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Med. Biol."],"published-print":{"date-parts":[[2024,10,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. T2-weighted 2D fast spin echo sequence serves as the standard sequence in clinical pelvic MR imaging protocols. However, motion artifacts and blurring caused by peristalsis present significant challenges. Patient preparation such as administering antiperistaltic agents is often required before examination to reduce artifacts, which discomfort the patients. This work introduce a novel dynamic approach for T2 weighted pelvic imaging to address peristalsis-induced motion issue without any patient preparation. <jats:italic>Approach<\/jats:italic>. A rapid dynamic data acquisition strategy with complementary sampling trajectory is designed to enable highly undersampled motion-resistant data sampling, and an unrolling method based on deep equilibrium model is leveraged to reconstruct images from the dynamic sampled k-space data. Moreover, the fix-point convergence of the equilibrium model ensures the stability of the reconstruction. The high acceleration factor in each temporal phase, which is much higher than that in traditional static imaging, has the potential to effectively freeze pelvic motion, thereby transforming the imaging problem from conventional motion prevention or removal to motion reconstruction. <jats:italic>Main results<\/jats:italic>. Experiments on both retrospective and prospective data have demonstrated the superior performance of the proposed dynamic approach in reducing motion artifacts and accurately depicting structural details compared to standard static imaging. <jats:italic>Significance<\/jats:italic>. The proposed dynamic approach effectively captures motion states through dynamic data acquisition and deep learning-based reconstruction, addressing motion-related challenges in pelvic imaging.<\/jats:p>","DOI":"10.1088\/1361-6560\/ad8335","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T22:56:27Z","timestamp":1727996187000},"page":"205019","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A dynamic approach for MR T2-weighted pelvic imaging"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9098-8048","authenticated-orcid":true,"given":"Jing","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingneng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naijia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9283-881X","authenticated-orcid":true,"given":"Zhuo-Xu","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenkui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guobin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huimao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1358-9777","authenticated-orcid":true,"given":"Dong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,10,15]]},"reference":[{"key":"pmbad8335bib1","first-page":"pp 109","article-title":"Spatio-temporal constrained reconstruction of sparse dynamic contrast enhanced radial MRI data","author":"Adluru","year":"2007"},{"key":"pmbad8335bib2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.212922","article-title":"Deep learning reconstruction for accelerated spine MRI: prospective analysis of interchangeability","volume":"306","author":"Almansour","year":"2023","journal-title":"Radiology"},{"key":"pmbad8335bib3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.231938","article-title":"Ultrafast brain MRI with deep learning reconstruction for suspected acute ischemic stroke","volume":"310","author":"Altmann","year":"2024","journal-title":"Radiology"},{"key":"pmbad8335bib4","article-title":"Deep equilibrium models","volume":"vol 32","author":"Bai","year":"2019"},{"key":"pmbad8335bib5","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1007\/s00330-017-5026-2","article-title":"Magnetic resonance imaging for clinical management of rectal cancer: updated recommendations from the 2016 European society of gastrointestinal and abdominal radiology (ESGAR) consensus meeting","volume":"28","author":"Beets-Tan","year":"2018","journal-title":"Eur. 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