{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:24:01Z","timestamp":1763018641194,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031460043"},{"type":"electronic","value":"9783031460050"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46005-0_9","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:01:36Z","timestamp":1696651296000},"page":"96-107","source":"Crossref","is-referenced-by-count":3,"title":["Diffusion-Based Graph Super-Resolution with\u00a0Application to\u00a0Connectomics"],"prefix":"10.1007","author":[{"given":"Nishant","family":"Rajadhyaksha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5595-6673","authenticated-orcid":false,"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1002\/mrm.22924","volume":"65","author":"JD Tournier","year":"2011","unstructured":"Tournier, J.D., Mori, S., Leemans, A.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65, 1532\u20131556 (2011)","journal-title":"Magn. Reson. Med."},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1002\/mp.12132","volume":"44","author":"K Bahrami","year":"2017","unstructured":"Bahrami, K., Shi, F., Rekik, I., Gao, Y., Shen, D.: 7T-guided super-resolution of 3T MRI. Med. Phys. 44, 1661\u20131677 (2017)","journal-title":"Med. Phys."},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/jimaging7060101","volume":"7","author":"P Kaur","year":"2021","unstructured":"Kaur, P., Sao, A.K., Ahuja, C.K.: Super resolution of magnetic resonance images. J. Imaging 7, 101 (2021)","journal-title":"J. Imaging"},{"key":"9_CR4","first-page":"430","volume":"12906","author":"Y Sui","year":"2021","unstructured":"Sui, Y., Afacan, O., Gholipour, A., Warfield, S.K.: MRI super-resolution through generative degradation learning. Med. Image Comput. Comput. Assist. Interv. 12906, 430\u2013440 (2021)","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Zhang, K., et al.: SOUP-GAN: Super-resolution MRI using generative adversarial networks. Tomography 8, 905\u2013919 (2022)","DOI":"10.3390\/tomography8020073"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1002\/mp.12132","volume":"44","author":"K Bahrami","year":"2017","unstructured":"Bahrami, K., Shi, F., Rekik, I., Gao, Y., Shen, D.: 7t-guided super-resolution of 3T MRI. Med. Phys. 44, 1661\u20131677 (2017)","journal-title":"Med. Phys."},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/nn.4502","volume":"20","author":"DS Bassett","year":"2017","unstructured":"Bassett, D.S., Sporns, O.: Network neuroscience. Nat. Neurosci. 20, 353 (2017)","journal-title":"Nat. Neurosci."},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1038\/s41583-019-0177-6","volume":"20","author":"MP van den Heuvel","year":"2019","unstructured":"van den Heuvel, M.P., Sporns, O.: A cross-disorder connectome landscape of brain dysconnectivity. Nat. Rev. Neurosci. 20, 435\u2013446 (2019)","journal-title":"Nat. Rev. Neurosci."},{"key":"9_CR9","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/nrn3901","volume":"16","author":"A Fornito","year":"2015","unstructured":"Fornito, A., Zalesky, A., Breakspear, M.: The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159\u2013172 (2015)","journal-title":"Nat. Rev. Neurosci."},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.jneumeth.2015.06.016","volume":"253","author":"S Qi","year":"2015","unstructured":"Qi, S., Meesters, S., Nicolay, K., ter Haar Romeny, B.M., Ossenblok, P.: The influence of construction methodology on structural brain network measures: a review. J. Neurosci. Methods 253, 170\u2013182 (2015)","journal-title":"J. Neurosci. Methods"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.tics.2010.04.004","volume":"14","author":"SL Bressler","year":"2010","unstructured":"Bressler, S.L., Menon, V.: Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14, 277\u2013290 (2010)","journal-title":"Trends Cogn. Sci."},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.irbm.2020.08.004","volume":"42","author":"Y Li","year":"2021","unstructured":"Li, Y., Sixou, B., Peyrin, F.: A review of the deep learning methods for medical images super resolution problems. IRBM 42, 120\u2013133 (2021)","journal-title":"IRBM"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Isallari, M., Rekik, I.: GSR-Net: graph super-resolution network for predicting high-resolution from low-resolution functional brain connectomes. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 139\u2013149. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59861-7_15","DOI":"10.1007\/978-3-030-59861-7_15"},{"key":"9_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-030-78191-0_16","volume-title":"Information Processing in Medical Imaging","author":"I Mhiri","year":"2021","unstructured":"Mhiri, I., Nebli, A., Mahjoub, M.A., Rekik, I.: Non-isomorphic inter-modality graph alignment and synthesis for holistic brain mapping. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 203\u2013215. