{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:26:54Z","timestamp":1763018814154,"version":"3.44.0"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031966279"},{"type":"electronic","value":"9783031966286"}],"license":[{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T00:00:00Z","timestamp":1754179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-96628-6_13","type":"book-chapter","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T07:41:08Z","timestamp":1754120468000},"page":"187-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["3D Shape-to-Image Brownian Bridge Diffusion for\u00a0Brain MRI Synthesis from\u00a0Cortical Surfaces"],"prefix":"10.1007","author":[{"given":"Fabian","family":"Bongratz","sequence":"first","affiliation":[]},{"given":"Yitong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sama","family":"Elbaroudy","sequence":"additional","affiliation":[]},{"given":"Christian","family":"Wachinger","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103093","volume":"93","author":"F Bongratz","year":"2024","unstructured":"Bongratz, F., Rickmann, A.M., Wachinger, C.: Neural deformation fields for template-based reconstruction of cortical surfaces from mri. Med. Image Anal. 93, 103093 (2024)","journal-title":"Med. Image Anal."},{"issue":"2","key":"13_CR2","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1167\/tvst.10.2.13","volume":"10","author":"P Burlina","year":"2021","unstructured":"Burlina, P., Joshi, N., Paul, W., Pacheco, K.D., Bressler, N.M.: Addressing artificial intelligence bias in retinal diagnostics. Transl. Vis. Sci. Technol. 10(2), 13\u201313 (2021)","journal-title":"Transl. Vis. Sci. Technol."},{"issue":"11","key":"13_CR3","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1109\/TMI.2006.880588","volume":"25","author":"O Camara","year":"2006","unstructured":"Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D., Fox, N.: Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med. Imaging 25(11), 1417\u20131430 (2006)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Castellano\u00a0Smith, A.D., Crum, W.R., Hill, D.L.G., Thacker, N.A., Bromiley, P.A.: Biomechanical simulation of atrophy in mr images. In: Sonka, M., Fitzpatrick, J.M. (eds.) Medical Imaging 2003: Image Processing, vol.\u00a05032, p.\u00a0481. SPIE, May 2003","DOI":"10.1117\/12.480412"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Cruz, R.S., Lebrat, L., Bourgeat, P., Fookes, C., Fripp, J., Salvado, O.: Deepcsr: a 3d deep learning approach for cortical surface reconstruction. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, January 2021","DOI":"10.1109\/WACV48630.2021.00085"},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1006\/nimg.1998.0395","volume":"9","author":"AM Dale","year":"1999","unstructured":"Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis. Neuroimage 9(2), 179\u2013194 (1999)","journal-title":"Neuroimage"},{"issue":"3","key":"13_CR7","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/0146-664X(80)90054-4","volume":"14","author":"PE Danielsson","year":"1980","unstructured":"Danielsson, P.E.: Euclidean distance mapping. Comput. Graphics Image Process. 14(3), 227\u2013248 (1980)","journal-title":"Comput. Graphics Image Process."},{"key":"13_CR8","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. In: NeurIPS (2021)"},{"issue":"7","key":"13_CR9","doi-asserted-by":"publisher","first-page":"4084","DOI":"10.1109\/JBHI.2024.3385504","volume":"28","author":"Z Dorjsembe","year":"2024","unstructured":"Dorjsembe, Z., Pao, H.K., Odonchimed, S., Xiao, F.: Conditional diffusion models for semantic 3d brain mri synthesis. IEEE J. Biomed. Health Inform. 28(7), 4084\u20134093 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"13_CR10","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. NeuroImage 62(2), 774\u2013781 (2012)","journal-title":"FreeSurfer. NeuroImage"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. 97(20), 11050\u201311055 (Sep 2000), publisher: Proceedings of the National Academy of Sciences","DOI":"10.1073\/pnas.200033797"},{"key":"13_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol.\u00a027. Curran Associates, Inc. (2014)"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Gopinath, K., Desrosiers, C., Lombaert, H.: Segrecon: Learning joint brain surface reconstruction and segmentation from images. