{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:16:50Z","timestamp":1787314610977,"version":"build-2736575974"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006447","name":"University of Zurich","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006447","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pediatr Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Radial\n                      <jats:italic>k<\/jats:italic>\n                      -space sampling is widely employed in paediatric magnetic resonance imaging (MRI) to mitigate motion and aliasing artefacts. Artificial intelligence (AI)-based image reconstruction has been developed to enhance image quality and accelerate acquisition time.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To assess image quality of deep learning (DL)-based denoising image reconstruction of body MRI in children.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      Children who underwent thoraco-abdominal MRI employing radial\n                      <jats:italic>k<\/jats:italic>\n                      -space filling technique (PROPELLER) with conventional and DL-based image reconstruction between April 2022 and January 2023 were eligible for this retrospective study. Only cases with previous MRI including comparable PROPELLER sequences with conventional image reconstruction were selected. Image quality was compared between DL-reconstructed axial T1-weighted and T2-weighted images and conventionally reconstructed images from the same PROPELLER acquisition. Quantitative image quality was assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the liver and spleen. Qualitative image quality was evaluated by three observers using a 4-point Likert scale and included presence of noise, motion artefact, depiction of peripheral lung vessels and subsegmental bronchi at the lung bases, sharpness of abdominal organ borders, and visibility of liver and spleen vessels. Image quality was compared with the Wilcoxon signed-rank test. Scan time length was compared to prior MRI obtained with conventional image reconstruction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In 21 children (median age 7 years, range 1.5 years to 15.8 years) included, the SNR and CNR of the liver and spleen on T1-weighted and T2-weighted images were significantly higher with DL-reconstruction (\n                      <jats:italic>P<\/jats:italic>\n                      &lt;0.001) than with conventional reconstruction. The DL-reconstructed images showed higher overall image quality, with improved delineation of the peripheral vessels and the subsegmental bronchi in the lung bases, sharper abdominal organ margins and increased visibility of the peripheral vessels in the liver and spleen. Not respiratory-gated DL-reconstructed\u00a0T1-weighted images demonstrated more pronounced respiratory motion artefacts in comparison to conventional reconstruction (\n                      <jats:italic>P=<\/jats:italic>\n                      0.015), while there was no difference for the respiratory-gated T2-weighted images. The median scan time per slice was reduced from 6.3 s (interquartile range, 4.2 \u2013 7.0 s) to 4.8 s (interquartile range, 4.4 \u2013 4.9 s) for the T1-weighted images and from 5.6 s (interquartile range, 5.4 \u2013 5.9 s) to 4.2 s (interquartile range, 3.9 \u2013 4.8 s) for the T2-weighted images.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      DL-based denoising image reconstruction of paediatric body MRI sequences employing radial\n                      <jats:italic>k<\/jats:italic>\n                      -space sampling allowed for improved overall image quality at shorter scan times. Respiratory motion artefacts were more pronounced on ungated T1-weighted images.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1007\/s00247-025-06230-5","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T19:17:08Z","timestamp":1743880628000},"page":"1235-1244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep learning-based denoising image reconstruction of body magnetic resonance imaging in children"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1546-2837","authenticated-orcid":false,"given":"Vanda","family":"Pocepcova","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-7266","authenticated-orcid":false,"given":"Michael","family":"Zellner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2862-6633","authenticated-orcid":false,"given":"Fraser","family":"Callaghan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4326-4005","authenticated-orcid":false,"given":"Maelene","family":"Lohezic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Geiger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9122-2431","authenticated-orcid":false,"given":"Christian