Instructor in Radiology, Harvard Medical School
Research Staff, Massachusetts General Hospital
Affiliated Staff, Boston Children’s Hospital
Athinoula A Martinos Center for Biomedical Imaging
149 13th St, Suite 2301
Charlestown, MA 02129
United States
mhoffmann at mgh dot harvard dot edu
Subject motion is a major confound in magnetic resonance imaging (MRI), as it is almost impossible to avoid artifacts if subjects do not remain still for the length of a scan; my research focuses on the development of techniques for detecting and correcting motion in the scanner. In particular, I am interested in methods using image registration, which capture the spatial relationship between objects directly from images.
Recently, my work builds on innovations in deep learning, which have enabled algorithms with unprecedented speed and reliability. My contributions include a strategy for learning registration from noise distributions, eliminating the need for acquired training data while providing deep neural networks with the previously missing ability to generalize to unseen data types.
My current goal is to automate and enhance the motion resilience of fetal MRI, which is challenging due to excessive levels of motion. Exploiting advances in deep learning and integrating these into the acquisition pipeline will unlock the full potential of the modality.
SynthStrip: Skull-Stripping for Any Brain Image
Hoopes A, Mora JS, Dalca AV, Fischl B*, Hoffmann M* (*equal contribution)
arXiv:2203.09974, 2022
Lipkin B, Tuckute G, Affourtit J, Small H, Mineroff Z, Kean H, Jouravlev O, Rakocevic L, Pritchett B, Siegelman M, Hoeflin C, Pongos A, Idan A. Blank, Melissa Kline Struhl, Ivanova A, Shannon S, Sathe A, Hoffmann M, Nieto-Castañón A, Fedorenko E
bioRxiv 2022.03.06.483177, 2022
SynthMorph: learning contrast-invariant registration without acquired images
Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV
IEEE Transactions on Medical Imaging (TMI), 41 (3), pp 543-558, 2022
Learning the Effect of Registration Hyperparameters with HyperMorph
Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV
Journal of Machine Learning for Biomedical Imaging (MELBA), IPMI 2021 Special Issue, 2022
An investigation across 45 languages and 12 language families reveals a universal language network
Ayyash D, Malik-Moraleda S, Gallee J, Affourtit J, Hoffmann M, Mineroff Z, Jouravlev O, Fedorenko E
Nature Neuroscience, in press, 2022
A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images
Greve DN, Billot B, Cordero D, Hoopes A, Hoffmann M, Dalca AV, Fischl B, Iglesias JE, Augustinack JC
NeuroImage, 244, p 118610, 2021
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
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
International Journal of Imaging Systems and Technology (IMA), 31 (3), pp 1136-1154, 2021
Learning MRI contrast-agnostic registration
Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV
IEEE International Symposium on Biomedical Imaging (ISBI), pp 899-903, 2021
HyperMorph: Amortized Hyperparameter Learning for Image Registration
Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV
Information Processing in Medical Imaging (IPMI), pp 3-17, 2021
Learning MRI contrast agnostic registration
Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV
Medical Imaging Meets NeurIPS (MED-NeurIPS), 2020
Additional sampling directions improve detection range of wireless radiofrequency probes
Hoffmann M, Mada M, Carpenter TA, Sawiak SJ, Williams GB
Magnetic Resonance in Medicine (MRM), 76 (3), pp 913-918, 2016
Spatial variation of the cooling lines in the reflection nebula NGC 7023
Bernard-Salas J, Habart E, Köhler M, Abergel A, Arab H, Lebouteiller V, Pinto C, van der Wiel MHD, White GJ, Hoffmann M
Astronomy & Astrophysics (A&A), 574, pp A97, 2015
Hoffmann M, Carpenter TA, Williams GB, Sawiak SJ
Magnetic Resonance Imaging (MRI), 33 (3), pp 346-350, 2014
SynthStrip: skull stripping for any brain image
Hoopes A, Mora JS, Dalca AV, Fischl B*, Hoffmann M* (*equal contribution)
Annual Meeting of the ISMRM, 2022
Singh NM, Hoffmann M, Moyer DC, Jang I, Chen L, Bezerra Cavalcanti Rockenbach MA, Guidon A, Aganj I, Kalpathy-Cramer J, Adalsteinsson E, Fischl B, Dalca AV, Golland P, Frost SR
Annual Meeting of the ISMRM, 2022
Automated MRI k-space Motion Artifact Detection in Segmented Multi-Slice Sequences
Jang I, Frost SR, Hoffmann M, Singh NM, Chen L, Guidon A, Bezerra Cavalcanti Rockenbach MA, Comeau D, Bizzo B, Chang K, Witham S, Rettmann D, Banerjee S, Brau A, Reese T, Aganj I, Dalca AV, Fischl B, Kalpathy-Cramer J
Annual Meeting of the ISMRM, 2022
Learning-based automatic field-of-view positioning for fetal-brain MRI
Hoffmann M, Moyer DC, Zhang L, Golland P, Grant PE, Gagoski B, van der Kouwe AJW
Annual Meeting of the ISMRM, 2021
Learning-based non-linear registration robust to MRI-sequence contrast
Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV
Annual Meeting of the ISMRM, 2021
Hoffmann M, Salat DH, Reuter M, Fischl B
Annual Meeting of the ISMRM, 2020
Hoffmann M, Frost SR, Salat DH, Tisdall MD, Polimeni J, van der Kouwe AJW
Annual Meeting of the ISMRM, 2020
Frost SR, Tisdall MD, Hoffmann M, Fischl B, Salat DH, van der Kouwe AJW
Annual Meeting of the ISMRM, 2020
Improved, rapid fetal-brain localization and orientation detection for auto-slice prescription
Hoffmann M, Gagoski B, Abaci Turk E, Wighton P, Tisdall MD, Reuter M, Adalsteinsson E, Grant PE, Wald LL, van der Kouwe AJW
Annual Meeting of the ISMRM, Montreal, QC, Canada, 2019
Fast, automated slice prescription of standard anatomical planes for fetal brain MRI
Hoffmann M, Gagoski B, Abaci Turk E, Wighton P, Tisdall MD, Reuter M, Adalsteinsson E, Grant PE, Wald LL, van der Kouwe AJW
Annual Meeting of the ISMRM, Paris, France, 2018
Hoffmann M, Sawiak SJ
Annual Meeting of the ISMRM, Honolulu, HI, United States, 2017
Hoffmann M, Sawiak SJ
Annual Meeting of the ISMRM, Singapore, 2016
Should volumetric, slice-wise or non-linear registration be used for motion correction of fMRI data?
Hoffmann M, Sawiak SJ
Annual Meeting of the ISMRM, Singapore, 2016
Additional sampling directions improve detection range of wireless RF probes for use in non-compliant patients
Hoffmann M, Mada M, Carpenter TA, Sawiak SJ, Williams GB
ISMRM British Chapter Annual Postgraduate Meeting, Stevenage, United Kingdom, 2015
Optimisation of retrospective motion correction in fMRI of uncooperative patients
Hoffmann M, Carpenter TA, Williams GB, Sawiak SJ
ISMRM British Chapter Annual Scientific Meeting, Edinburgh, United Kingdom, 2014
Doctor of Philosophy (PhD), MR Physics
University of Cambridge, United Kingdom
Master of Advanced Studies (MASt), Experimental and Theoretical Physics
University of Cambridge, United Kingdom
Licence et Magistère 1, Physique Fondamentale
University of Paris XI, France