Malte’s research focuses on the development of computer-vision algorithms for medical image processing and analysis using artificial intelligence. He is particularly interested in registration, which captures the spatial relationship between objects from images. Malte’s work also includes exploiting this information to advance medical imaging applications with clinical impact, such as fetal-MRI acquisition protocols.

Education

PhD in MRI Physics, University of Cambridge

Select Publications

1. Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: learning contrast-invariant registration without acquired images. IEEE Transactions on Medical Imaging (TMI). 2022;41(3):543-58.

2. Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV. Learning the Effect of Registration Hyperparameters with HyperMorph. Journal of Machine Learning for Biomedical Imaging (MELBA). 2022; IPMI 2021 Special Issue.

3. Hoffmann M, Abaci Turk E, Gagoski B, Morgan L, Wighton P, Tisdall MD, Reuter M, Adalsteinsson E, Grant PE, Wald LL, van der Kouwe AJW. Rapid head-pose detection for automated slice prescription of fetal-brain MRI. International Journal of Imaging Systems and Technology (IMA). 2021;31(3):1136-54.2.

Highlights

2020: NIH Pathway to Independence Award K99/R00

2020, 2021: ISMRM Summa Cum Laude Merit Award for top 5% abstract

2019: ISMRM Detection & Correction of Motion in MRI & MRS Study Group Awardp