Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI
<b>Background/Objectives:</b> This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. <b>Methods:&l...
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MDPI AG
2025-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/5/595 |
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| author | Seung Ha Cha Yeo Eun Han Na Yeon Han Min Ju Kim Beom Jin Park Ki Choon Sim Deuk Jae Sung Seulki Yoo Patricia Lan Arnaud Guidon |
| author_facet | Seung Ha Cha Yeo Eun Han Na Yeon Han Min Ju Kim Beom Jin Park Ki Choon Sim Deuk Jae Sung Seulki Yoo Patricia Lan Arnaud Guidon |
| author_sort | Seung Ha Cha |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. <b>Methods:</b> This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIR<sup>TM</sup> Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. <b>Results:</b> DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. <b>Conclusions:</b> DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging. |
| format | Article |
| id | doaj-art-9695a120c14642deb4591e1e81a3dc2a |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-9695a120c14642deb4591e1e81a3dc2a2025-08-20T02:58:58ZengMDPI AGDiagnostics2075-44182025-02-0115559510.3390/diagnostics15050595Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRISeung Ha Cha0Yeo Eun Han1Na Yeon Han2Min Ju Kim3Beom Jin Park4Ki Choon Sim5Deuk Jae Sung6Seulki Yoo7Patricia Lan8Arnaud Guidon9Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaDepartment of Radiology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of KoreaGE Healthcare, 416 Hangang-daero, Seoul 04637, Republic of KoreaMR Collaborations & Clinical Solutions, GE HealthCare, Menlo Park, CA 94025, USAMR Collaborations & Clinical Solutions, GE Healthcare, Boston, MA 02142, USA<b>Background/Objectives:</b> This study compared the image quality of conventional multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) and deep learning MUSE-DWI with that of vendor-specific deep learning (DL) reconstruction applied to bladder MRI. <b>Methods:</b> This retrospective study included 57 patients with a visible bladder mass. DWI images were reconstructed using a vendor-provided DL algorithm (AIR<sup>TM</sup> Recon DL; GE Healthcare)—a CNN-based algorithm that reduces noise and enhances image quality—applied here as a prototype for MUSE-DWI. Two radiologists independently assessed qualitative features using a 4-point scale. For the quantitative analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), and apparent diffusion coefficient (ADC) of the bladder lesions were recorded by two radiologists. The weighted kappa test and intraclass correlation were used to evaluate the interobserver agreement in the qualitative and quantitative analyses, respectively. Wilcoxon signed-rank test was used to compare the image quality of the two sequences. <b>Results:</b> DL MUSE-DWI demonstrated significantly improved qualitative image quality, with superior sharpness and lesion conspicuity. There were no significant differences in the distortion or artifacts. The qualitative analysis of the images by the two radiologists was in good to excellent agreement (κ ≥ 0.61). Quantitative analysis revealed higher SNR, CNR, and SIR in DL MUSE-DWI than in MUSE-DWI. The ADC values were significantly higher in DL MUSE-DWI. Interobserver agreement was poor (ICC ≤ 0.32) for SNR and CNR and excellent (ICC ≥ 0.85) for SIR and ADC values in both DL MUSE-DWI and MUSE-DWI. <b>Conclusions:</b> DL MUSE-DWI significantly enhanced the image quality in terms of lesion sharpness, conspicuity, SNR, CNR, and SIR, making it a promising tool for clinical imaging.https://www.mdpi.com/2075-4418/15/5/595bladder MRIdiffusion-weighted imaging (DWI)deep learning reconstructionimage quality |
| spellingShingle | Seung Ha Cha Yeo Eun Han Na Yeon Han Min Ju Kim Beom Jin Park Ki Choon Sim Deuk Jae Sung Seulki Yoo Patricia Lan Arnaud Guidon Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI Diagnostics bladder MRI diffusion-weighted imaging (DWI) deep learning reconstruction image quality |
| title | Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI |
| title_full | Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI |
| title_fullStr | Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI |
| title_full_unstemmed | Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI |
| title_short | Assessing Image Quality in Multiplexed Sensitivity-Encoding Diffusion-Weighted Imaging with Deep Learning-Based Reconstruction in Bladder MRI |
| title_sort | assessing image quality in multiplexed sensitivity encoding diffusion weighted imaging with deep learning based reconstruction in bladder mri |
| topic | bladder MRI diffusion-weighted imaging (DWI) deep learning reconstruction image quality |
| url | https://www.mdpi.com/2075-4418/15/5/595 |
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