Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction
Purpose: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). Methods: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI...
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Elsevier
2024-12-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000431 |
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| author | Noriko Nishioka Noriyuki Fujima Satonori Tsuneta Masato Yoshikawa Rina Kimura Keita Sakamoto Fumi Kato Haruka Miyata Hiroshi Kikuchi Ryuji Matsumoto Takashige Abe Jihun Kwon Masami Yoneyama Kohsuke Kudo |
| author_facet | Noriko Nishioka Noriyuki Fujima Satonori Tsuneta Masato Yoshikawa Rina Kimura Keita Sakamoto Fumi Kato Haruka Miyata Hiroshi Kikuchi Ryuji Matsumoto Takashige Abe Jihun Kwon Masami Yoneyama Kohsuke Kudo |
| author_sort | Noriko Nishioka |
| collection | DOAJ |
| description | Purpose: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). Methods: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. Results: In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). Conclusion: Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research. |
| format | Article |
| id | doaj-art-8d51d03539e94be5a5f724a3f8a938d8 |
| institution | Kabale University |
| issn | 2352-0477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-8d51d03539e94be5a5f724a3f8a938d82024-12-15T06:15:45ZengElsevierEuropean Journal of Radiology Open2352-04772024-12-0113100588Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstructionNoriko Nishioka0Noriyuki Fujima1Satonori Tsuneta2Masato Yoshikawa3Rina Kimura4Keita Sakamoto5Fumi Kato6Haruka Miyata7Hiroshi Kikuchi8Ryuji Matsumoto9Takashige Abe10Jihun Kwon11Masami Yoneyama12Kohsuke Kudo13Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Correspondence to: Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido 060-8648, Japan.Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Department of Radiology, Graduate School of Dental Medicine, Hokkaido University, N13 W7, Kita-ku, Sapporo 060-8586, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Department of Radiology, Jichi Medical University Saitama Medical Center, 1-847 Amanuma-cho, Omiya-ku, Saitama, 330-8503, JapanDepartment of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, JapanDepartment of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, JapanDepartment of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, JapanDepartment of Renal and Genitourinary Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo 060-8638, JapanPhilips Japan Ltd., 1-3-1 Azabudai, Minato-ku, Tokyo 106-0041, JapanPhilips Japan Ltd., 1-3-1 Azabudai, Minato-ku, Tokyo 106-0041, JapanDepartment of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo 060-8648, Japan; Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo 060-8638, Japan; Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, 060-8648, JapanPurpose: To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI). Methods: This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue. Results: In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively). Conclusion: Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.http://www.sciencedirect.com/science/article/pii/S2352047724000431ProstateDWIDeep learning reconstructionImage quality |
| spellingShingle | Noriko Nishioka Noriyuki Fujima Satonori Tsuneta Masato Yoshikawa Rina Kimura Keita Sakamoto Fumi Kato Haruka Miyata Hiroshi Kikuchi Ryuji Matsumoto Takashige Abe Jihun Kwon Masami Yoneyama Kohsuke Kudo Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction European Journal of Radiology Open Prostate DWI Deep learning reconstruction Image quality |
| title | Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction |
| title_full | Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction |
| title_fullStr | Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction |
| title_full_unstemmed | Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction |
| title_short | Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction |
| title_sort | enhancing the image quality of prostate diffusion weighted imaging in patients with prostate cancer through model based deep learning reconstruction |
| topic | Prostate DWI Deep learning reconstruction Image quality |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000431 |
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