{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:08:34Z","timestamp":1778659714300,"version":"3.51.4"},"reference-count":43,"publisher":"IOP Publishing","issue":"9","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20241202124902004"],"award-info":[{"award-number":["JCYJ20241202124902004"]}]},{"name":"Shenzhen Engineering Laboratory for Diagnosis & Treatment key technologies of interventional surgical robots","award":["XMHT20220104009"],"award-info":[{"award-number":["XMHT20220104009"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U20A20373"],"award-info":[{"award-number":["U20A20373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Develop Program of China","award":["2022YFC2409000"],"award-info":[{"award-number":["2022YFC2409000"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,5,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Complicated deformation remains a critical challenge in medical image registration (MIR). Although deep learning-based spatial domain registration networks have achieved improved accuracy and efficiency, they still suffer from irreversible information loss, the \u2018small objects move fast\u2019 problem\u2014where small objects are lost at low resolutions and reappear with large motions at finer resolutions\u2014and accumulated deformation errors propagating through coarse-to-fine architectures.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose Wave-Reg, a spatial-frequency registration framework built on a wavelet pyramid architecture. By integrating the discrete wavelet transform (DWT), the encoder extracts spatial-frequency features via DWT-guided ConvNet to minimize detail loss, while the decoder reconstructs displacement vector field (DVF) via inverse DWT-guided Swin Transformer to mitigate the \u2018small objects move fast\u2019 problem. A cross-scale self-correction module based on Heun\u2019s predictor\u2013corrector method further refines deformation fields across scales to tackle accumulated errors.\n                    <jats:italic>Main results.<\/jats:italic>\n                    Experiments on three datasets demonstrate substantial gains in registration accuracy for both large deformation and multi-modality registration tasks.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Wave-Reg demonstrates that spatial-frequency feature learning and predictor\u2013corrector refinement offer an effective solution to longstanding challenges in MIR.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae6414","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T22:52:53Z","timestamp":1776984773000},"page":"095027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Wave-Reg: full-stage wavelet-guided image registration framework with cross-scale correction"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8301-5391","authenticated-orcid":true,"given":"Chen","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingke","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Teng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2547-0394","authenticated-orcid":true,"given":"Wenjian","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoqin","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,5,13]]},"reference":[{"key":"pmbae6414bib1","doi-asserted-by":"publisher","first-page":"7010","DOI":"10.3390\/s22187010","type":"journal-article","article-title":"WCNN3D: wavelet convolutional neural network-based 3D object detection for autonomous driving","volume":"22","author":"Alaba","year":"2022","journal-title":"Sensors"},{"key":"pmbae6414bib2","author":"Anon","type":"report"},{"key":"pmbae6414bib3","doi-asserted-by":"publisher","first-page":"64","DOI":"10.4103\/jmp.jmp_168_23","type":"journal-article","article-title":"The investigating image registration accuracy and contour propagation for adaptive radiotherapy purposes in line with the task group No. 132 recommendation","volume":"49","author":"Archawametheekul","year":"2024","journal-title":"J. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2025-11-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-04-23","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-05-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}