{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:47:27Z","timestamp":1765547247455,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T00:00:00Z","timestamp":1715990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T00:00:00Z","timestamp":1715990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2020-116417RB-C41"],"award-info":[{"award-number":["PID2020-116417RB-C41"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Magnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient\u2019s facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Using the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient\u2019s face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 \u00b1 0.19 mm and a target registration error of 4.07 \u00b1 1.28 mm, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03102-5","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T11:01:50Z","timestamp":1716030110000},"page":"1367-1374","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-6289","authenticated-orcid":false,"given":"Manuel","family":"Villa","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-6596","authenticated-orcid":false,"given":"Jaime","family":"Sancho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-1236","authenticated-orcid":false,"given":"Gonzalo","family":"Rosa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3440","authenticated-orcid":false,"given":"Miguel","family":"Chavarrias","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-1511","authenticated-orcid":false,"given":"Eduardo","family":"Juarez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2411-9132","authenticated-orcid":false,"given":"Cesar","family":"Sanz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"key":"3102_CR1","unstructured":"BRAINLAB AG (2006) Tracking system for medical equipment with infrared transmission. Published as EP1733693A1"},{"key":"3102_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2021.06.032","author":"S Chidambaram","year":"2021","unstructured":"Chidambaram S, Stifano V, Demetres M, Teyssandier M, Palumbo MC, Redaelli A, Olivi A, Apuzzo ML, Pannullo SC (2021) Applications of augmented reality in the neurosurgical operating room: a systematic review of the literature. J Clin Neurosci. https:\/\/doi.org\/10.1016\/j.jocn.2021.06.032","journal-title":"J Clin Neurosci"},{"key":"3102_CR3","doi-asserted-by":"publisher","unstructured":"Choi S, Zhou QY, Koltun V (2015) Robust reconstruction of indoor scenes. https:\/\/doi.org\/10.1109\/CVPR.2015.7299195","DOI":"10.1109\/CVPR.2015.7299195"},{"key":"3102_CR4","doi-asserted-by":"crossref","unstructured":"Claus D, Fitzgibbon AW (2005) A rational function lens distortion model for general cameras. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 213\u2013219","DOI":"10.1109\/CVPR.2005.43"},{"key":"3102_CR5","doi-asserted-by":"publisher","unstructured":"Drouin S, Kersten-Oertel M, Chen SJS, Collins DL (2012) A realistic test and development environment for mixed reality in neurosurgery. https:\/\/doi.org\/10.1007\/978-3-642-32630-1_2","DOI":"10.1007\/978-3-642-32630-1_2"},{"key":"3102_CR6","doi-asserted-by":"publisher","DOI":"10.3171\/2018.8.FOCUS18191","author":"A Hale","year":"2018","unstructured":"Hale A, Stonko D, Wang L, Strother M, Chambless L (2018) Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging. Neurosurg Focus. https:\/\/doi.org\/10.3171\/2018.8.FOCUS18191","journal-title":"Neurosurg Focus"},{"key":"3102_CR7","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276407","author":"S Katz","year":"2007","unstructured":"Katz S, Tal A, Basri R (2007) Direct visibility of point sets. ACM Trans Graph. https:\/\/doi.org\/10.1145\/1276377.1276407","journal-title":"ACM Trans Graph"},{"key":"3102_CR8","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2019.11.113","author":"C Liang","year":"2019","unstructured":"Liang C, Li M, Gong J, Zhang B, Lin C, He H, Zhang K, Guo Y (2019) A new application of ultrasound-magnetic resonance multimodal fusion virtual navigation in glioma surgery. Ann Transl Med. https:\/\/doi.org\/10.21037\/atm.2019.11.113","journal-title":"Ann Transl Med"},{"key":"3102_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2023.1239764","author":"O MacCormac","year":"2023","unstructured":"MacCormac O, Noonan P, Janatka M, Horgan CC, Bahl A, Qiu J, Elliot M, Trotouin T, Jacobs J, Patel S, Bergholt MS, Ashkan K, Ourselin S, Ebner M, Vercauteren T, Shapey J (2023) Lightfield hyperspectral imaging in neuro-oncology surgery: an ideal 0 and 1 study. Front Neurosci. https:\/\/doi.org\/10.3389\/fnins.2023.1239764","journal-title":"Front Neurosci"},{"key":"3102_CR10","unstructured":"Microsoft (2023) Azure Kinect DK specifications. https:\/\/learn.microsoft.com\/en-us\/azure\/kinect-dk\/hardware-specification. Accessed 25 Oct 2023"},{"key":"3102_CR11","doi-asserted-by":"publisher","unstructured":"Morales Mojica CM, Velazco-Garcia JD, Pappas EP, Birbilis TA, Becker A, Leiss EL, Webb A, Seimenis I, Tsekos NV (2021) A holographic augmented reality interface for visualizing of MRI data and planning of neurosurgical