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However, both modalities are prone to misdiagnoses or missed lesions. Fusing these complementary data sources, specifically the combined CT\u2010PET image data from PSMA PET\/CT scans and contrast\u2010enhanced MRI, may improve diagnostic accuracy, with precise spatial registration being a crucial prerequisite for effective image fusion. While previous studies have used software tools for MRI\u2013PSMA PET\/CT fusion, most rely on MRI\u2013CT\u2010based anatomical registration and treat PET as a secondary overlay, thereby underutilizing PSMA's tumor\u2010specific metabolic\u00a0information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To propose a global and local information\u2010based registration network (GLNet) that integrates PSMA PET\/CT's functional\u2010semantic features with MRI's high\u2010resolution soft\u2010tissue details to improve PCa lesion\u00a0diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To improve prostate image registration, GLNet was designed using semantic gating convolutional (SGC) modules and a convolutional long short\u2010term memory network based on a U\u2010shaped channel (U\u2010CLSTM). Specifically, SGC modules enhance perception of the prostate gland using global information, while U\u2010CLSTM improves attention to local tumor regions. The dataset comprised 77 clinical cases, each verified by two experienced physicians through clinical biopsy. After data augmentation, 244 cases were used for training and validation, and 64 cases for testing. GLNet's performance was compared against state\u2010of\u2010the\u2010art methods: symmetric normalization (SyN), VoxelMorph (VM), volume tweening network (VTN), and recursive deformable pyramid (RDP). Statistical analyses were conducted using the Kruskal\u2013Wallis test with Bonferroni correction for pairwise comparisons, and effect sizes were assessed using Cohen's\u00a0d.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      GLNet achieved Dice similarity coefficient (DSC) of 0.76  0.11, HD95 of 9.32  3.13 mm, average symmetric surface distance (ASD) of 1.690.65 mm, and a near\u2010zero negative Jacobian proportion of  for prostate gland registration. In contrast to other networks, GLNet demonstrated significant improvements (\n                      <jats:italic>p<\/jats:italic>\n                      0.001), with DSC increasing by \u2013, 5th\u2010percentile Hausdorff distance (HD95) decreasing by 1.69\u201330.03 mm, and ASD reducing by 0.24\u201315.44 mm. Cohen's d values indicated large effect sizes. For local lesion detection, GLNet achieved  precision,  recall, and an F1\u2010score of 0.86, which outperformed other methods by \u2013 in precision, \u2013 in recall, and 0.21\u20130.37\u00a0F1\u2010score.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents GLNet, a deep learning\u2010based non\u2010rigid registration network that fuses PSMA PET\/CT and contrast\u2010enhanced MRI. GLNet outperforms existing methods in registration accuracy and lesion detection, thereby offering a promising approach for integrating structural and functional imaging in clinical PCa\u00a0diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70232","type":"journal-article","created":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T13:20:22Z","timestamp":1766841622000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Global and local information\u2010based prostate image registration of prostate\u2010specific membrane antigen PET\/CT and enhanced MRI"],"prefix":"10.1002","volume":"53","author":[{"given":"Yu'ang","family":"Niu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyang","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Urology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Urology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwen","family":"Zou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (imLic), Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0090-4295(00)00753-6"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2008.20.4669"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2016.10.010"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/bju.13903"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2015.08.052"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32401-1"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2007.06.028"},{"issue":"9","key":"e_1_2_10_10_1","first-page":"1445","article-title":"Prostate\u2010specific membrane antigen: a novel folate hydrolase in human prostatic carcinoma cells","volume":"2","author":"Pinto JT","year":"1996","journal-title":"Clin Cancer Res: Offic J American Assoc Cancer Res"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/bju.14648"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/RCT.0000000000001116"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(98)80022-4"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.796284"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000043755.93987.aa"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2007.06.004"},{"key":"e_1_2_10_17_1","doi-asserted-by":"crossref","unstructured":"WolbergG ZokaiS.Robust image registration using log\u2010polar transform. 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