Advancing ASL kidney image registration: a tailored pipeline with VoxelMorph

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dc.contributor.authorOyarzun Domeño, Anne
dc.contributor.authorCia Alonso, Izaskun
dc.contributor.authorEcheverría Chasco, Rebeca
dc.contributor.authorFernández Seara, María A.
dc.contributor.authorMartín Moreno, Paloma L.
dc.contributor.authorGarcía Fernández, Nuria
dc.contributor.authorBastarrika, Gorka
dc.contributor.authorNavallas Irujo, Javier
dc.contributor.authorVillanueva Larre, Arantxa
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritzaeu
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
dc.contributor.funderGobierno de Navarra / Nafarroako Gobernua
dc.date.accessioned2025-06-11T09:44:08Z
dc.date.available2025-06-11T09:44:08Z
dc.date.issued2025-01-31
dc.date.updated2025-06-11T09:33:39Z
dc.description.abstractIn clinical renal assessment, image registration plays a pivotal role, as patient movement during data acquisition can significantly impede image post-processing and the accurate estimation of hemodynamic parameters. This study introduces a deep learning-based image registration framework specifically for arterial spin labeling (ASL) imaging. ASL is a magnetic resonance imaging technique that modifies the longitudinal magnetization of blood perfusing the kidney using a series of radiofrequency pulses combined with slice-selective gradients. After tagging the arterial blood, label images are captured following a delay, allowing the tagged blood bolus to enter the renal tissue, while control images are acquired without tagging the arterial spins. Given that perfusion maps are generated at the pixel level by subtracting control images from label images and considering the relatively small signal intensity difference, precise alignment of these images is crucial to minimize motion artefacts and prevent significant errors in perfusion calculations. Moreover, due to the extended ASL acquisition times and the anatomical location of the kidneys, renal images are often susceptible to pulsation, peristalsis, and breathing motion. These motion-induced noises and other instabilities can adversely affect ASL imaging outcomes, making image registration essential. However, research on renal MRI registration, particularly with respect to learning-based techniques, remains limited, with even less focus on renal ASL. Our study proposes a learning-based image registration approach that builds upon VoxelMorph and introduces groupwise inference as a key enhancement. The dataset includes 2448 images of transplanted kidneys (TK) and 2456 images of healthy kidneys (HK). We compared the automatic image registration results with the widely recognized optimization method Elastix. The model’s performance was evaluated using the mean structural similarity index (MSSIM), normalized correlation coefficient (NCC), temporal signal-tonoise ratio (TSNR) of the samples, and the mean cortical signal (CSIM) in perfusion-weighted images, thereby extending the evaluation beyond traditional similarity-based metrics. Our method achieved superior image registration performance, with peak NCC (0.987 ± 0.006) and MSSIM (0.869 ± 0.048) values in the kidney region, significantly surpassing Elastix and the unregistered series (p\ 0.05) on TK and HK datasets. Regularization analysis showed that higher k values (1, 2) produced smoother deformation fields, while moderate k values (0.5, 0.9) balanced smoothness and detail, maintaining low non-positive Jacobian percentages (\1%) comparable to Elastix. Additionally, our method improved CSIM by 14.3% (2.304 ± 1.167) and TSNR by 13.1% (3.888 ± 2.170) in TK, and achieved up to 13.2% (CSIM) and 29.8% (TSNR) enhancements in HK, demonstrating robustness and improved signal quality across datasets and acquisition techniques.en
dc.description.sponsorshipOpen access funding provided by Universidad Pública de Navarra. Project PC181-182 RM-RENAL, supported by the Department of University, Innovation and Digital Transformation (Government of Navarre). The author receives a PhD scholarship number 0011-0537-2021-000050 from Department of University, Innovation and Digital Transformation (Government of Navarre).
dc.format.mimetypeapplication/pdfen
dc.format.mimetypeapplication/msworden
dc.identifier.citationOyarzun-Domeño, A., Cia I., Echeverria-Chasco, R., Fernández-Seara, M. A., Martin-Moreno, P. L., Garcia-Fernandez, N., Bastarrika, G., Navallas, J., Villanueva, A. (2025). Advancing ASL kidney image registration: a tailored pipeline with VoxelMorph. Neural Computing and Applications, 37(14), 8347-8369. https://doi.org/10.1007/s00521-025-11000-3.
dc.identifier.doi10.1007/s00521-025-11000-3
dc.identifier.issn0941-0643
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/54216
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofNeural Computing and Applications (2025), vol. 37
dc.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Navarra//0011–0537-2021–000050/
dc.relation.publisherversionhttps://doi.org/10.1007/s00521-025-11000-3
dc.rights© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArterial spin labellingen
dc.subjectImage registrationen
dc.subjectImage similarityen
dc.subjectTemporal signal-to-noise ratioen
dc.titleAdvancing ASL kidney image registration: a tailored pipeline with VoxelMorphen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
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relation.isAuthorOfPublicationf51e7976-2044-4ed0-9aff-ec7653f7b945
relation.isAuthorOfPublication5cfc3bb5-70a4-49da-9dcb-ea6cd22e0780
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