• About
• Preprints
• Publications
• Abstracts
• Projects
• Education
• Awards

Malte Hoffmann⬏

I am a faculty member in Radiology at Harvard Medical School (HMS) and Massachusetts General Hospital (MGH). I received a Bachelor's degree in physics from the University of Paris XI and a Master's degree and PhD from the University of Cambridge, where I specialized in correcting subject motion in brain magnetic resonance imaging (MRI). I completed my postdoctoral training at MGH and HMS with Bruce Fischl, André van der Kouwe, and Adrian Dalca.

My research develops artificial intelligence (AI) for medical image analysis, spanning deep learning, computer vision, and MRI acquisition. I focus on segmentation and registration: delineating anatomical structures and spatially aligning images. My goal is to build techniques that enable clinically impactful applications. I also occasionally serve as a consultant and scientific advisor to companies creating health and imaging AI technologies.

Preprints⬏

• Deep infant brain segmentation from multi-contrast MRI

  Hoffmann M, Zöllei L*, Dalca AV,* (*equal contribution)

  arXiv:2512.05114, 2025

Peer-reviewed articles⬏

• Domain-randomized deep learning for neuroimage analysis (arXiv, PMC)

  Hoffmann M

  IEEE Signal Processing Magazine (SPM), 42 (4), pp 78-90, 2025

• SingleStrip: learning skull-stripping from a single labeled example (arXiv)

  Specktor-Fadida B, Hoffmann M

  MICCAI Workshop on Data Engineering in Medical Imaging (DEMI), 16191, pp 42-52, 2025

• Learning accurate rigid registration for longitudinal brain MRI from synthetic data (arXiv, PMC)

  Fu J, Dalca AV, Fischl B, Moreno R, Hoffmann M

  IEEE International Symposium on Biomedical Imaging (ISBI), pp 1-5, 2025

• MultiMorph: on-demand atlas construction (arXiv)

  Abulnaga SM, Hoopes A, Dey N, Hoffmann M, Rakic M, Fischl B, Guttag J, Dalca AV

  IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), pp 30906-30917, 2025

• Hierarchical uncertainty estimation for learning-based registration in neuroimaging (arXiv)

  Hu X, Gopinath K, Liu P, Hoffmann M, van Leemput K, Puonti O, Iglesias JE

  International Conference on Learning Representations (ICLR), pp 11771-11787, 2025

• Deep-ER: deep learning ECCENTRIC reconstruction for fast high-resolution neurometabolic imaging (arXiv, PMC)

  Weiser PJ, Langs G, Bogner W, Motyka S, Strasser B, Golland P, Singh NM, Dietrich J, Uhlmann EJ, Batchelor TT, Cahill D, Hoffmann M, Klauser A, Andronesi OC

  NeuroImage, 309, p 121045, 2025

• Anatomy-aware and acquisition-agnostic joint registration with SynthMorph (arXiv, PMC)

  Hoffmann M, Hoopes A, Greve DN, Fischl B*, Dalca AV* (*equal contribution)

  Imaging Neuroscience, 2, pp 1-33, 2024

• Boosting skull-stripping performance for pediatric brain images (arXiv, PMC)

  Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L*, Hoffmann M* (*equal contribution)

  IEEE International Symposium on Biomedical Imaging (ISBI), pp 1-5, 2024

• Synthetic data in generalizable, learning-based neuroimaging (PMC)

  Gopinath K*, Hoopes A*, Alexander DC, Arnold SE, Balbastre Y, Casamitjana A, Cheng Y, Chua RYZ, Edlow BL, Fischl B, Gazula H, Hoffmann M, Keene CD, Kim S, Kimberly WT, Laguna S, Larson KE, van Leemput K, Puonti O, Rodrigues LM, Rosen MS, Tregidgo HFJ, Varadarajan D, Young SI, Dalca AV*, Iglesias JE* (*equal contribution)

  Imaging Neuroscience, 2, pp 1-22, 2024

• Registration by regression (RbR): a framework for interpretable and flexible atlas registration (arXiv, PMC)

  Gopinath K*, Hu X*, Hoffmann M, Puonti O*, Iglesias JE* (*equal contribution)

  Workshop on Biomedical Image Registration (WBIR), 15249, pp 205-215, 2024

• SE(3)-equivariant and noise-invariant 3D rigid motion tracking in brain MRI (arXiv, PMC)

