Prof. Dr. Florian Knoll
Chair of Human Processes / Department Artificial Intelligence in Biomedical Engineering (AIBE)
Prof. Dr. Florian Knoll
Chair of Human Processes / Department Artificial Intelligence in Biomedical Engineering (AIBE)
The goal of my research is the development and application of machine learning methods to medical imaging, and their translation into clinical practice so that they can help patients on a day-to-day level.
Research projects
- Accelerated MR imaging
- Machine Learning for MRI data acquisition and image reconstruction
- Quantitative imaging biomarkers for disease processes
- Increase the global availability of imaging technology in second and third world countries
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Teilprojekt A2
(Third Party Funds Group – Sub project)
Overall project: Quantitative diffusionsgewichtete MRT und Suszeptibilitätskartierung zur Charakterisierung der Gewebemikrostruktur
Project leader:
Term: 1. September 2023 - 31. August 2027
Funding source: DFG / Forschungsgruppe (FOR) -
FOR 5534 TP A2: Quantitative diffusionsgewichtete MRT und Suszeptibilitätskartierung zur Charakterisierung der Gewebemikrostruktur
(Third Party Funds Group – Sub project)
Overall project: FOR 5534: Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultra-hohen Magnetfeldstärken
Project leader: ,
Term: 1. September 2023 - 31. August 2027
Acronym: FOR 5534 TP A2
Funding source: DFG / Forschungsgruppe (FOR)Dieses Projekt ist Teil der Forschungsgruppe (FOR) "Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultrahohen Magnetfeldstärken". Es konzentriert sich auf die Erweiterung, Beschleunigung und Verbesserung der Diffusions- und quantitativen Suszeptibilitäts-Magnetresonanztomographie. Das Arbeitsprogramm ist in zwei Teile gegliedert. Im ersten Teil wird ein beschleunigtes Protokoll für die klinischen Projekte der FOR vorbereitet. Im zweiten Teil sollen eine weitere Beschleunigung sowie Qualitätsverbesserungen erreicht werden. Konkret werden wir eine lokal niedrigrangig regularisierte echoplanare Bildgebungssequenz für die diffusionsgewichtete Bildgebung implementieren. Sie nutzt Datenredundanzen bei Akquisitionen mit mehreren Diffusionskodierungen, um das Signal-Rausch-Verhältnis effektiv zu erhöhen und damit den Akquisitionsprozess zu beschleunigen. Die Sequenz wird im Wesentlichen beliebige Diffusionskodierungsmöglichkeiten ermöglichen (z.B. b-Tensor-Kodierung). In einem zweiten Schritt werden wir eine verschachtelte Mehrschuss-Version dieser Sequenz entwickeln, um Bildverzerrungen zu reduzieren, die bei der 7-Tesla echoplanaren Bildgebung störend sind. Für die quantitative Suszeptibilitätskartierung (QSM) werden wir eine Sequenz mit einer Stack-of-Stars-Aufnahmetrajektorie implementieren. Da die Magnitudenbilder von Gradientenechosequenzen, die zu unterschiedlichen Echozeiten akquiriert werden, Datenredundanzen aufweisen, die mit denen von diffusionskodierten Bildern vergleichbar sind, werden wir bei der Bildrekonstruktion ebenfalls eine lokale Regularisierung niedrigen Ranges verwenden. Die radialen Trajektorien dieser Sequenz sollten für eine unterabgetastete und damit beschleunigte Bildrekonstruktion gut geeignet sein. In einem zweiten Schritt werden wir die Fähigkeiten unserer Sequenz durch eine quasi-kontinuierliche Echozeit-Abtastung erweitern, bei dem jede Speiche ihre eigene optimierte Echozeit hat. Dies wird eine verbesserte Qualität der QSM ermöglichen, wenn Fett im Bild vorhanden ist, wie es häufig bei Muskeluntersuchungen und in der Brustbildgebung der Fall ist. Bezüglich der QSM-Rekonstruktion werden wir Verfahren des tiefen Lernens entwickeln, um eine qualitativ hochwertige Rekonstruktion mit einer geringeren Menge an Bilddaten als bei herkömmlichen Rekonstruktionsansätzen zu ermöglichen. Wir werden bestehende neuronale Netzwerke von niedrigeren Feldstärken auf 7 T anpassen und deren Fähigkeiten so erweitern, dass wir auch atemzyklusabhängige Feldkarten. Dieses Projekt wird parallele Sendemethoden (pTx) vom pTx-Projekt der FOR erhalten. Wir werden die entwickelten Sequenzen nach dem ersten Jahr an die klinischen Projekte der FOR liefern. Darüber hinaus werden wir wesentliche Auswerte- und Bildrekonstruktionsmethoden an die anderen Projekte der FOR transferieren.und quasi-kontinuierliche Echozeiten in die Rekonstruktion integrieren können.
