{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:54:32Z","timestamp":1772240072612,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture with an Enhanced Cross-Modal Channel Attention (E-CMCA) module and a LSTM-Based Spatial Deformation Modeling Module. The E-CMCA module efficiently extracts and integrates multi-scale cross-modal features, while the LSTM-Based Spatial Deformation Modeling Module models temporal dynamics by processing depth-sliced 3D deformation fields as sequential data. A VecInt operation ensures smooth, diffeomorphic transformations, and a FuseConv layer enhances feature integration for precise alignment. Experiments on the \u03bc-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model significantly improves registration accuracy and performs robustly in both static 3D and dynamic 4D registration tasks. Experiments on the \u03bc-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model achieves a DSC of 0.865, RDSC of 0.898, TRE of 2.278 mm, and RTRE of 1.293, surpassing state-of-the-art methods and performing robustly in both static 3D and dynamic 4D registration tasks.<\/jats:p>","DOI":"10.3390\/jimaging11090292","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:49:34Z","timestamp":1756309774000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer"],"prefix":"10.3390","volume":"11","author":[{"given":"Ciliang","family":"Shao","sequence":"first","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijin","family":"Xue","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixu","family":"Gu","sequence":"additional","affiliation":[{"name":"Pittsburgh Institute, Sichuan University, Chengdu 610207, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","article-title":"Weakly-supervised convolutional neural networks for multimodal image registration","volume":"49","author":"Hu","year":"2018","journal-title":"Med. 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