{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:19:30Z","timestamp":1767320370277,"version":"3.48.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095121","type":"print"},{"value":"9783032095138","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-09513-8_12","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:15:12Z","timestamp":1767320112000},"page":"117-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Action to Anatomy - Countering Data Scarcity with Video-Based Training for Ill-Posed MRI Problems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7684-966X","authenticated-orcid":false,"given":"Simon","family":"Graf","sequence":"first","affiliation":[]},{"given":"Walter A.","family":"Wohlgemuth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-1302","authenticated-orcid":false,"given":"Andreas","family":"Deistung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/cmr.b.20034","author":"JP Marques","year":"2005","unstructured":"Marques, J.P., Bowtell, R.: Application of a Fourier-based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility. Conc. Magn. Reson. (2005). https:\/\/doi.org\/10.1002\/cmr.b.20034","journal-title":"Conc. Magn. Reson."},{"key":"12_CR2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21828","author":"T Liu","year":"2009","unstructured":"Liu, T., Spincemaille, P., de Rochefort, L., Kressler, B., Wang, Y.: Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn. Reson. Med. (2009). https:\/\/doi.org\/10.1002\/mrm.21828","journal-title":"Magn. Reson. Med."},{"key":"12_CR3","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3569","author":"A Deistung","year":"2017","unstructured":"Deistung, A., Schweser, F., Reichenbach, J.R.: Overview of quantitative susceptibility mapping. NMR Biomed. (2017). https:\/\/doi.org\/10.1002\/nbm.3569","journal-title":"NMR Biomed."},{"key":"12_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26977","author":"K Hammernik","year":"2018","unstructured":"Hammernik, K., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. (2018). https:\/\/doi.org\/10.1002\/mrm.26977","journal-title":"Magn. Reson. Med."},{"key":"12_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4271","author":"D Polak","year":"2020","unstructured":"Polak, D., et al.: Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM). NMR Biomed. (2020). https:\/\/doi.org\/10.1002\/nbm.4271","journal-title":"NMR Biomed."},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Lai, K.-W., Aggarwal, M., van Zijl, P., Li, X., Sulam, J.: Learned proximal networks for quantitative susceptibility mapping. In: Medical Image Computing and Computer-Assisted Intervention: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_13","DOI":"10.1007\/978-3-030-59713-9_13"},{"key":"12_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083","author":"M Wang","year":"2018","unstructured":"Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing (2018). https:\/\/doi.org\/10.1016\/j.neucom.2018.05.083","journal-title":"Neurocomputing"},{"key":"12_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3117407","author":"H Guan","year":"2022","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. (2022). https:\/\/doi.org\/10.1109\/TBME.2021.3117407","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"12_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7040066","author":"JM Valverde","year":"2021","unstructured":"Valverde, J.M., et al.: Transfer learning in magnetic resonance brain imaging: a systematic review. J. Imaging (2021). https:\/\/doi.org\/10.3390\/jimaging7040066","journal-title":"J. Imaging"},{"key":"12_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104115","author":"MA Morid","year":"2021","unstructured":"Morid, M.A., Borjali, A., Del Fiol, G.: A scoping review of transfer learning research on medical image analysis using ImageNet. Comput. Biol. Med. (2021). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104115","journal-title":"Comput. Biol. Med."},{"key":"12_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105382","author":"Y Chen","year":"2022","unstructured":"Chen, Y., et al.: Generative adversarial networks in medical image augmentation: a review. Comput. Biol. Med. (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105382","journal-title":"Comput. Biol. Med."},{"key":"12_CR12","unstructured":"Bowles, C., et al.: GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018). https:\/\/arxiv.org\/abs\/1810.10863"},{"key":"12_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.03.060","author":"S Bollmann","year":"2019","unstructured":"Bollmann, S., et al.: DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping. Neuroimage (2019). https:\/\/doi.org\/10.1016\/j.neuroimage.2019.03.060","journal-title":"Neuroimage"},{"key":"12_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2024.1366165","author":"S Graf","year":"2024","unstructured":"Graf, S., Wohlgemuth, W.A., Deistung, A.: Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution. Front. Neurosci. (2024). https:\/\/doi.org\/10.3389\/fnins.2024.1366165","journal-title":"Front. Neurosci."