2026
- Brock, L., Liebert, A., Schreiter, H., Skwierawska, D., Ehring, C., Eberle, J.,... Bickelhaupt, S. (2026). Influence of co-registration on lesion characterization in diffusion-weighted breast MRI. Magnetic Resonance Materials in Physics Biology and Medicine. https://doi.org/10.1007/s10334-026-01324-z
- Kim, J., Nickel, M.D., & Knoll, F. (2026). Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.70467
- Rohe, M., Tkotz, K., Nagel, A.M., Sommer, S., Brockmüller, M., Knoll, F.,... Wilferth, T. (2026). Fat/Water Separation at 7 T Using a 3D Radial Sequence With Quasi-Continuous Echo Times. Magnetic Resonance in Medicine, 1-13. https://doi.org/10.1002/mrm.70323
- Stein, E., Vornehm, M., Wetzl, J., Knoll, F., & Giese, D. (2026). A fully automated workflow for acquiring and quantifying 4D flow MRI in the aorta. Poster presentation at 2026 ISMRM & ISMRT Annual Meeting & Exhibition, Cape Town, ZA.
- Tan, Z., Liebig, P.A., Hofmann, A., Laun, F.B., & Knoll, F. (2026). High-Resolution Diffusion-Weighted Imaging With Self-Gated Self-Supervised Unrolled Reconstruction. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.70250
2025
- Ayde, R., Vornehm, M., Zhao, Y., Knoll, F., Wu, E.X., & Sarracanie, M. (2025). MRI at low field: A review of software solutions for improving SNR. NMR in Biomedicine, 38(1). https://doi.org/10.1002/nbm.5268
- Brenes, A., Tan, Z., Bae, J., Solomon, E., Gösche, E., Knoll, F., & Kim, S.G. (2025). Direct Reconstruction of Tracer Kinetic Parameter Maps in Abbreviated Breast MRI. Paper presentation at 2025 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, HI, US.
- Gösche, E., Tan, Z., Flaßkamp, K., Kim, S.G., & Knoll, F. (2025). Comparative Evaluation of Deep Learning and Compressed Sensing Methods for Dynamic Contrast-Enhanced MRI Reconstruction. Poster presentation at 2025 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, HI, US.
- Kim, J., Nickel, M.D., & Knoll, F. (2025). Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data. NMR in Biomedicine, 38(3), e70002. https://doi.org/10.1002/nbm.70002
- Solomon, E., Johnson, P.M., Tan, Z., Tibrewala, R., Lui, Y.W., Knoll, F.,... Heacock, L. (2025). FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI. Radiology: Artificial Intelligence, 7(1). https://doi.org/10.1148/ryai.240345
- Stepansky, L., Ruppel, R., Sommerfeld, L., Kleiß, J.-M., Türkan, K., Arndt, S.,... May, M. (2025). Adrenal gland volume measurement in depressed patients. Journal of Psychiatric Research, 187, 74-79. https://doi.org/10.1016/j.jpsychires.2025.05.013
- Stöcker, T., Keenan, K.E., Knoll, F., Priovoulos, N., Uecker, M., & Zaitsev, M. (2025). Reproducibility and quality assurance in MRI. Magnetic Resonance Materials in Physics Biology and Medicine. https://doi.org/10.1007/s10334-025-01271-1
- Vornehm, M., Chen, C., Sultan, M.A., Arshad, S.M., Han, Y., Knoll, F., & Ahmad, R. (2025). Multi-dynamic deep image prior for cardiac MRI. Magnetic Resonance in Medicine, 94(6), 2668-2679. https://doi.org/10.1002/mrm.70000
- Vornehm, M., Chen, C., Sultan, M.A., Arshad, S.M., Knoll, F., & Ahmad, R. (2025). Motion-Guided Deep Image Prior for Dynamic Cardiac MRI. Paper presentation at 2025 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, HI, US.