  Billot B, Dey N, Moyer D, Hoffmann M, Abaci Turk E, Gagoski B, Grant PE, Golland P

  IEEE Transactions on Medical Imaging (TMI), 43 (11), pp 4029-4040, 2024

• WALINET: a water and lipid identification convolutional neural network for nuisance signal removal in 1H MR spectroscopic imaging (arXiv, PMC)

  Weiser PJ, Langs G, Motyka S, Bogner W, Courvoisier S, Hoffmann M, Klauser A*, Andronesi OC* (*equal contribution)

  Magnetic Resonance in Medicine (MRM), 93 (4), pp 1430-1442, 2024

• Data consistent deep rigid MRI motion correction (arXiv, PMC)

  Singh NM, Dey N, Hoffmann M, Fischl B, Adalsteinsson E, Frost R*, Dalca AV*, Golland P* (*equal contribution)

  Medical Imaging with Deep Learning (MIDL), 177, 2023

• Equivariant and denoising CNNs to decouple intensity and spatial features for motion tracking in fetal brain MRI

  Billot B, Moyer D, Karani N, Hoffmann M, Abaci Turk E, Grant PE, Golland P

  Medical Imaging with Deep Learning (MIDL), 39, 2023

• Anatomy-specific acquisition-agnostic affine registration learned from fictitious images (PDF)

  Hoffmann M, Hoopes A, Fischl B*, Dalca AV* (*equal contribution)

  SPIE Medical Imaging: Image Processing, 12464, p 1246402, 2023

• SynthStrip: skull-stripping for any brain image (arXiv, PMC)

  Hoopes A, Mora JS, Dalca AV, Fischl B*, Hoffmann M* (*equal contribution)

  NeuroImage, 260, p 119474, 2022

• SynthMorph: learning contrast-invariant registration without acquired images (arXiv, PMC)

  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 (arXiv, PMC)

  Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV

  Machine Learning for Biomedical Imaging (MELBA), IPMI 2021 Special Issue, 2022

• Probabilistic atlas for the language network based on precision fMRI data from >800 individuals (bioRxiv, PMC)

  Lipkin B, Tuckute G, Affourtit J, Small H, Mineroff Z, Kean H, Jouravlev O, Rakocevic L, Pritchett B, Siegelman M, Hoeflin C, Pongos A, Blank IA, Kline Struhl M, Ivanova A, Shannon S, Sathe A, Hoffmann M, Nieto-Castañón A, Fedorenko E

  Scientific Data, 9, p 529, 2022

• An investigation across 45 languages and 12 language families reveals a universal language network (bioRxiv, PMC)

  Malik-Moraleda S, Ayyash D, Gallée J, Affourtit J, Hoffmann M, Mineroff Z, Jouravlev O, Fedorenko E

  Nature Neuroscience, 25, pp 1014-1019, 2022

• A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images (PMC)

  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 (arXiv, PMC)

  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 (PDF, PMC)

  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 (arXiv)

  Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV

  Information Processing in Medical Imaging (IPMI), pp 3-17, 2021

• Additional sampling directions improve detection range of wireless radiofrequency probes (PMC)

  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 (arXiv)

  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

• A survey of patient motion in disorders of consciousness and optimization of its retrospective correction (Apollo)

  Hoffmann M, Carpenter TA, Williams GB, Sawiak SJ

  Magnetic Resonance Imaging (MRI), 33 (3), pp 346-350, 2014

Peer-reviewed abstracts⬏

• A method for automatic 3D vasculature segmentation in ex vivo MRI using synthetic data

  Mauri C, Chollet E, Willis A, Jama A, Mahmood A, Ream A, Garcia I, Benlahcen M, Wood S, Lin S, Onta P, Tran N, Zeng X, Magnain C, Herisse R, Garcia Pallares EX, Hoffmann M*, Fischl B*, Balbastre Y* (*equal contribution)

  Annual Meeting of the ISMRM, Honolulu, HI, USA, 2025

• Deep-ERx2: deep learning reconstruction for fast high-resolution non-Cartesian compressed-sensing MR spectroscopic imaging at 3T and 7T

  Weiser PJ, Langs G, Motyka S, Strasser B, Bogner W, Golland P, Singh NM, Dietrich J, Uhlmann EJ, Batchelor TT, Cahill D, Ungan G, Hoffmann M, Klauser A, Andronesi OC