},{"key":"12_CR15","doi-asserted-by":"publisher","DOI":"10.1162\/imag_a_00337","author":"K Gopinath","year":"2024","unstructured":"Gopinath, K., et al.: Synthetic data in generalizable, learning-based neuroimaging. Imaging Neurosci. (2024). https:\/\/doi.org\/10.1162\/imag_a_00337","journal-title":"Imaging Neurosci."},{"key":"12_CR16","unstructured":"Kay, W., et al.: The Kinetics Human Action Video Dataset (2017). https:\/\/arxiv.org\/abs\/1705.06950"},{"key":"12_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2293423","author":"W Xue","year":"2014","unstructured":"Xue, W., Zhang, L., Mou, X., Bovik, A.C.: Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Trans. Image Process. (2014). https:\/\/doi.org\/10.1109\/TIP.2013.2293423","journal-title":"IEEE Trans. Image Process."},{"key":"12_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc. (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process. Publ. IEEE Sig. Process. Soc."},{"key":"12_CR19","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Softw. Tools (2000)"},{"key":"12_CR20","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (2016). http:\/\/arxiv.org\/pdf\/1606.06650v1","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"12_CR21","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 1929\u20131958 (2014)"},{"key":"12_CR22","unstructured":"Loshchilov, I., Hutter, F.: Decoupled Weight Decay Regularization (2017). http:\/\/arxiv.org\/pdf\/1711.05101v3"},{"key":"12_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119522","author":"Y Shi","year":"2022","unstructured":"Shi, Y., Feng, R., Li, Z., Zhuang, J., Zhang, Y., Wei, H.: Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: a multi-orientation gradient-echo MRI dataset. Neuroimage (2022). https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119522","journal-title":"Neuroimage"},{"key":"12_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119842","author":"Z Xiong","year":"2023","unstructured":"Xiong, Z., Gao, Y., Liu, F., Sun, H.: Affine transformation edited and refined deep neural network for quantitative susceptibility mapping. Neuroimage (2023). https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119842","journal-title":"Neuroimage"},{"key":"12_CR25","unstructured":"Azizi, S., et al.: Big Self-Supervised Models Advance Medical Image Classification (2021). http:\/\/arxiv.org\/pdf\/2101.05224v2"},{"key":"12_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101840","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. (2021). https:\/\/doi.org\/10.1016\/j.media.2020.101840","journal-title":"Med. Image Anal."},{"key":"12_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.06.014","author":"N Malik","year":"2022","unstructured":"Malik, N., Bzdok, D.: From YouTube to the brain: transfer learning can improve brain-imaging predictions with deep learning. Neural Netw. Off. J. Int. Neural Netw. Soc. (2022). https:\/\/doi.org\/10.1016\/j.neunet.2022.06.014","journal-title":"Neural Netw. Off. J. Int. Neural Netw. Soc."},{"key":"12_CR28","unstructured":"Ke, A., Huang, S.-C., O\u2019Connell, C.P., Klimont, M., Yeung, S., Rajpurkar, P.: Video Pretraining Advances 3D Deep Learning on Chest CT Tasks (2023). http:\/\/arxiv.org\/pdf\/2304.00546v1"},{"key":"12_CR29","doi-asserted-by":"crossref","unstructured":"Dhinagar, N.J., et al.: Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis (2023). http:\/\/arxiv.org\/pdf\/2309.04651v1","DOI":"10.1117\/12.3008837"},{"key":"12_CR30","doi-asserted-by":"publisher","DOI":"10.1186\/s41747-023-00411-3","author":"S Tayebi Arasteh","year":"2024","unstructured":"Tayebi Arasteh, S., Misera, L., Kather, J.N., Truhn, D., Nebelung, S.: Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images. Euro. Radiol. Exp. (2024). https:\/\/doi.org\/10.1186\/s41747-023-00411-3","journal-title":"Euro. Radiol. Exp."},{"key":"12_CR31","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-62294-7","author":"O Jaubert","year":"2024","unstructured":"Jaubert, O., et al.: Training deep learning based dynamic MR image reconstruction using open-source natural videos. Sci. Rep. (2024). https:\/\/doi.org\/10.1038\/s41598-024-62294-7","journal-title":"Sci. Rep."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09513-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:15:14Z","timestamp":1767320114000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09513-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095121","9783032095138"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09513-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}