- Vornehm, M., Wetzl, J., Giese, D., Fürnrohr, F., Pang, J., Chow, K.,... Knoll, F. (2025). CineVN: Variational network reconstruction for rapid functional cardiac cine MRI. Magnetic Resonance in Medicine, 93(1), 138-150. https://doi.org/10.1002/mrm.30260
2024
- Amsel, D., Vornehm, M., Wetzl, J., Schmidt, M., Tillmanns, C., Gebker, R.,... Kuestner, T. (2024). Deep learning-based image reconstruction for higher resolution cardiac T1 mapping. Poster presentation at 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore, SG.
- Bae, J., Tan, Z., Solomon, E., Huang, Z., Heacock, L., Moy, L.,... Kim, S.G. (2024). Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.30152
- Brock, L., Liebert, A., Schreiter, H., Ehring, C., Eberle, J., Laun, F.B.,... Bickelhaupt, S. (2024). How to best match voxels - evaluating different sequential co-registration strategies for ultra-high b-value DWI in multiparametric breast MRI. Poster presentation at 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore, SG.
- Brock, L., Liebert, A., Schreiter, H., Skwierawska, D., Ehring, C., Eberle, J.,... Bickelhaupt, S. (2024). Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping. In Marc Modat, Žiga Špiclin, Alessa Hering, Ivor Simpson, Wietske Bastiaansen, Tony C. W. Mok (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 127-136). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
- Fürnrohr, F., Wetzl, J., Vornehm, M., Giese, D., & Knoll, F. (2024). Data Consistent Variational Networks for Zero-shot Self-supervised MR Reconstruction. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024 (pp. 316-321). Erlangen, DE: Wiesbaden: Springer Vieweg.
- Gösche, E., Eghbali, R., Knoll, F., & Rauschecker, A.M. (2024). Domain Influence in MRI Medical Image Segmentation: Spatial Versus k-Space Inputs. In Xuanang Xu, Zhiming Cui, Islem Rekik, Xi Ouyang, Kaicong Sun (Eds.), Machine Learning in Medical Imaging (pp. 310-319). Marrakesh, MA: Cham: Springer.
- Kim, J., Nickel, M.D., & Knoll, F. (2024). Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP. In Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP. Hamburg, Germany.
- Kim, J., Nickel, M.D., & Knoll, F. (2024). Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography. In Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography. Singapore, SG.
- Ott, Y., Vornehm, M., Wetzl, J., Giese, D., & Knoll, F. (2024). Training deep learning reconstruction models for radial real-time cardiac cine MRI using synthetic golden-angle data. Paper presentation at 26th Annual Meeting of the German-speaking section of ISMRM, Tübingen, DE.
- Rajput, J.R., Weinmüller, S., Endres, J., Dawood, P., Knoll, F., Maier, A., & Zaiß, M. (2024). Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions. In Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VII (pp. 233-241). Marrakesh, MA: Cham: Springer.
- Tan, Z., Liebig, P.A., Heidemann, R.M., Laun, F.B., & Knoll, F. (2024). Accelerated diffusion-weighted magnetic resonance imaging at 7 T: Joint reconstruction for shift-encoded navigator-based interleaved echo planar imaging (JETS-NAViEPI). Imaging Neuroscience, 2, 1-15. https://doi.org/10.1162/imag_a_00085
- Vornehm, M., Wetzl, J., Fürnrohr, F., Giese, D., Ahmad, R., & Knoll, F. (2024). Low-Latency Reconstruction of Real-Time Cine MRI Using an Unrolled Network. Paper presentation at 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore, SG.
- Vornehm, M., Wetzl, J., Fürnrohr, F., Giese, D., Ahmad, R., & Knoll, F. (2024). Variational Network Meets Conjugate Gradient: Inline Reconstruction and Strain Analysis of Accelerated Cardiac Cine MRI. Poster presentation at 2024 ISMRM & ISMRT Annual Meeting & Exhibition, Singapore, SG.