  Annual Meeting of the ISMRM, Honolulu, HI, USA, 2025

• WALINET+: a water and lipid identification neural network for nuisance removal of water unsuppressed magnetic resonance spectroscopic imaging

  Weiser PJ, Langs G, Motyka S, Bogner W, Courvoisier S, Ungan G, Hoffmann M, Klauser A, Andronesi OC

  Annual Meeting of the ISMRM, Honolulu, HI, USA, 2025

• Deep-ECCENTRIC: deep learning reconstruction of whole-brain non-Cartesian compressed-sensing MR spectroscopic imaging

  Weiser PJ, Langs G, Motyka S, Strasser B, Bogner W, Golland P, Singh NM, Dietrich J, Uhlmann EJ, Batchelor TT, Cahill D, Hoffmann M*, Klauser A*, Andronesi OC* (*equal contribution)

  Annual Meeting of the ISMRM, Singapore, 2024

• MR software tools for real-time decision making and FOV prescription

  Wighton P, Hinds O, Frost R, Hoffmann M, Gagoski B, Varadarajan D, Proulx S, Reuter M, Polimeni JR, Fischl B, Ghosh S, van der Kouwe AJW

  Annual Meeting of the ISMRM, Singapore, 2024

• Real-time motion correction and multicoil shim array B0 update for whole-brain MR spectroscopic imaging

  Andronesi OC, Frost R, Arango NS, Jayadev NB, Chang Y, Wighton P, Hoffmann M, Stockmann J, van der Kouwe AJW

  Annual Meeting of the ISMRM, Singapore, 2024

• LIPCON: lipid identification with convolutional neural network for MR spectroscopic imaging

  Weiser PJ, Langs G, Motyka S, Strasser B, Bogner W, Courvoisier S, Hoffmann M, Andronesi OC*, Klauser A* (*equal contribution)

  Annual Meeting of the ISMRM, Singapore, 2024

• Deep-ERv2: metabolically trained deep learning reconstruction of whole-brain non-Cartesian compressed-sensing MR spectroscopic imaging

  Weiser PJ, Langs G, Motyka S, Strasser B, Golland P, Singh NM, Ungan G, Hoffmann M, Klauser A, Andronesi OC

  ISMRM Workshop on MR Spectroscopy, Boston, MA, USA, 2024

• Can we predict motion artifacts in clinical MRI before the scan completes? (PMC)

  Hoffmann M, Singh NM, Dalca AV, Fischl B, Frost R

  Annual Meeting of the ISMRM, Toronto, ON, Canada, 2023

• Motion-aware neural networks improve rigid motion correction of accelerated segmented multislice MRI

  Singh NM, Hoffmann M, Adalsteinsson E, Fischl B, Golland P*, Dalca AV*, Frost R* (*equal contribution)

  Annual Meeting of the ISMRM, Toronto, ON, Canada, 2023

• Clinical evaluation of k-space correlation informed motion artifact detection in segmented multi-slice MRI (PMC)

  Jang I, Hoffmann M, Singh NM, Balbastre Y, Chen L, Bezerra Cavalcanti Rockenbach MA, Dalca AV, Aganj I, Kalpathy-Cramer J, Fischl B, Frost R

  Annual Meeting of the ISMRM, Toronto, ON, Canada, 2023

• Data consistency-based rigid motion correction in accelerated multi-shot 2D-encoded brain MRI

  Singh NM, Hoffmann M, Adalsteinsson E, Fischl B, Golland P, Dalca AV, Frost R

  ISMRM Workshop on Motion Detection & Correction, Oxford, UK, 2022

• SynthStrip: skull stripping for any brain image

  Hoopes A, Mora JS, Dalca AV, Fischl B*, Hoffmann M* (*equal contribution)

  Annual Meeting of the ISMRM, London, UK, 2022

• Joint neural network for fast retrospective rigid motion correction of accelerated segmented multislice MRI

  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 R* (*equal contribution)

  Annual Meeting of the ISMRM, London, UK, 2022

• Automated MRI k-space motion artifact detection in segmented multi-slice sequences

  Jang I*, Frost R*, 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* (*equal contribution)

  Annual Meeting of the ISMRM, London, UK, 2022

• Learning-based automatic field-of-view positioning for fetal-brain MRI (PMC)