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference. Strasbourg, France, September 27 \u2013 October 1, 2021, Proceedings, Part VII, pp. 650\u2013659. Springer, Heidelberg (2021)","DOI":"10.1007\/978-3-030-87234-2_61"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Gopinath, K., et al.: Synthetic data in generalizable, learning-based neuroimaging. Imaging Neuroscience (2024)","DOI":"10.1162\/imag_a_00337"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Han, K., et al.: MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation, pp. 759\u2013769. Springer Nature Switzerland (2023)","DOI":"10.1007\/978-3-031-43907-0_72"},{"issue":"3","key":"13_CR16","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.neuroimage.2004.06.043","volume":"23","author":"X Han","year":"2004","unstructured":"Han, X., Pham, D.L., Tosun, D., Rettmann, M.E., Xu, C., Prince, J.L.: Cruise: cortical reconstruction using implicit surface evolution. Neuroimage 23(3), 997\u20131012 (2004)","journal-title":"Neuroimage"},{"key":"13_CR17","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol.\u00a033, pp. 6840\u20136851. Curran Associates, Inc. (2020)"},{"key":"13_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119474","volume":"260","author":"A Hoopes","year":"2022","unstructured":"Hoopes, A., Mora, J.S., Dalca, A.V., Fischl, B., Hoffmann, M.: Synthstrip: skull-stripping for any brain image. Neuroimage 260, 119474 (2022)","journal-title":"Neuroimage"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. CVPR (2017)","DOI":"10.1109\/CVPR.2017.632"},{"issue":"5","key":"13_CR20","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TMI.2006.873221","volume":"25","author":"B Karacali","year":"2006","unstructured":"Karacali, B., Davatzikos, C.: Simulation of tissue atrophy using a topology preserving transformation model. IEEE Trans. Med. Imaging 25(5), 649\u2013652 (2006)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"13_CR21","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.neuroimage.2016.03.061","volume":"134","author":"B Khanal","year":"2016","unstructured":"Khanal, B., Lorenzi, M., Ayache, N., Pennec, X.: A biophysical model of brain deformation to simulate and analyze longitudinal mris of patients with Alzheimer\u2019s disease. Neuroimage 134, 35\u201352 (2016)","journal-title":"Neuroimage"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Kim, K., et al.: Controllable text-to-image synthesis for multi-modality mr images. In: 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 7921\u20137930. IEEE, January 2024","DOI":"10.1109\/WACV57701.2024.00775"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Konz, N., Chen, Y., Dong, H., Mazurowski, M.A.: Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion Models, pp. 88\u201398. Springer Nature Switzerland (2024)","DOI":"10.1007\/978-3-031-72104-5_9"},{"issue":"4","key":"13_CR24","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1038\/s41591-024-02838-6","volume":"30","author":"I Ktena","year":"2024","unstructured":"Ktena, I., et al.: Generative models improve fairness of medical classifiers under distribution shifts. Nat. Med. 30(4), 1166\u20131173 (2024)","journal-title":"Nat. Med."},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"Larson, K.E., Oguz, I.: Synthetic atrophy for longitudinal cortical surface analyses. Frontiers in Neuroimaging 1, June 2022","DOI":"10.3389\/fnimg.2022.861687"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Lee, E., Jeong, S., Sohn, K.: EBDM: Exemplar-Guided Image Translation with Brownian-Bridge Diffusion Models, pp. 306\u2013323. Springer Nature Switzerland, October 2024","DOI":"10.1007\/978-3-031-72624-8_18"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Li, B., Xue, K., Liu, B., Lai, Y.K.: Bbdm: Image-to-image translation with brownian bridge diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1952\u20131961 (2023)","DOI":"10.1109\/CVPR52729.2023.00194"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Li, Y., Yakushev, I., Hedderich, D.M., Wachinger, C.: Pasta: Pathology-aware mri to pet cross-modal translation with diffusion models. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 529\u2013540. Springer Nature Switzerland, Cham (2024)","DOI":"10.1007\/978-3-031-72104-5_51"},{"issue":"3","key":"13_CR29","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1006\/nimg.1999.0534","volume":"12","author":"D MacDonald","year":"2000","unstructured":"MacDonald, D., Kabani, N., Avis, D., Evans, A.C.: Automated 3-d extraction of inner and outer surfaces of cerebral cortex from mri. Neuroimage 12(3), 340\u2013356 (2000)","journal-title":"Neuroimage"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Maneewongvatana, S., Mount, D.: Analysis of approximate nearest neighbor searching with clustered point sets, pp. 105\u2013123. American Mathematical Society (Dec 2002)","DOI":"10.1090\/dimacs\/059\/06"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Menten, M.J., Paetzold, J.C., Dima, A., Menze, B.H., Knier, B., Rueckert, D.: Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs, pp. 330\u2013340. Springer Nature Switzerland (2022)","DOI":"10.1007\/978-3-031-16452-1_32"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., Shan, Y.: T2i-adapter: learning adapters to dig out more controllable ability for text-to-image diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 4296\u20134304 (2024)","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Narasimhaswamy, S., Bhattacharya, U., Chen, X., Dasgupta, I., Mitra, S., Hoai, M.: Handiffuser: text-to-image generation with realistic hand appearances. