AI for quantifying MRI: reconstruction & statistical analysis
MRI measures beyond what the eye can see, and artificial intelligence (AI) learns beyond what the human mind can perceive. To quantify this information is what drives us in research and teaching.
We are developing methods throughout the chain of MRI acquisition, reconstruction, quantification and statistical analysis. These studies are deeply motivated by clinical needs, and find their application in different disease types.
AI for MRI reconstruction and beyond
MRI provides excellent soft tissue contrast, but is limited by extensive imaging times. We have developed the Recurrent Inference Machine (RIM), a physics-informed neural network for accelerating MRI. It has proven successful in learning domain invariant features. We hold a successful track record in participating in reconstruction challenges and have been winning in FastMRI knee and Calgary brain challenges, while being in the top three of the generalization track of the FastMRI Brain challenge.
Clinical decision making increasingly requires further quantification of the imaging data. We are developing end-to-end models for reconstruction and quantification, in parameter mapping and segmentation of structural and functional MRI.
70% of the world population has little or no access to 1.5T and 3T MRI facilities. Low-field MRI scanners find their application in specific clinical settings, are more affordable and require little support in installation and maintenance. Through deep learning techniques, we aim to accelerate the acquisition and efficiently denoise the images.
Together with the Netherlands Cancer Institute, we are developing methods for image guided radiotheraphy.
Funding: TKI-PPP, ABC, ZonMW, industry
Team: Daisy van den Berg, Dimitris Karkalousos, Kai Lønning, Henk Marquering, Nikos Priovoulos, Gustav Strijkers, Matthan Caan
Machine learning for disease characterization in neuroimaging
Machine learning is a powerful tool for early detection, prediction, and treatment of brain diseases in neuroimaging. We focus on methods for robust extraction of quantitative features from medical images. This includes the use of radiomics-based models for disease diagnosis and prognosis.
Large population studies such as UK Biobank provide an opportunity to train machine learning models on large data and account for physiological variation in the data through normative modeling.
In the context of psychiatric disorders, machine learning can be used to predict treatment outcomes and identify patients who are likely to benefit from specific interventions. Machine learning thus has the potential to revolutionize neuroimaging research and clinical practice by providing non-invasive, reliable indicators of brain health, resilience, and vulnerability before clinical manifestations of disease.
Team: Jerke van den Berg, Mingshi Chen, Diogo Fernandes, Maarten Poirot, Liesbeth Reneman, Henk Marquering, Matthan Caan
Funding: TKI-PPP, Eurostars
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Structural connectivity of thalamic subnuclei in major depressive disorder: An ultra-high resolution diffusion MRI study at 7-Tesla
Liu, W., Heij, J., Liu, S., Liebrand, L., Caan, M., van der Zwaag, W., Veltman, D. J., Lu, L., Aghajani, M. & van Wingen, G., 1 Feb 2025, In: Journal of affective disorders. 370, p. 412-426 15 p.Research output: Contribution to journal › Article › Academic › peer-review
Prediction of methylphenidate treatment response for ADHD using conventional and radiomics T1 and DTI features: Secondary analysis of a randomized clinical trial
Chen, M., van der Pal, Z., Poirot, M. G., Schrantee, A., Bottelier, M., Kooij, S. J. J., Marquering, H. A., Reneman, L. & Caan, M. W. A., 1 Jan 2025, In: NeuroImage: Clinical. 45, 103707.Research output: Contribution to journal › Article › Academic › peer-review
Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group
Poirot, M. G., Boucherie, D. E., Caan, M. W. A., Goya-Maldonado, R., Belov, V., Corruble, E., Colle, R., Couvy-Duchesne, B., Kamishikiryo, T., Shinzato, H., Ichikawa, N., Okada, G., Okamoto, Y., Harrison, B. J., Davey, C. G., Jamieson, A. J., Cullen, K. R., Başgöze, Z., Klimes-Dougan, B. & Mueller, B. A. & 16 others, , 1 Jan 2025, In: Human brain mapping. 46, 1, e70053.Research output: Contribution to journal › Article › Academic › peer-review
Structural connectivity of dopaminergic pathways in major depressive disorder: An ultra-high resolution 7-Tesla diffusion MRI study
Liu, W., Heij, J., Liu, S., Liebrand, L., Caan, M., van der Zwaag, W., Veltman, D. J., Lu, L., Aghajani, M. & van Wingen, G., 1 Dec 2024, In: European neuropsychopharmacology. 89, p. 58-70 13 p.Research output: Contribution to journal › Article › Academic › peer-review
Quantitative MRI at 7-Tesla reveals novel frontocortical myeloarchitecture anomalies in major depressive disorder
Heij, J., van der Zwaag, W., Knapen, T., Caan, M. W. A., Forstman, B., Veltman, D. J., van Wingen, G. & Aghajani, M., Dec 2024, In: Translational psychiatry. 14, 1, 262.Research output: Contribution to journal › Article › Academic › peer-review
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging
Karkalousos, D., Išgum, I., Marquering, H. A. & Caan, M. W. A., 1 Nov 2024, In: Computer methods and programs in biomedicine. 256, 108377.Research output: Contribution to journal › Article › Academic › peer-review
Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke
van Voorst, H., Pitkänen, J., van Poppel, L., de Vries, L., Mojtahedi, M., Martou, L., Emmer, B. J., Roos, Y. B. W. E. M., van Oostenbrugge, R., Postma, A. A., Marquering, H. A., Majoie, C. B. L. M., Curtze, S., Melkas, S., Bentley, P. & Caan, M. W. A., Aug 2024, In: European radiology. 34, 8, p. 5080-5093 14 p.Research output: Contribution to journal › Article › Academic › peer-review
Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver
Lønning, K., Caan, M. W. A., Nowee, M. E. & Sonke, J.-J., 1 Apr 2024, In: Computerized medical imaging and graphics. 113, 102348.Research output: Contribution to journal › Article › Academic › peer-review
Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial
Poirot, M. G., Ruhe, H. G., Mutsaerts, H.-J. M. M., Maximov, I. I., Groote, I. R., Bjørnerud, A., Marquering, H. A., Reneman, L. & Caan, M. W. A., 1 Mar 2024, In: American journal of psychiatry. 181, 3, p. 223-233 11 p.Research output: Contribution to journal › Article › Academic › peer-review
Whole-liver flip-angle shimming at 7 T using parallel-transmit kT-point pulses and Fourier phase-encoded DREAM B1+ mapping
Runderkamp, B. A., Roos, T., van der Zwaag, W., Strijkers, G. J., Caan, M. W. A. & Nederveen, A. J., Jan 2024, In: Magnetic resonance in medicine. 91, 1, p. 75-90 16 p.Research output: Contribution to journal › Article › Academic › peer-review
Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla
Liu, W., Heij, J., Liu, S., Liebrand, L., Caan, M., van der Zwaag, W., Veltman, D. J., Lu, L., Aghajani, M. & van Wingen, G., 2024, (E-pub ahead of print) In: European archives of psychiatry and clinical neuroscience.Research output: Contribution to journal › Article › Academic › peer-review
Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits
Liebrand, L. C., Karkalousos, D., Poirion, É., Emmer, B. J., Roosendaal, S. D., Marquering, H. A., Majoie, C. B. L. M., Savatovsky, J. & Caan, M. W. A., 2024, (E-pub ahead of print) In: Magnetic Resonance Materials in Physics, Biology and Medicine.Research output: Contribution to journal › Article › Academic › peer-review
Feasibility of detecting atrophy relevant for disability and cognition in multiple sclerosis using 3D-FLAIR
Noteboom, S., van Nederpelt, D. R., Bajrami, A., Moraal, B., Caan, M. W. A., Barkhof, F., Calabrese, M., Vrenken, H., Strijbis, E. M. M., Steenwijk, M. D. & Schoonheim, M. M., Nov 2023, In: Journal of neurology. 270, 11, p. 5201-5210 10 p.Research output: Contribution to journal › Article › Academic › peer-review
Thrombus radiomics in patients with anterior circulation acute ischemic stroke undergoing endovascular treatment
van Voorst, H., Bruggeman, A. A. E., Yang, W., Andriessen, J., Welberg, E., Dutra, B. G., Konduri, P. R., Arrarte Terreros, N., Hoving, J. W., Tolhuisen, M. L., Kappelhof, M., Brouwer, J., Boodt, N., van Kranendonk, K. R., Koopman, M. S., Hund, H. M., Krietemeijer, M., van Zwam, W. H., van Beusekom, H. M. M. & van der Lugt, A. & 5 others, , 1 Sept 2023, In: Journal of neurointerventional surgery. neurintsurg-2022-019085.Research output: Contribution to journal › Article › Academic › peer-review
Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net
van Elst, S., de Bloeme, C. M., Noteboom, S., de Jong, M. C., Moll, A. C., Göricke, S., de Graaf, P. & Caan, M. W. A., 1 May 2023, In: JOURNAL OF MEDICAL IMAGING. 10, 3, p. 034501 034501.Research output: Contribution to journal › Article › Academic › peer-review
Prognostic Value of Thrombus Volume and Interaction With First-Line Endovascular Treatment Device Choice
MR-CLEAN Registry Investigators & MR CLEAN Registry Investigators, 1 Apr 2023, In: Stroke. 54, 4, p. 1056-1065 10 p.Research output: Contribution to journal › Article › Academic › peer-review
A densely interconnected network for deep learning accelerated MRI
Ottesen, J. A., Caan, M. W. A., Groote, I. R. & Bjørnerud, A., Feb 2023, In: Magma (New York, N.Y.). p. 65-77Research output: Contribution to journal › Article › Academic › peer-review
Utilizing 7-Tesla Subthalamic Nucleus Connectivity in Deep Brain Stimulation for Parkinson Disease
Mathiopoulou, V., Rijks, N., Caan, M. W. A., Liebrand, L. C., Ferreira, F., de Bie, R. M. A., van den Munckhof, P., Schuurman, P. R. & Bot, M., 1 Feb 2023, In: Neuromodulation. p. 333-339Research output: Contribution to journal › Article › Academic › peer-review
MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis
Karkalousos, D., Išgum, I., Marquering, H. A. & Caan, M. W. A., 2023, Medical Imaging with Deep Learning 2023, MIDL 2023. ML Research Press, Vol. 227. p. 991-1005 (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis
Karkalousos, D., Išgum, I., Marquering, H. A. & Caan, M. W. A., 2023, p. 991-1005. 15 p.Research output: Contribution to conference › Paper › Academic
See below for examples of internship projects that we offer. The projects may not always be entirely up-to-date, but they give a good impression of the work at our department. In addition, you can find the contact details of supervisors that you can send a message. Also if you have your own project proposal matching our research scope, please don’t hesitate to contact us.