  Hoffmann M, Moyer DC, Zhang L, Golland P, Grant PE, Gagoski B, van der Kouwe AJW

  Annual Meeting of the ISMRM, online, 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, online, 2021

• Learning MRI contrast agnostic registration

  Hoffmann M, Billot B, Iglesias JE, Fischl B, Dalca AV

  Medical Imaging Meets NeurIPS (MED-NeurIPS), online, 2020

• Longitudinal FreeSurfer with non-linear subject-specific template improves sensitivity to cortical thinning

  Hoffmann M, Salat DH, Reuter M*, Fischl B* (*equal contribution)

  Annual Meeting of the ISMRM, online, 2020

• Real-time brain masking algorithm improves motion tracking accuracy in scans with volumetric navigators (vNavs) (PMC)

  Hoffmann M, Frost R, Salat DH, Tisdall MD, Polimeni JR, van der Kouwe AJW

  Annual Meeting of the ISMRM, online, 2020

• Scan-specific assessment of vNav motion artifact mitigation in the HCP Aging study using reverse motion correction

  Frost R, Tisdall MD, Hoffmann M, Fischl B, Salat DH, van der Kouwe AJW

  Annual Meeting of the ISMRM, online, 2020

• Improved, rapid fetal-brain localization and orientation detection for auto-slice prescription

  Hoffmann M, Abaci Turk E, Gagoski B, 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

• Evaluation of prospective and retrospective motion correction exploiting the slice-based acquisition of fMRI

  Hoffmann M, Sawiak SJ

  Annual Meeting of the ISMRM, Honolulu, HI, USA, 2017

• Automated rejection of motion-corrupted slices and optimised retrospective ghost correction for multi-shot DTI

  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, UK, 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, UK, 2014

• A survey of patient motion in disorders of consciousness and optimisation of its retrospective correction

  Hoffmann M, Carpenter TA, Williams GB, Sawiak SJ

  ISMRM Workshop on Motion Correction in MRI, Tromsø, Norway, 2014

Projects⬏

• BabySeg

  BabySeg is a brain segmentation framework for infants and young children. Trained with a data engine that generates artificial images from label maps and acquired MRI, it delineates anatomical structures without preprocessing. BabySeg can pool information from a flexible number of input volumes of varying size, resolution, and contrast passed in any order.

• SynthStrip

  SynthStrip is a robust skull-stripping tool that removes non-brain image content from full-head scans across imaging modalities, contrasts, resolutions, and subject populations. The SynthStrip dataset is a freely available collection of full-head images with corresponding brain masks from 622 MRI, CT, and PET scans.

• SynthMorph

  SynthMorph, a strategy for learning contrast-invariant registration without acquired images, enables unprecedented network generalization across a landscape of real image types. By exposing deep neural networks to wildly varying synthetic training data, SynthMorph alleviates the need for retraining models.

• Fetal-Align

  Fetal-Align is a fetal-head pose detection algorithm for full-uterus stacks of echo planar imaging (EPI) slices. The project aims at improving fetal MRI by automating the clinical workflow and includes a small testset of 41 EPI stacks from fetuses at 26-37 weeks' gestation.

Education⬏

• 2017-2022 Postdoctoral Fellow, Radiology

  Harvard Medical School, USA

  Massachusetts General Hospital, USA

• 2013-2017 Doctor of Philosophy (PhD), MRI Physics

  University of Cambridge, UK

• 2011-2012 Master of Advanced Studies (MASt), Experimental and Theoretical Physics

  University of Cambridge, UK

• 2008-2011 Selective Bachelor's degree (Licence et Magistère 1), Fundamental Physics

  University of Paris XI, France

Awards⬏

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

• 2020 NIH K99/R00 Pathway to Independence Award

• 2019 ISMRM Detection & Correction of Motion in MRI & MRS Study Group Award

• 2016-2019 ISMRM Educational Stipend

• 2015, 2016 Cusanuswerk Graduate Travel Award

• 2014, 2015 Downing College Graduate Travel Fund Award

• 2014 University of Cambridge Medical School EG Fearnside Travel Fund Award

• 2014 ISMRM Workshop on Motion Correction in MRI Trainee Stipend

• 2013-2016 Cusanuswerk Graduate Scholarship