- Zaiß, M., Rajput, J.R., Dang, H.N., Golkov, V., Cremers, D., Knoll, F., & Maier, A. (2024). Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant. In Andreas Maier, Thomas M. Deserno, Heinz Handels, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2024. BVM 2024 (pp. 94-99). Erlangen, DE: Wiesbaden: Springer Vieweg.
2023
- Amsel, D., Vornehm, M., Wetzl, J., Schmidt, M., & Knoll, F. (2023). Deep learning-based accelerated T1 mapping for cardiac MRI. Poster presentation at 25. Jahrestagung der Deutschen Sektion der ISMRM, Berlin, DE.
- Hammernik, K., Kustner, T., Yaman, B., Huang, Z., Rueckert, D., Knoll, F., & Akcakaya, M. (2023). Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging. IEEE Signal Processing Magazine, 40(1), 98-114. https://doi.org/10.1109/MSP.2022.3215288
- Johnson, P.M., Lin, D.J., Zbontar, J., Zitnick, C.L., Sriram, A., Muckley, M.,... Knoll, F. (2023). Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Radiology, 307(2), e220425-. https://doi.org/10.1148/radiol.220425
- Kim, J., Benkert, T., Riemenschneider, B., Nickel, M.D., & Knoll, F. (2023, June). Analysis of Deep Learning-based Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune. Poster presentation at 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, ON, CA.
- Kim, J., Nickel, M.D., & Knoll, F. (2023). Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems. In Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems. Munich, DE.
- Vornehm, M., Wetzl, J., Giese, D., & Knoll, F. (2023). k-t Adaptive Regularization in Variational Networks for Cardiac Cine Reconstruction. Poster presentation at ISMRM Workshop on Data Sampling & Image Reconstruction, Sedona, AZ, US.
- Vornehm, M., Wetzl, J., Giese, D., & Knoll, F. (2023). Towards Low-Latency Deep Learning-Based Reconstruction of Real-Time Cine MRI. Poster presentation at 25. Jahrestagung der Deutschen Sektion der ISMRM, Berlin, DE.
- Vornehm, M., Wetzl, J., Giese, D., Pang, J., Ahmad, R., & Knoll, F. (2023). Deep Learning-Based Reconstruction of Accelerated Cardiac Cine MRI at 0.55T. Poster presentation at 2023 ISMRM & ISMRT Annual Meeting & Exhibition, Toronto, ON, CA.
- Zach, M., Knoll, F., & Pock, T. (2023). Stable Deep MRI Reconstruction using Generative Priors. IEEE Transactions on Medical Imaging, 1-1. https://doi.org/10.1109/TMI.2023.3311345
2022
- Bae, J., Huang, Z., Knoll, F., Geras, K., Sood, T.P., Feng, L.,... Kim, S.G. (2022). Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach. Magnetic Resonance in Medicine, 87(5), 2536-2550. https://doi.org/10.1002/mrm.29148
- Johnson, P.M., Tong, A., Donthireddy, A., Melamud, K., Petrocelli, R., Smereka, P.,... Knoll, F. (2022). Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate. Journal of Magnetic Resonance Imaging, 56(1), 184-195. https://doi.org/10.1002/jmri.28024
- Khodarahmi, I., Brinkmann, I.M., Lin, D.J., Bruno, M., Johnson, P.M., Knoll, F.,... Fritz, J. (2022). New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First in Vitro Experience at 0.55 T and Comparison with 1.5 T. Investigative Radiology, 57(8), 517-526. https://doi.org/10.1097/RLI.0000000000000866
- Liang, Z., Lee, C.H., Arefin, T.M., Dong, Z., Walczak, P., Shi, S.-H.,... Zhang, J. (2022). Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning. eLife, 11. https://doi.org/10.7554/eLife.72331
- Narnhofer, D., Effland, A., Kobler, E., Hammernik, K., Knoll, F., & Pock, T. (2022). Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction. IEEE Transactions on Medical Imaging, 41(2), 279-291. https://doi.org/10.1109/TMI.2021.3112040
- Radmanesh, A., Muckley, M.J., Murrell, T., Lindsey, E., Sriram, A., Knoll, F.,... Lui, Y.W. (2022). Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI. Radiology: Artificial Intelligence, 4(6). https://doi.org/10.1148/ryai.210313
- Vornehm, M., Wetzl, J., Giese, D., Ahmad, R., & Knoll, F. (2022, August). Spatiotemporal variational neural network for reconstruction of highly accelerated cardiac cine MRI. Poster presentation at Artificial Intelligence in Cardiovascular Magnetic Resonance Imaging - A Joint Summit of the EACVI and SCMR, London.