Johannes","family":"Kellenberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"key":"6230_CR1","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.2214\/AJR.19.21987","volume":"214","author":"SD Serai","year":"2020","unstructured":"Serai SD, Hu HH, Ahmad R et al (2020) Newly developed methods for reducing motion artifacts in pediatric abdominal MRI: tips and pearls. Am J Roentgenol 214:1042\u20131053","journal-title":"Am J Roentgenol"},{"key":"6230_CR2","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1148\/rg.333125027","volume":"33","author":"GB Chavhan","year":"2013","unstructured":"Chavhan GB, Babyn PS, Vasanawala SS (2013) Abdominal MR imaging in children: motion compensation, sequence optimization, and protocol organization. RadioGraphics 33:703\u2013719","journal-title":"RadioGraphics"},{"key":"6230_CR3","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1038\/242190a0","volume":"242","author":"PC Lauterbur","year":"1973","unstructured":"Lauterbur PC (1973) Image formation by induced local interactions: examples employing nuclear magnetic resonance. Nature 242:190\u2013191","journal-title":"Nature"},{"key":"6230_CR4","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.2214\/AJR.07.3657","volume":"191","author":"Y Hirokawa","year":"2008","unstructured":"Hirokawa Y, Isoda H, Maetani YS et al (2008) MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and prospective acquisition correction (PACE) technique. Am J Roentgenol 191:1154\u20131158","journal-title":"Am J Roentgenol"},{"key":"6230_CR5","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1007\/s00247-014-3244-1","volume":"45","author":"JH Lee","year":"2015","unstructured":"Lee JH, Choi YH, Cheon JE et al (2015) Improved abdominal MRI in non-breath-holding children using a radial k-space sampling technique. Pediatr Radiol 45:840\u2013846","journal-title":"Pediatr Radiol"},{"key":"6230_CR6","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L","volume":"42","author":"JG Pipe","year":"1999","unstructured":"Pipe JG (1999) Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med 42:963\u2013969","journal-title":"Magn Reson Med"},{"key":"6230_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1002\/jmri.1176","volume":"14","author":"KPN Forbes","year":"2001","unstructured":"Forbes KPN, Pipe JG, Bird CR, Heiserman JE (2001) PROPELLER MRI: clinical testing of a novel technique for quantification and compensation of head motion. Magnet Reson Imaging 14:215\u2013222","journal-title":"Magnet Reson Imaging"},{"key":"6230_CR8","unstructured":"Forbes KP, Pipe JG, Karis JP, et al (2003) Brain imaging in the unsedated pediatric patient: comparison of periodically rotated overlapping parallel lines with enhanced reconstruction and single-shot fast spin-echo sequences"},{"key":"6230_CR9","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/j.ejrad.2015.12.016","volume":"85","author":"M Meier-Schroers","year":"2016","unstructured":"Meier-Schroers M, Kukuk G, Homsi R et al (2016) MRI of the lung using the PROPELLER technique: artifact reduction, better image quality and improved nodule detection. Eur J Radiol 85:707\u2013713","journal-title":"Eur J Radiol"},{"key":"6230_CR10","doi-asserted-by":"publisher","first-page":"W1093","DOI":"10.2214\/AJR.10.6065","volume":"197","author":"TJ Dietrich","year":"2011","unstructured":"Dietrich TJ, Ulbrich EJ, Zanetti M et al (2011) PROPELLER technique to improve image quality of MRI of the shoulder. Am J Roentgenol 197:W1093\u2013W1100","journal-title":"Am J Roentgenol"},{"key":"6230_CR11","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1002\/jmri.23720","volume":"36","author":"AA Tamhane","year":"2012","unstructured":"Tamhane AA, Arfanakis K, Anastasio M et al (2012) Rapid PROPELLER-MRI: a combination of iterative reconstruction and under-sampling. Magnet Resonance Imaging 36:1241\u20131247","journal-title":"Magnet Resonance Imaging"},{"key":"6230_CR12","doi-asserted-by":"publisher","first-page":"e220133","DOI":"10.1148\/rg.220133","volume":"43","author":"S Kiryu","year":"2023","unstructured":"Kiryu S, Akai H, Yasaka K et al (2023) Clinical impact of deep learning reconstruction in MRI. RadioGraphics 43:e220133","journal-title":"RadioGraphics"},{"key":"6230_CR13","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1002\/jmri.27078","volume":"53","author":"DJ Lin","year":"2021","unstructured":"Lin DJ, Johnson PM, Knoll F, Lui YW (2021) Artificial intelligence for MR image reconstruction: an overview for clinicians. Magnet Reson Imaging 53:1015\u20131028","journal-title":"Magnet Reson Imaging"},{"key":"6230_CR14","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/JPROC.2022.3141367","volume":"110","author":"Y Chen","year":"2022","unstructured":"Chen Y, Schonlieb C-B, Lio P et al (2022) AI-based reconstruction for fast MRI\u2014a systematic review and meta-analysis. Proc IEEE 110:224\u2013245","journal-title":"Proc IEEE"},{"key":"6230_CR15","unstructured":"Lebel RM (2020) Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv preprint. arXiv:2008.06559. https:\/\/arxiv.org\/abs\/2008.06559. Posted August 14, 2020. Accessed January 15, 2025."