Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease
Purpose To assess the quality of four images obtained using single-breath-hold (SBH), single-shot fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based reconstruction (DLR) in patients with Crohn’s disease. Materials and Methods This study included 61 pa...
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Format: | Article |
Language: | English |
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The Korean Society of Radiology
2023-11-01
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Series: | Journal of the Korean Society of Radiology |
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Online Access: | https://doi.org/10.3348/jksr.2023.0008 |
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author | Eun Joo Park Yedaun Lee Joonsung Lee |
author_facet | Eun Joo Park Yedaun Lee Joonsung Lee |
author_sort | Eun Joo Park |
collection | DOAJ |
description | Purpose To assess the quality of four images obtained using single-breath-hold (SBH), single-shot
fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based
reconstruction (DLR) in patients with Crohn’s disease.
Materials and Methods This study included 61 patients who underwent MR enterography (MRE) for
Crohn’s disease. The following images were compared: SBH-SSFSE with (SBH-DLR) and without (SBHconventional
reconstruction [CR]) DLR and MBH-SSFSE with (MBH-DLR) and without (MBH-CR) DLR.
Two radiologists independently reviewed the overall image quality, artifacts, sharpness, and motionrelated
signal loss using a 5-point scale. Three inflammatory parameters were evaluated in the ileum,
the terminal ileum, and the colon. Moreover, the presence of a spatial misalignment was evaluated.
Signal-to-noise ratio (SNR) was calculated at two locations for each sequence.
Results DLR significantly improved the image quality, artifacts, and sharpness of the SBH images. No
significant differences in scores between MBH-CR and SBH-DLR were detected. SBH-DLR had the
highest SNR (p < 0.001). The inter-reader agreement for inflammatory parameters was good to excellent
(κ = 0.76–0.95) and the inter-sequence agreement was nearly perfect (κ = 0.92–0.94). Misalignment
artifacts were observed more frequently in the MBH images than in the SBH images (p < 0.001).
Conclusion SBH-DLR demonstrated equivalent quality and performance compared to MBH-CR. Furthermore, it can be acquired in less than half the time, without multiple BHs and reduce slice misalignments. |
first_indexed | 2024-03-09T14:22:37Z |
format | Article |
id | doaj.art-51f1be12a7da4efa8817d3d555437155 |
institution | Directory Open Access Journal |
issn | 2951-0805 |
language | English |
last_indexed | 2024-03-09T14:22:37Z |
publishDate | 2023-11-01 |
publisher | The Korean Society of Radiology |
record_format | Article |
series | Journal of the Korean Society of Radiology |
spelling | doaj.art-51f1be12a7da4efa8817d3d5554371552023-11-28T09:10:55ZengThe Korean Society of RadiologyJournal of the Korean Society of Radiology2951-08052023-11-0184613091323https://doi.org/10.3348/jksr.2023.0008Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s DiseaseEun Joo ParkYedaun LeeJoonsung LeePurpose To assess the quality of four images obtained using single-breath-hold (SBH), single-shot fast spin-echo (SSFSE) and multiple-breath-hold (MBH) SSFSE with and without deep-learning based reconstruction (DLR) in patients with Crohn’s disease. Materials and Methods This study included 61 patients who underwent MR enterography (MRE) for Crohn’s disease. The following images were compared: SBH-SSFSE with (SBH-DLR) and without (SBHconventional reconstruction [CR]) DLR and MBH-SSFSE with (MBH-DLR) and without (MBH-CR) DLR. Two radiologists independently reviewed the overall image quality, artifacts, sharpness, and motionrelated signal loss using a 5-point scale. Three inflammatory parameters were evaluated in the ileum, the terminal ileum, and the colon. Moreover, the presence of a spatial misalignment was evaluated. Signal-to-noise ratio (SNR) was calculated at two locations for each sequence. Results DLR significantly improved the image quality, artifacts, and sharpness of the SBH images. No significant differences in scores between MBH-CR and SBH-DLR were detected. SBH-DLR had the highest SNR (p < 0.001). The inter-reader agreement for inflammatory parameters was good to excellent (κ = 0.76–0.95) and the inter-sequence agreement was nearly perfect (κ = 0.92–0.94). Misalignment artifacts were observed more frequently in the MBH images than in the SBH images (p < 0.001). Conclusion SBH-DLR demonstrated equivalent quality and performance compared to MBH-CR. Furthermore, it can be acquired in less than half the time, without multiple BHs and reduce slice misalignments.https://doi.org/10.3348/jksr.2023.0008crohn’s diseasemagnetic resonance imagingt2-weighted-imagingdeep learning reconstruction |
spellingShingle | Eun Joo Park Yedaun Lee Joonsung Lee Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease Journal of the Korean Society of Radiology crohn’s disease magnetic resonance imaging t2-weighted-imaging deep learning reconstruction |
title | Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease |
title_full | Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease |
title_fullStr | Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease |
title_full_unstemmed | Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease |
title_short | Impact of Deep-Learning Based Reconstruction on Single-Breath-Hold, Single-Shot Fast Spin-Echo in MR Enterography for Crohn’s Disease |
title_sort | impact of deep learning based reconstruction on single breath hold single shot fast spin echo in mr enterography for crohn s disease |
topic | crohn’s disease magnetic resonance imaging t2-weighted-imaging deep learning reconstruction |
url | https://doi.org/10.3348/jksr.2023.0008 |
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