Prof. Dr. Florian Knoll

2022

• 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://dx.doi.org/10.1109/TCI.2022.3176129

2021

• 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://dx.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://dx.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://dx.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://dx.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://dx.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://dx.doi.org/10.1002/mrm.28338
• 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://dx.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://dx.doi.org/10.1038/s41598-020-76126-x