},{"key":"6230_CR16","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.mri.2022.04.005","volume":"90","author":"T Tajima","year":"2022","unstructured":"Tajima T, Akai H, Yasaka K et al (2022) Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach. Magnet Reson Imaging 90:76\u201383","journal-title":"Magnet Reson Imaging"},{"key":"6230_CR17","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s00234-022-03053-1","volume":"65","author":"S-H Kim","year":"2023","unstructured":"Kim S-H, Choi YH, Lee JS et al (2023) Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology 65:207\u2013214","journal-title":"Neuroradiology"},{"key":"6230_CR18","doi-asserted-by":"publisher","first-page":"e200278","DOI":"10.1148\/ryai.2021200278","volume":"3","author":"KM Koch","year":"2021","unstructured":"Koch KM, Sherafati M, Arpinar VE et al (2021) Analysis and evaluation of a deep learning reconstruction approach with denoising for orthopedic MRI. Radiol Art Intell 3:e200278","journal-title":"Radiol Art Intell"},{"key":"6230_CR19","unstructured":"Wang X, Litwiller D, Lebel R, et al (2020) High resolution T2W imaging using deep learning reconstruction and reduced field-of-view PROPELLER. In: Proc. of the 2020 ISMRM & SMRT Virtual Conference & Exhibition"},{"key":"6230_CR20","unstructured":"Wang X, Litwiller D, Ersoz A, et al (2020) Diffusion weighted imaging using PROPELLER acquisition and a deep learning based reconstruction. In: Proc. of the 2020 ISMRM & SMRT Virtual Conference & Exhibition"},{"key":"6230_CR21","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s00330-022-09151-1","volume":"33","author":"M Kaniewska","year":"2022","unstructured":"Kaniewska M, Deininger-Czermak E, Getzmann JM et al (2022) Application of deep learning\u2013based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time. Eur Radiol 33:1513\u20131525","journal-title":"Eur Radiol"},{"key":"6230_CR22","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/s00261-022-03687-y","volume":"48","author":"LA Ginocchio","year":"2022","unstructured":"Ginocchio LA, Smereka PN, Tong A et al (2022) Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection. Abdom Radiol 48:282\u2013290","journal-title":"Abdom Radiol"},{"key":"6230_CR23","doi-asserted-by":"publisher","first-page":"e220425","DOI":"10.1148\/radiol.220425","volume":"307","author":"PM Johnson","year":"2023","unstructured":"Johnson PM, Lin DJ, Zbontar J et al (2023) Deep learning reconstruction enables prospectively accelerated clinical knee MRI. Radiology 307:e220425","journal-title":"Radiology"},{"key":"6230_CR24","doi-asserted-by":"publisher","first-page":"1323443","DOI":"10.3389\/fcvm.2024.1323443","volume":"11","author":"TM Vollbrecht","year":"2024","unstructured":"Vollbrecht TM, Hart C, Zhang S et al (2024) Deep learning denoising reconstruction for improved image quality in fetal cardiac cine MRI. Front Cardiovasc Med 11:1323443","journal-title":"Front Cardiovasc Med"},{"key":"6230_CR25","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TMI.2021.3075856","volume":"40","author":"MJ Muckley","year":"2021","unstructured":"Muckley MJ, Riemenschneider B, Radmanesh A et al (2021) Results of the 2020 fastMRI challenge for machine learning MR image reconstruction. IEEE Trans Med Imaging 40:2306\u20132317","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Pediatric Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-025-06230-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00247-025-06230-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00247-025-06230-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T05:51:36Z","timestamp":1748411496000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00247-025-06230-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,5]]},"references-count":25,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["6230"],"URL":"https:\/\/doi.org\/10.1007\/s00247-025-06230-5","relation":{},"ISSN":["1432-1998"],"issn-type":[{"value":"1432-1998","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,5]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}