radiotherapy; Retrospective Studies; Signal-To-Noise Ratio
• ISSN: 1867-1071; 1867-108X
• DOI: 10.1007/s11604-021-01225-2

Purpose The purpose of this study was to evaluate whether deep learning reconstruction (DLR) improves the image quality of intracranial magnetic resonance angiography (MRA) at 1.5 T. Materials and methods In this retrospective study, MRA images of 40 patients (21 males and 19 females; mean age, 65.8 ± 13.2 years) were reconstructed with and without the DLR technique (DLR image and non-DLR image, respectively). Quantitative image analysis was performed by placing regions of interest on the basilar artery and cerebrospinal fluid in the prepontine cistern. We calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for analyses of the basilar artery. Two experienced radiologists evaluated the depiction of structures (the right internal carotid artery, right ophthalmic artery, basilar artery, and right superior cerebellar artery), artifacts, subjective noise and overall image quality in a qualitative image analysis. Scores were compared in the quantitative and qualitative image analyses between the DLR and non-DLR images using Wilcoxon signed-rank tests. Results The SNR and CNR for the basilar artery were significantly higher for the DLR images than for the non-DLR images ( p