procedures. J Digit Imaging. https:\/\/doi.org\/10.1007\/s10278-020-00412-3","DOI":"10.1007\/s10278-020-00412-3"},{"key":"3102_CR12","unstructured":"NaturalPoint (2023) Optitrack system. https:\/\/optitrack.com\/. Accessed 25 Oct 2023"},{"key":"3102_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17934-1_7","author":"K Revett","year":"2011","unstructured":"Revett K (2011) An introduction to magnetic resonance imaging: from image acquisition to clinical diagnosis. Innov Intell Image Anal. https:\/\/doi.org\/10.1007\/978-3-642-17934-1_7","journal-title":"Innov Intell Image Anal"},{"key":"3102_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2023.102893","author":"J Sancho","year":"2023","unstructured":"Sancho J, Villa M, Chavarr\u00edas M, Juarez E, Lagares A, Sanz C (2023) Slimbrain: augmented reality real-time acquisition and processing system for hyperspectral classification mapping with depth information for in-vivo surgical procedures. J Syst Archit. https:\/\/doi.org\/10.1016\/j.sysarc.2023.102893","journal-title":"J Syst Archit"},{"key":"3102_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.wneu.2019.01.046","author":"CA Sarkiss","year":"2019","unstructured":"Sarkiss CA, Germano IM (2019) Machine learning in neuro-oncology: can data analysis from 5346 patients change decision-making paradigms? World Neurosurg. https:\/\/doi.org\/10.1016\/j.wneu.2019.01.046","journal-title":"World Neurosurg"},{"key":"3102_CR16","doi-asserted-by":"publisher","DOI":"10.1177\/107327480301000203","author":"M Schulder","year":"2003","unstructured":"Schulder M, Carmel PW (2003) Intraoperative magnetic resonance imaging: impact on brain tumor surgery. Cancer Control. https:\/\/doi.org\/10.1177\/107327480301000203","journal-title":"Cancer Control"},{"key":"3102_CR17","doi-asserted-by":"crossref","unstructured":"Segal AV, H\u00e4hnel D, Thrun S (2009) Generalized-ICP","DOI":"10.15607\/RSS.2009.V.021"},{"key":"3102_CR18","doi-asserted-by":"publisher","unstructured":"Strobl KH, Hirzinger G (2011) More accurate pinhole camera calibration with imperfect planar target. https:\/\/doi.org\/10.1109\/ICCVW.2011.6130369","DOI":"10.1109\/ICCVW.2011.6130369"},{"issue":"2","key":"3102_CR19","doi-asserted-by":"publisher","first-page":"113","DOI":"10.3340\/jkns.2022.0130","volume":"66","author":"N Sung Hyun","year":"2023","unstructured":"Sung Hyun N, Pyung Goo C, Keung Nyun K, Sang Hyun K, Dong AhS (2023) Artificial intelligence for neurosurgery: current state and future directions. J. Korean Neurosurg. Soc. 66(2):113. https:\/\/doi.org\/10.3340\/jkns.2022.0130","journal-title":"J. Korean Neurosurg. Soc."},{"key":"3102_CR20","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.29012","author":"CM Tempany","year":"2015","unstructured":"Tempany CM, Jayender J, Kapur T, Bueno R, Golby A, Agar N, Jolesz FA (2015) Multimodal imaging for improved diagnosis and treatment of cancers. Cancer. https:\/\/doi.org\/10.1002\/cncr.29012","journal-title":"Cancer"},{"key":"3102_CR21","doi-asserted-by":"publisher","unstructured":"Terzakis G, Lourakis M (2020). A consistently fast and globally optimal solution to the perspective-n-point problem. https:\/\/doi.org\/10.1007\/978-3-030-58452-8_28","DOI":"10.1007\/978-3-030-58452-8_28"},{"key":"3102_CR22","unstructured":"Tonarelli L (2013) Magnetic resonance imaging of brain tumor. CEwebsource.com"},{"key":"3102_CR23","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.media.2016.06.011","volume":"33","author":"T Ungi","year":"2016","unstructured":"Ungi T, Lasso A, Fichtinger G (2016) Open-source platforms for navigated image-guided interventions. Med Image Anal 33:181\u2013186. https:\/\/doi.org\/10.1016\/j.media.2016.06.011","journal-title":"Med Image Anal"},{"issue":"10","key":"3102_CR24","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/34.159901","volume":"14","author":"J Weng","year":"1992","unstructured":"Weng J, Cohen P, Herniou M (1992) Camera calibration with distortion models and accuracy evaluation. IEEE Trans Pattern Anal Mach Intell 14(10):965\u2013980. https:\/\/doi.org\/10.1109\/34.159901","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3102_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-023-1423-y","author":"W Wu","year":"2023","unstructured":"Wu W, Peng H, Yu S (2023) Yunet: a tiny millisecond-level face detector. Mach Intell Res. https:\/\/doi.org\/10.1007\/s11633-023-1423-y","journal-title":"Mach Intell Res"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03102-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03102-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03102-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T17:15:42Z","timestamp":1720458942000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03102-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,18]]},"references-count":25,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["3102"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03102-5","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2024,5,18]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}