Detecting Peripheral Nerves in the Elbow using Three-Dimensional Diffusion-Weighted PSIF Sequences: a Feasibility Pilot Study
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 나도민 | - |
dc.contributor.author | 류재일 | - |
dc.contributor.author | 홍석주 | - |
dc.contributor.author | 홍선화 | - |
dc.contributor.author | 윤민아 | - |
dc.contributor.author | 안경식 | - |
dc.contributor.author | 강창호 | - |
dc.contributor.author | 김백현 | - |
dc.date.accessioned | 2021-09-04T07:00:47Z | - |
dc.date.available | 2021-09-04T07:00:47Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2016 | - |
dc.identifier.issn | 2384-1095 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/90888 | - |
dc.description.abstract | Purpose: To analyze the feasibility of three-dimensional (3D) diffusion-weighted (DW) PSIF (reversed FISP [fast imaging with steady-state free precession]) sequence in order to evaluate peripheral nerves in the elbow. Materials and Methods: Ten normal, asymptomatic volunteers were enrolled (6 men, 4 women, mean age 27.9 years). The following sequences of magnetic resonance images (MRI) of the elbow were obtained using a 3.0-T machine: 3D DW PSIF, 3D T2 SPACE (sampling perfection with application optimized contrasts using different flip angle evolution) with SPAIR (spectral adiabatic inversion recovery) and 2D T2 TSE (turbo spin echo) with modified Dixon (m-Dixon) sequence. Two observers used a 5-point grading system to analyze the image quality of the ulnar, median, and radial nerves. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of each nerve were measured. We compared 3D DW PSIF images with other sequences using the Wilcoxon-signed rank test and Friedman test. Inter-observer agreement was measured using intraclass correlation coefficient (ICC) analysis. Results: The mean 5-point scores of radial, median, and ulnar nerves in 3D DW PSIF (3.9/4.2/4.5, respectively) were higher than those in 3D T2 SPACE SPAIR (1.9/2.8/2.8) and 2D T2 TSE m-Dixon (1.7/2.8/2.9) sequences (P < 0.05). The mean SNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR, but there was no difference between 3D DW PSIF and 2D T2 TSE m-Dixon in all of the three nerves. The mean CNR in 3D DW PSIF was lower than 3D T2 SPACE SPAIR and 2D T2 TSE m-Dixon in the median and ulnar nerves, but no difference among the three sequences in the radial nerve. Conclusion: The three-dimensional DW PSIF sequence may be feasible to evaluate the peripheral nerves around the elbow in MR imaging. However, further optimization of the image quality (SNR, CNR) is required. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | 대한자기공명의과학회 | - |
dc.title | Detecting Peripheral Nerves in the Elbow using Three-Dimensional Diffusion-Weighted PSIF Sequences: a Feasibility Pilot Study | - |
dc.title.alternative | Detecting Peripheral Nerves in the Elbow using Three-Dimensional Diffusion-Weighted PSIF Sequences: a Feasibility Pilot Study | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 홍석주 | - |
dc.identifier.doi | 10.13104/imri.2016.20.2.81 | - |
dc.identifier.bibliographicCitation | Investigative Magnetic Resonance Imaging, v.20, no.2, pp.81 - 87 | - |
dc.relation.isPartOf | Investigative Magnetic Resonance Imaging | - |
dc.citation.title | Investigative Magnetic Resonance Imaging | - |
dc.citation.volume | 20 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 81 | - |
dc.citation.endPage | 87 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002124292 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | 3T MR neurography | - |
dc.subject.keywordAuthor | 3D DW PSIF | - |
dc.subject.keywordAuthor | Elbow joint | - |
dc.subject.keywordAuthor | Peripheral nerve | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.