Exploring the impact of super-resolution deep learning on MR angiography image quality.
Purpose: The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T. Methods: This retrospective study involved 35 pati...
| Publicado en: | Neuroradiology Vol. 66; no. 2; pp. 217 - 227 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174971235&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174971235 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Feb2024 vid: 66 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174971235 174436181 174971235 174971235 10.1007/s00234-023-03271-1 174971235 ppf: 217 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Exploring the impact of super-resolution deep learning on MR angiography image quality. aug: au: Hokamura, Masamichi Uetani, Hiroyuki Nakaura, Takeshi Matsuo, Kensei Morita, Kosuke Nagayama, Yasunori Kidoh, Masafumi Yamashita, Yuichi Ueda, Mitsuharu Mukasa, Akitake Hirai, Toshinori affil: https://ror.org/02cgss904 Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, 860-8556, Kumamoto, Japan sug: subj: Deep Learning Evaluation Magnetic Resonance Angiography Methods Radiographic Image Enhancement Image Processing, Computer Assisted Human Male Female Retrospective Design Record Review Basilar Artery Anterior Cerebral Artery Radiologists Quantitative Studies Qualitative Studies Wilcoxon Rank Sum Test Descriptive Statistics Male Female ab: Purpose: The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T. Methods: This retrospective study involved 35 patients who underwent intracranial TOF-MRA using a 3-T MRI system with SR-DLR based on k-space properties in October and November 2022. We reconstructed MRA with SR-DLR (matrix = 1008 × 1008) and MRA without SR-DLR (matrix = 336 × 336). We measured the signal-to-noise ratio (SNR), contrast, and contrast-to-noise ratio (CNR) in the basilar artery (BA) and the anterior cerebral artery (ACA) and the sharpness of the posterior cerebral artery (PCA) using the slope of the signal intensity profile curve at the half-peak points. Two radiologists evaluated image noise, artifacts, contrast, sharpness, and overall image quality of the two image types using a 4-point scale. We compared quantitative and qualitative scores between images with and without SR-DLR using the Wilcoxon signed-rank test. Results: The SNRs, contrasts, and CNRs were all significantly higher in images with SR-DLR than those without SR-DLR (p < 0.001). The slope was significantly greater in images with SR-DLR than those without SR-DLR (p < 0.001). The qualitative scores in MRAs with SR-DLR were all significantly higher than MRAs without SR-DLR (p < 0.001). Conclusion: SR-DLR with k-space properties can offer the benefits of increased spatial resolution without the associated drawbacks of longer scan times and reduced SNR and CNR in intracranial MRA. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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