- Zhao, R., Yaman, B., Zhang, Y., Stewart, R., Dixon, A., Knoll, F.,... Lungren, M.P. (2022). fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01255-z
- Zibetti, M.V.W., Knoll, F., & Regatte, R.R. (2022). Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications. IEEE Transactions on Computational Imaging, 1-1. https://doi.org/10.1109/TCI.2022.3176129
2021
- Huang, Z., Bae, J., Johnson, P.M., Sood, T., Heacock, L., Fogarty, J.,... Knoll, F. (2021). A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction. In Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 45-53). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
- Johnson, P.M., Jeong, G., Hammernik, K., Schlemper, J., Qin, C., Duan, J.,... Knoll, F. (2021). Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. In Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 25-34). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
- Lin, D.J., Johnson, P.M., Knoll, F., & Lui, Y.W. (2021). Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians. Journal of Magnetic Resonance Imaging, 53(4), 1015-1028. https://doi.org/10.1002/jmri.27078
- Maier, O., Baete, S.H., Fyrdahl, A., Hammernik, K., Harrevelt, S., Kasper, L.,... Knoll, F. (2021). CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. Magnetic Resonance in Medicine, 85(4), 1821-1839. https://doi.org/10.1002/mrm.28569
- Muckley, M.J., Ades-Aron, B., Papaioannou, A., Lemberskiy, G., Solomon, E., Lui, Y.W.,... Knoll, F. (2021). Training a neural network for Gibbs and noise removal in diffusion MRI. Magnetic Resonance in Medicine, 85(1), 413-428. https://doi.org/10.1002/mrm.28395
- Muckley, M.J., Riemenschneider, B., Radmanesh, A., Kim, S., Jeong, G., Ko, J.,... Knoll, F. (2021). Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2021.3075856
2020
- Johnson, P.M., Recht, M.P., & Knoll, F. (2020). Improving the Speed of MRI with Artificial Intelligence. Seminars in Musculoskeletal Radiology, 24(1), 12-20. https://dx.doi.org/10.1055/s-0039-3400265
- Knoll, F., Hammernik, K., Zhang, C., Moeller, S., Pock, T., Sodickson, D.K., & Akcakaya, M. (2020). Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues. IEEE Signal Processing Magazine, 37(1), 128-140. https://doi.org/10.1109/MSP.2019.2950640
- Knoll, F., Murrell, T., Sriram, A., Yakubova, N., Zbontar, J., Rabbat, M.,... Recht, M.P. (2020). Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Magnetic Resonance in Medicine, 84(6), 3054-3070. https://doi.org/10.1002/mrm.28338
- Knoll, F., Zbontar, J., Sriram, A., Muckley, M.J., Bruno, M., Defazio, A.,... Lui, Y.W. (2020). FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning. Radiology: Artificial Intelligence, 2(1). https://doi.org/10.1148/ryai.2020190007
- Razavian, N., Knoll, F., & Geras, K.J. (2020). Artificial Intelligence Explained for Nonexperts. Seminars in Musculoskeletal Radiology, 24(1), 3-11. https://dx.doi.org/10.1055/s-0039-3401041
- Recht, M.P., Zbontar, J., Sodickson, D.K., Knoll, F., Yakubova, N., Sriram, A.,... Zitnick, C.L. (2020). Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study. American Journal of Roentgenology, 215(6), 1421-1429. https://doi.org/10.2214/AJR.20.23313
- Sriram, A., Zbontar, J., Murrell, T., Defazio, A., Zitnick, C.L., Yakubova, N.,... Johnson, P. (2020). End-to-End Variational Networks for Accelerated MRI Reconstruction. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 64-73). Lima, PER: Springer Science and Business Media Deutschland GmbH.
- Zibetti, M.V.W., Johnson, P.M., Sharafi, A., Hammernik, K., Knoll, F., & Regatte, R.R. (2020). Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76126-x