{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:56:20Z","timestamp":1776110180064,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T00:00:00Z","timestamp":1745625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T00:00:00Z","timestamp":1745625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"the Japan Society for the Promotion of Science","doi-asserted-by":"crossref","award":["23K07057"],"award-info":[{"award-number":["23K07057"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>This study aimed to evaluate the efficacy of deep learning-based reconstruction\u00a0(DLR) in improving pancreatic diffusion-weighted imaging (DWI) quality.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>In total, 117 patients (mean age of 68.0\u2009\u00b1\u200912.9\u00a0years) suspected of pancreatic diseases underwent magnetic resonance imaging (MRI) between July and December 2023. MRI sequences included\u00a0respiratory-gated conventional diffusion-weighted images (RGC-DWIs), respiratory-gated diffusion-weighted images with deep learning-based reconstruction (DLR) (RGDLR-DWIs), and breath-hold diffusion-weighted images with DLR (BHDLR-DWIs) (short TE and long TE equal to other DWIs) at a 3\u00a0T MR system. Among these patients, 27 had solid lesions. Two radiologists qualitatively assessed pancreatic shape, main pancreatic duct (MPD) visualization, and solid lesion conspicuity using a 5-point scale. Quantitative analysis included apparent diffusion coefficient (ADC)\u00a0values for pancreatic parenchyma and solid lesions, signal-to-noise ratio (SNR), pancreas-to-muscle signal-intensity ratio (PM-SIR) and lesion-to-pancreas signal-intensity ratio (LP-SIR). Differences among DWI sequences were analyzed using Friedman\u2019s and Bonferroni\u2019s tests.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Qualitatively, BHDLR-DWIs (short TE) had the highest scores for pancreatic shape and MPD but lowest for solid lesions visibility, whereas RGDLR-DWIs had the highest score for solid lesions. Quantitatively, BHDLR-DWIs (short TE) had the lowest ADC values for pancreatic parenchyma and solid lesions, with the highest PM-SIR. There was no significant difference between BHDLR-DWIs (short TE) and RGDLR-DWIs for solid lesion ADC values. RGC-DWIs had the highest SNR, though differences from RGDLR-DWIs and BHDLR-DWIs (short TE) were not significant. Although LP-SIR in RGDLR-DWIs were the lowest, the difference was not significant.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>BHDLR-DWIs (short TE) provided the best pancreatic morphology image quality, whereas RGDLR-DWIs were superior for solid lesion detection.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11604-025-01790-w","type":"journal-article","created":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T01:50:32Z","timestamp":1745632232000},"page":"1509-1519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improvement of image quality of diffusion-weighted imaging (DWI) with deep learning reconstruction of the pancreas: comparison with respiratory-gated conventional DWI"],"prefix":"10.1007","volume":"43","author":[{"given":"Kazuki","family":"Oyama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumihito","family":"Ichinohe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuo","family":"Adachi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshihiro","family":"Kito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katsuya","family":"Maruyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minoru","family":"Mitsuda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Benkert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Omar","family":"Darwish","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3752-257X","authenticated-orcid":false,"given":"Yasunari","family":"Fujinaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"1790_CR1","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/s0720-048x(02)00303-0","volume":"45","author":"R Bammer","year":"2003","unstructured":"Bammer R. Basic principles of diffusion-weighted imaging. Eur J Radiol. 2003;45:169\u201384. https:\/\/doi.org\/10.1016\/s0720-048x(02)00303-0.","journal-title":"Eur J Radiol"},{"key":"1790_CR2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1148\/radiol.14130778","volume":"274","author":"M Barral","year":"2015","unstructured":"Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, et al. Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology. 2015;274:45\u201363. https:\/\/doi.org\/10.1148\/radiol.14130778.","journal-title":"Radiology"},{"key":"1790_CR3","doi-asserted-by":"publisher","first-page":"424","DOI":"10.4329\/wjr.v7.i12.424","volume":"7","author":"MY Tang","year":"2015","unstructured":"Tang MY, Zhang XM, Chen TW, Huang XH. Various diffusion magnetic resonance imaging techniques for pancreatic cancer. World J Radiol. 