Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.
Purpose: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learning-based reconstruction have shown the potential to...
| Published in: | Neuroradiology Vol. 67; no. 5; pp. 1203 - 1214 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | algorithm diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
May2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185594526&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185594526 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: May2025 vid: 67 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185594526 183747493 185594526 185594526 10.1007/s00234-025-03564-7 185594526 ppf: 1203 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T. aug: au: Jeon, Young Hun Park, Chanrim Lee, Kyung Hoon Choi, Kyu Sung Lee, Ji Ye Hwang, Inpyeong Yoo, Roh-Eul Yun, Tae Jin Choi, Seung Hong Kim, Ji-Hoon Sohn, Chul-Ho Kang, Koung Mi affil: https://ror.org/01z4nnt86 Seoul National University Hospital, Seoul, Republic of Korea sug: subj: Imaging, Three-Dimensional Magnetic Resonance Angiography Methods Cerebrovascular Disorders Diagnosis Skull Radiography Deep Learning Radiographic Image Enhancement Human China Male Female Middle Age Aged Retrospective Design Record Review Regression Radiographic Image Interpretation, Computer-Assisted Cerebrovascular Disorders Radiography Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Purpose: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learning-based reconstruction have shown the potential to improve image quality and reduce scan times. This study aimed to evaluate the effectiveness of accelerated intracranial TOF-MRA using deep learning-based image enhancement (TOF-DL) compared to conventional TOF-MRA (TOF-Con) at both 3-T and 1.5-T. Materials and methods: In this retrospective study, patients who underwent both conventional and 40% accelerated TOF-MRA protocols on 1.5-T or 3-T scanners from July 2022 to March 2023 were included. A commercially available DL-based image enhancement algorithm was applied to the accelerated MRA. Quantitative image quality assessments included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), and vessel sharpness (VS), while qualitative assessments were conducted using a five-point Likert scale. Cohen's d was used to compare the quantitative image metrics, and a cumulative link mixed regression model analyzed the readers' scores. Results: A total of 129 patients (mean age, 64 years ± 12 [SD], 99 at 3-T and 30 at 1.5-T) were included. TOF-DL showed significantly higher SNR, CNR, CR, and VS compared to TOF-Con (CNR = 183.89 vs. 45.58; CR = 0.63 vs. 0.59; VS = 0.73 vs. 0.61; all p < 0.001). The improvement in VS was more pronounced at 1.5-T (Cohen's d = 2.39) compared to 3-T HR and routine (Cohen's d = 0.83 and 0.75, respectively). TOF-DL also outperformed TOF-Con in qualitative image parameters, enhancing the visibility of small- and medium-sized vessels, regardless of the degree of resolution and field strength. TOF-DL showed comparable diagnostic accuracy (AUC: 0.77–0.85) to TOF-Con (AUC: 0.79–0.87) but had higher specificity for steno-occlusive lesions. CONCLUSIONS: Accelerated intracranial MRA with deep learning-based reconstruction reduces scan times by 40% and significantly enhances image quality over conventional TOF-MRA at both 3-T and 1.5-T. pubtype: Academic Journal doctype: algorithm diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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