{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T09:40:04Z","timestamp":1744882804589,"version":"3.40.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031869198","type":"print"},{"value":"9783031869204","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-86920-4_11","type":"book-chapter","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T08:58:12Z","timestamp":1744880292000},"page":"119-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["QID$$^2$$: An Image-Conditioned Diffusion Model for\u00a0Q-Space Up-Sampling of\u00a0DWI Data"],"prefix":"10.1007","author":[{"given":"Zijian","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jueqi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Archana","family":"Venkataraman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"issue":"1","key":"11_CR1","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S0006-3495(94)80775-1","volume":"66","author":"PJ Basser","year":"1994","unstructured":"Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J . 66(1), 259\u2013267 (1994)","journal-title":"Biophys. J ."},{"issue":"1","key":"11_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/TMI.2017.2756072","volume":"37","author":"J Cheng","year":"2017","unstructured":"Cheng, J., Shen, D., Yap, P.T., Basser, P.J.: Single-and multiple-shell uniform sampling schemes for diffusion MRI using spherical codes. IEEE Trans. Med. Imaging 37(1), 185\u2013199 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR3","unstructured":"Chung, M.K., Chen, Z.: Embedding of functional human brain networks on a sphere. arXiv preprint arXiv:2204.03653 (2022)"},{"key":"11_CR4","unstructured":"Doshi, A., Gerke, L., Marchione, J., Bou-Haidar, P., Delman, B.: Physiologic evaluation of the brain with magnetic resonance imaging. Youmans and Winn Neurological Surgery. 7th ed. New York: Elsevier, pp. 69\u201395 (2017)"},{"key":"11_CR5","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"11_CR6","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.mri.2021.12.011","volume":"87","author":"RR Jha","year":"2022","unstructured":"Jha, R.R., Jaswal, G., Bhavsar, A., Nigam, A.: Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction framework. Magn. Reson. Imaging 87, 133\u2013156 (2022)","journal-title":"Magn. Reson. Imaging"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Kim, B., Han, I., Ye, J.C.: DiffuseMorph: unsupervised deformable image registration using diffusion model. In: European Conference on Computer Vision, pp. 347\u2013364. Springer (2022)","DOI":"10.1007\/978-3-031-19821-2_20"},{"issue":"6","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.2214\/AJR.06.1403","volume":"188","author":"DM Koh","year":"2007","unstructured":"Koh, D.M., Collins, D.J.: Diffusion-weighted MRI in the body: applications and challenges in oncology. Am. J. Roentgenol. 188(6), 1622\u20131635 (2007)","journal-title":"Am. J. Roentgenol."},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Zero-shot medical image translation via frequency-guided diffusion models. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3325703"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Michailovich, O., Rathi, Y.: Fast and accurate reconstruction of HARDI data using compressed sensing. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 607\u2013614. Springer (2010)","DOI":"10.1007\/978-3-642-15705-9_74"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Rahman, A., Valanarasu, J.M.J., Hacihaliloglu, I., Patel, V.M.: Ambiguous medical image segmentation using diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11536\u201311546 (2023)","DOI":"10.1109\/CVPR52729.2023.01110"},{"key":"11_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1007\/978-3-030-87234-2_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"M Ren","year":"2021","unstructured":"Ren, M., Kim, H., Dey, N., Gerig, G.: Q-space conditioned translation networks for directional synthesis of diffusion weighted images from multi-modal structural MRI. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 530\u2013540. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_50"},{"issue":"10","key":"11_CR15","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1007\/s00723-022-01482-y","volume":"53","author":"SM Rezaeijo","year":"2022","unstructured":"Rezaeijo, S.M., Entezari Zarch, H., Mojtahedi, H., Chegeni, N., Danyaei, A.: Feasibility study of synthetic DW-MR images with different b values compared with real DW-MR images: quantitative assessment of three models based-deep learning including CycleGAN, Pix2PiX, and DC2Anet. Appl. Magn. Reson. 53(10), 1407\u20131429 (2022)","journal-title":"Appl. Magn. Reson."},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Shaham, T.R., Dekel, T., Michaeli, T.: SinGAN: learning a generative model from a single natural image. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4570\u20134580 (2019)","DOI":"10.1109\/ICCV.2019.00467"},{"key":"11_CR19","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Strike, L.T., et al.: Queensland Twin Imaging (QTIM) (2023). https:\/\/doi.org\/10.18112\/openneuro.ds004169.v1.0.7","DOI":"10.18112\/openneuro.ds004169.v1.0.7"},{"issue":"2","key":"11_CR21","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3988\/jcn.2018.14.2.129","volume":"14","author":"WS Tae","year":"2018","unstructured":"Tae, W.S., Ham, B.J., Pyun, S.B., Kang, S.H., Kim, B.J.: Current clinical applications of diffusion-tensor imaging in neurological disorders. J. Clin. Neurol. 14(2), 129\u2013140 (2018)","journal-title":"J. Clin. Neurol."},{"issue":"1","key":"11_CR22","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1038\/s41598-024-53278-8","volume":"14","author":"H Tatekawa","year":"2024","unstructured":"Tatekawa, H., et al.: Deep learning-based diffusion tensor image generation model: a proof-of-concept study. Sci. Rep. 14(1), 2911 (2024)","journal-title":"Sci. Rep."},{"issue":"4","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1016\/j.neuroimage.2007.02.016","volume":"35","author":"JD Tournier","year":"2007","unstructured":"Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459\u20131472 (2007)","journal-title":"Neuroimage"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Van\u00a0Essen, D.C., et\u00a0al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Waibel, D.J., R\u00f6ell, E., Rieck, B., Giryes, R., Marr, C.: A diffusion model predicts 3D shapes from 2D microscopy images. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230752"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Wang, J., Levman, J., Pinaya, W.H.L., Tudosiu, P.D., Cardoso, M.J., Marinescu, R.: InverseSR: 3D brain MRI super-resolution using a latent diffusion model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 438\u2013447. Springer (2023)","DOI":"10.1007\/978-3-031-43999-5_42"},{"issue":"4","key":"11_CR27","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"11_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118651","volume":"245","author":"FC Yeh","year":"2021","unstructured":"Yeh, F.C., Irimia, A., de Almeida Bastos, D.C., Golby, A.J.: Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 245, 118651 (2021)","journal-title":"Neuroimage"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, H., Deng, C., Wang, Y., Ma, J.: Better fibre orientation estimation with single-shell diffusion MRI using spherical U-Net. In: International Conference of Pioneering Computer Scientists, Engineers and Educators, pp. 3\u201312. Springer (2023)","DOI":"10.1007\/978-981-99-5971-6_1"}],"container-title":["Lecture Notes in Computer Science","Computational Diffusion MRI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-86920-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T08:58:24Z","timestamp":1744880304000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-86920-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031869198","9783031869204"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-86920-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"18 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CDMRI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Diffusion MRI","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cdmri2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cmic.cs.ucl.ac.uk\/cdmri\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}