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2468\u20132479 (2024)","DOI":"10.1109\/CVPR52733.2024.00239"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"issue":"3","key":"13_CR35","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1016\/j.neuroimage.2011.02.046","volume":"56","author":"B Patenaude","year":"2011","unstructured":"Patenaude, B., Smith, S.M., Kennedy, D.N., Jenkinson, M.: A bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3), 907\u2013922 (2011)","journal-title":"Neuroimage"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Peng, W., Adeli, E., Bosschieter, T., Park, S.H., Zhao, Q., Pohl, K.M.: Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model, pp. 14\u201324. Springer Nature Switzerland (2023)","DOI":"10.1007\/978-3-031-43993-3_2"},{"key":"13_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103325","volume":"98","author":"W Peng","year":"2024","unstructured":"Peng, W., Bosschieter, T., Ouyang, J., Paul, R., Sullivan, E.V., Pfefferbaum, A., Adeli, E., Zhao, Q., Pohl, K.M.: Metadata-conditioned generative models to synthesize anatomically-plausible 3d brain mris. Med. Image Anal. 98, 103325 (2024)","journal-title":"Med. Image Anal."},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Pinaya, W.H.L., et al.: Brain Imaging Generation with Latent Diffusion Models, p. 117\u2013126. Springer Nature Switzerland (2022)","DOI":"10.1007\/978-3-031-18576-2_12"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10674\u201310685. IEEE, June 2022","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"13_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102576","volume":"82","author":"F Rusak","year":"2022","unstructured":"Rusak, F., Santa Cruz, R., Lebrat, L., Hlinka, O., Fripp, J., Smith, E., Fookes, C., Bradley, A.P., Bourgeat, P.: Quantifiable brain atrophy synthesis for benchmarking of cortical thickness estimation methods. Med. Image Anal. 82, 102576 (2022)","journal-title":"Med. Image Anal."},{"issue":"3","key":"13_CR41","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.media.2010.02.002","volume":"14","author":"S Sharma","year":"2010","unstructured":"Sharma, S., Noblet, V., Rousseau, F., Heitz, F., Rumbach, L., Armspach, J.P.: Evaluation of brain atrophy estimation algorithms using simulated ground-truth data. Med. Image Anal. 14(3), 373\u2013389 (2010)","journal-title":"Med. Image Anal."},{"key":"13_CR42","unstructured":"Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. pp. 2256\u20132265. ICML\u201915, JMLR.org (2015)"},{"key":"13_CR43","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional gans. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8798\u20138807. IEEE, June 2018","DOI":"10.1109\/CVPR.2018.00917"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"Wu, J., Peng, W., Li, B., Zhang, Y., Pohl, K.M.: Evaluating the Quality of Brain MRI Generators, pp. 297\u2013307. Springer Nature Switzerland (2024)","DOI":"10.1007\/978-3-031-72117-5_28"},{"key":"13_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105817","volume":"198","author":"CG Xanthis","year":"2021","unstructured":"Xanthis, C.G., Filos, D., Haris, K., Aletras, A.H.: Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with mri. Comput. Methods Programs Biomed. 198, 105817 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3836\u20133847, October 2023","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"13_CR48","unstructured":"Zhao, S., Chen, D., Chen, Y.C., Bao, J., Hao, S., Yuan, L., Wong, K.Y.K.: Uni-controlnet: All-in-one control to text-to-image diffusion models. Advances in Neural Information Processing Systems 36 (2024)"},{"key":"13_CR49","doi-asserted-by":"crossref","unstructured":"Zhu, B., Gu, L., Zhang, J., Yan, Z., Pan, L., Zhao, Q.: Simulation of organ deformation using boundary element method and meshless shape matching. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3253\u20133256 (2008)","DOI":"10.1109\/IEMBS.2008.4649898"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96628-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T12:10:23Z","timestamp":1757333423000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96628-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"ISBN":["9783031966279","9783031966286"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96628-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"3 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 May 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}