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_16"},{"key":"9_CR15","doi-asserted-by":"publisher","unstructured":"Pala, F., Mhiri, I., Rekik, I.: Template-based inter-modality super-resolution of brain connectivity. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds.) PRIME 2021. LNCS, vol. 12928, pp. 70\u201382. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87602-9_7","DOI":"10.1007\/978-3-030-87602-9_7"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Mhiri, I., Mahjoub, M.A., Rekik, I.: StairwayGraphNet for inter- and intra-modality multi-resolution brain graph alignment and synthesis. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 140\u2013150. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87589-3_15","DOI":"10.1007\/978-3-030-87589-3_15"},{"key":"9_CR17","first-page":"3438","volume":"34","author":"D Chen","year":"2020","unstructured":"Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. Proc. AAAI Conf. Artif. Intell. 34, 3438\u20133445 (2020)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"9_CR18","unstructured":"Ansari, A.F., Scarlett, J., Soh, H.: A characteristic function approach to deep implicit generative modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7478\u20137487 (2020)"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"9_CR20","doi-asserted-by":"publisher","unstructured":"Yu, W., Heber, S., Pock, T.: Learning reaction-diffusion models for image inpainting. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 356\u2013367. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24947-6_29","DOI":"10.1007\/978-3-319-24947-6_29"},{"key":"9_CR21","unstructured":"Leng, Y., et al.: Binauralgrad: a two-stage conditional diffusion probabilistic model for binaural audio synthesis. Adv. Neural. Inf. Process. Syst. 35, 23689\u201323700 (2022)"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Pascual, S., Bhattacharya, G., Yeh, C., Pons, J., Serr\u00e0, J.: Full-band general audio synthesis with score-based diffusion. In: ICASSP 2023\u20132023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10096760"},{"key":"9_CR23","unstructured":"Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., Fleet, D.J.: Video diffusion models (2022)"},{"key":"9_CR24","unstructured":"Molad, E., et al.: Dreamix: video diffusion models are general video editors (2023)"},{"key":"9_CR25","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020)"},{"key":"9_CR26","doi-asserted-by":"crossref","unstructured":"Lee, J.S., Kim, J., Kim, P.M.: Score-based generative modeling for de novo protein design. Nat. Comput. Sci. (2023)","DOI":"10.21203\/rs.3.rs-1855828\/v1"},{"key":"9_CR27","first-page":"11287","volume":"34","author":"A Vahdat","year":"2021","unstructured":"Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. Adv. Neural. Inf. Process. Syst. 34, 11287\u201311302 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR28","unstructured":"Jo, J., Lee, S., Hwang, S.J.: Score-based generative modeling of graphs via the system of stochastic differential equations. In: International Conference on Machine Learning, pp. 10362\u201310383. PMLR (2022)"},{"key":"9_CR29","unstructured":"Nichol, A., et al.: Glide: towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9_CR31","unstructured":"Chen, T.: On the importance of noise scheduling for diffusion models (2023)"},{"key":"9_CR32","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015)"},{"key":"9_CR33","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUS) (2020)"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Nagi, J., et al.:. Max-pooling convolutional neural networks for vision-based hand gesture recognition, pp. 342\u2013347 (2011)","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"9_CR35","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance (2022)"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: Longitudinal test-retest neuroimaging data from healthy young adults in southwest china. Sci. Data 4 (2017)","DOI":"10.1038\/sdata.2017.17"},{"key":"9_CR37","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, 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"9_CR38","doi-asserted-by":"crossref","unstructured":"Dosenbach, N.U., et al.: Prediction of individual brain maturity using FMRI. Science 329, 1358\u20131361 (2010)","DOI":"10.1126\/science.1194144"},{"key":"9_CR39","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"9_CR40","unstructured":"Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023)"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46005-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:01:51Z","timestamp":1696651311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46005-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031460043","9783031460050"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46005-0_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]}}}