2015;7:424\u201337. https:\/\/doi.org\/10.4329\/wjr.v7.i12.424.","journal-title":"World J Radiol"},{"key":"1790_CR4","doi-asserted-by":"publisher","first-page":"6892","DOI":"10.1007\/s00330-023-09661-6","volume":"33","author":"FN Harder","year":"2023","unstructured":"Harder FN, Jung E, Weiss K, Graf MM, Kamal O, McTavish S, et al. Computed high-b-value high-resolution DWI improves solid lesion detection in IPMN of the pancreas. Eur Radiol. 2023;33:6892\u2013901. https:\/\/doi.org\/10.1007\/s00330-023-09661-6.","journal-title":"Eur Radiol"},{"key":"1790_CR5","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1002\/jmri.21651","volume":"29","author":"R Fattahi","year":"2009","unstructured":"Fattahi R, Balci NC, Perman WH, Hsueh EC, Alkaade S, Havlioglu N, et al. Pancreatic diffusion-weighted imaging (DWI): comparison between mass-forming focal pancreatitis (FP), pancreatic cancer (PC), and normal pancreas. J Magn Reson Imaging. 2009;29:350\u20136. https:\/\/doi.org\/10.1002\/jmri.21651.","journal-title":"J Magn Reson Imaging"},{"key":"1790_CR6","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1007\/s11604-020-01057-6","volume":"38","author":"M Bartoli","year":"2020","unstructured":"Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Soyer P, et al. CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol. 2020;38:1111\u201324. https:\/\/doi.org\/10.1007\/s11604-020-01057-6.","journal-title":"Jpn J Radiol."},{"key":"1790_CR7","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1148\/radiol.14132563","volume":"273","author":"MJ Park","year":"2014","unstructured":"Park MJ, Kim YK, Choi SY, Rhim H, Lee WJ, Choi D. Preoperative detection of small pancreatic carcinoma: value of adding diffusion-weighted imaging to conventional MR imaging for improving confidence level. Radiology. 2014;273:433\u201343. https:\/\/doi.org\/10.1148\/radiol.14132563.","journal-title":"Radiology"},{"key":"1790_CR8","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1007\/s00330-008-0968-z","volume":"18","author":"TC Kwee","year":"2008","unstructured":"Kwee TC, Takahara T, Ochiai R, Nievelstein RAJ, Luijten PR. Diffusion-weighted whole-body imaging with background body signal suppression (DWIBS): features and potential applications in oncology. Eur Radiol. 2008;18:1937\u201352. https:\/\/doi.org\/10.1007\/s00330-008-0968-z.","journal-title":"Eur Radiol"},{"key":"1790_CR9","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ejrad.2010.02.018","volume":"76","author":"O Dietrich","year":"2010","unstructured":"Dietrich O, Biffar A, Baur-Melnyk A, Reiser MF. Technical aspects of MR diffusion imaging of the body. Eur J Radiol. 2010;76:314\u201322. https:\/\/doi.org\/10.1016\/j.ejrad.2010.02.018.","journal-title":"Eur J Radiol"},{"key":"1790_CR10","doi-asserted-by":"publisher","first-page":"2186","DOI":"10.1007\/s00330-012-2469-3","volume":"22","author":"N Kartalis","year":"2012","unstructured":"Kartalis N, Loizou L, Edsborg N, Segersv\u00e4rd R, Albiin N. Optimising diffusion-weighted MR imaging for demonstrating pancreatic cancer: a comparison of respiratory-triggered, free-breathing and breath-hold techniques. Eur Radiol. 2012;22:2186\u201392. https:\/\/doi.org\/10.1007\/s00330-012-2469-3.","journal-title":"Eur Radiol"},{"key":"1790_CR11","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s11604-018-0796-2","volume":"37","author":"T Higaki","year":"2019","unstructured":"Higaki T, Nakamura Y, Tatsugami F, Nakaura T, Awai K. Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol. 2019;37:73\u201380. https:\/\/doi.org\/10.1007\/s11604-018-0796-2.","journal-title":"Jpn J Radiol"},{"key":"1790_CR12","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.diii.2022.11.002","volume":"104","author":"S Afat","year":"2023","unstructured":"Afat S, Herrmann J, Almansour H, Benkert T, Weiland E, H\u00f6lldobler T, et al. Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagn Interv Imaging. 2023;104:178\u201384. https:\/\/doi.org\/10.1016\/j.diii.2022.11.002.","journal-title":"Diagn Interv Imaging"},{"key":"1790_CR13","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.mri.2021.05.001","volume":"80","author":"M Tanabe","year":"2021","unstructured":"Tanabe M, Higashi M, Yonezawa T, Yamaguchi T, Iida E, Furukawa M, et al. Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique. Magn Reson Imaging. 2021;80:121\u20136. https:\/\/doi.org\/10.1016\/j.mri.2021.05.001.","journal-title":"Magn Reson Imaging"},{"key":"1790_CR14","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1097\/RLI.0000000000000943","volume":"58","author":"F Ichinohe","year":"2023","unstructured":"Ichinohe F, Oyama K, Yamada A, Hayashihara H, Adachi Y, Kitoh Y, et al. Usefulness of breath-hold fat-suppressed T2-weighted images with deep learning-based reconstruction of the liver: comparison to conventional free-breathing turbo spin echo. Invest Radiol. 2023;58:373\u20139. https:\/\/doi.org\/10.1097\/RLI.0000000000000943.","journal-title":"Invest Radiol"},{"key":"1790_CR15","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.diii.2022.05.001","volume":"103","author":"S Mul\u00e9","year":"2022","unstructured":"Mul\u00e9 S, Kharrat R, Zerbib P, Massire A, Nickel MD, Ambarki K, et al. Fast T2-weighted liver MRI: image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence. Diagn Interv Imaging. 2022;103:479\u201385. https:\/\/doi.org\/10.1016\/j.diii.2022.05.001.","journal-title":"Diagn Interv Imaging"},{"key":"1790_CR16","doi-asserted-by":"publisher","first-page":"338","DOI":"10.4329\/wjr.v15.i12.338","volume":"15","author":"Y Takayama","year":"2023","unstructured":"Takayama Y, Sato K, Tanaka S, Murayama R, Goto N, Yoshimitsu K. Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas. World J Radiol. 2023;15:338\u201349. https:\/\/doi.org\/10.4329\/wjr.v15.i12.338.","journal-title":"World J Radiol"},{"key":"1790_CR17","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1016\/j.acra.2024.02.049","volume":"31","author":"S Altmann","year":"2024","unstructured":"Altmann S, Grauhan NF, Mercado MAA, Steinmetz S, Kronfeld A, Paul R, et al. Deep learning accelerated brain diffusion-weighted MRI with super resolution processing. Acad Radiol. 2024;31:4171\u201382. https:\/\/doi.org\/10.1016\/j.acra.2024.02.049.","journal-title":"Acad Radiol"},{"key":"1790_CR18","doi-asserted-by":"publisher","unstructured":"Jeong J, Yeom SK, Choi IY, Cha SH, Han JS, Lee CH, et al. Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications. Quant Imaging Med Surg. 2024;14:3432\u201346. https:\/\/doi.org\/10.21037\/qims-23-1379","DOI":"10.21037\/qims-23-1379"},{"key":"1790_CR19","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/mrm.26977","volume":"79","author":"K Hammernik","year":"2018","unstructured":"Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79:3055\u201371. https:\/\/doi.org\/10.1002\/mrm.26977.","journal-title":"Magn Reson Med"},{"key":"1790_CR20","doi-asserted-by":"publisher","unstructured":"Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. New York: IEEE; 2016 p. 1874\u201383. https:\/\/doi.org\/10.1109\/CVPR.2016.207.","DOI":"10.1109\/CVPR.2016.207"},{"key":"1790_CR21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.5152\/dir.2015.14440","volume":"22","author":"A Celik","year":"2016","unstructured":"Celik A. Effect of imaging parameters on the accuracy of apparent diffusion coefficient and optimization strategies. Diagn Interv Radiol. 2016;22:101\u20137. https:\/\/doi.org\/10.5152\/dir.2015.14440.","journal-title":"Diagn Interv Radiol"},{"key":"1790_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110428","volume":"154","author":"SH Bae","year":"2022","unstructured":"Bae SH, Hwang J, Hong SS, Lee EJ, Jeong J, Benkert T, et al. Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: comparison with conventional diffusion weighted imaging. Eur J Radiol. 2022;154: 110428. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110428.","journal-title":"Eur J Radiol"},{"key":"1790_CR23","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1007\/s00330-019-06648-0","volume":"30","author":"M Iima","year":"2020","unstructured":"Iima M, Partridge SC, Le Bihan D. Six DWI questions you always wanted to know but were afraid to ask: clinical relevance for breast diffusion MRI. Eur Radiol. 2020;30:2561\u201370. https:\/\/doi.org\/10.1007\/s00330-019-06648-0.","journal-title":"Eur Radiol"},{"key":"1790_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111149","volume":"168","author":"Q Chen","year":"2023","unstructured":"Chen Q, Fang S, Yuchen Y, Li R, Deng R, Chen Y, et al. Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: improvement of image quality and impact on apparent diffusion coefficient value. Eur J Radiol. 2023;168: 111149. https:\/\/doi.org\/10.1016\/j.ejrad.2023.111149.","journal-title":"Eur J Radiol"},{"key":"1790_CR25","doi-asserted-by":"publisher","unstructured":"Hectors SJ, Wagner M, Corcuera-Solano I, Kang M, Stemmer A, Boss MA, et al. C comparison between 3-scan trace and diagonal body diffusion-weighted imaging acquisitions: a phantom and volunteer study. Tomography. 2016;2:411\u2013420. https:\/\/doi.org\/10.18383\/j.tom.2016.00229","DOI":"10.18383\/j.tom.2016.00229"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-025-01790-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T08:49:00Z","timestamp":1756457340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11604-025-01790-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,26]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1790"],"URL":"https:\/\/doi.org\/10.1007\/s11604-025-01790-w","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,26]]},"assertion":[{"value":"25 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Katsuya Maruyama and Minoru Mitsuda are employees of Siemens Healthcare K.K.; Thomas Benkert and Omar Darwish are employees of Siemens Healthcare AG. The other authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This prospective study was approved by our institutional review board (No. 5878) and all procedures adhered to the ethical standards of the 1964 Declaration of Helsinki and its later amendments.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Written informed consent was obtained from all participants.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}