Research

Journal Papers:

1. Kimberly Amador, Matthias Wilms, Anthony Winder, Jens Fiehler, Nils D. Forkert:
   Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks
   Medical Image Analysis, 82, 102610, 2022.
    
2. Jordan J. Bannister, Hailey Juszczak, J. David Aponte, David C. Katz, P. Daniel Knott, Seth M. Weinberg, Benedikt Hallgrímsson, Nils D. Forkert, Rahul Seth:
   Sex differences in adult facial three-dimensional morphology: Application to gender-affirming facial surgery 
   Journal of Facial Plastic Surgery & Aesthetic Medicine, 24(S2), S24-S30, 2022.
    
3. Jordan J. Bannister, Matthias Wilms, David Aponte, David C. Katz, Ophir D. Klein, Francois P.J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
   A deep invertible 3D facial shape model for interpretable computer-assisted genetic syndrome diagnosis 
   IEEE Journal of Biomedical and Health Informatics, 26(7), 3229-3239, 2022.
    
4. Jordan J. Bannister, Matthias Wilms, David Aponte, David C. Katz, Ophir D. Klein, Francois P.J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
   Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models
   Artificial Intelligence in Medicine, 134, 102425, 2022.
    
5. Jay Devine, Marta Vidal-García, Wei Liu, Amanda Neves, Lucas D. Lo Vercio, Rebecca M. Green, Heather A. Richbourg, Marta Marchini, Colton M. Unger, Audrey C. Nickle, Bethany Radford, Nathan M. Young, Paula N. Gonzalez, Robert E. Schuler, Alejandro Bugacov, Campbell Rolian, Christopher J. Percival, Trevor Williams, Lee Niswander, Anne L. Calof, Arthur D. Lander, Axel Visel, Frank R. Jirik, James M. Cheverud, Ophir D. Klein, Ramon Y. Birnbaum, Amy E. Merrill, Rebecca R. Ackermann, Daniel Graf, Myriam Hemberger, Wendy Dean, Nils D. Forkert, Stephen A. Murray, Henrik Westerberg, Ralph S. Marcucio, Benedikt Hallgrímsson:
   MusMorph: A database of standardized mouse morphology data for morphometric meta-analyses
   Scientific Data, 9, 230, 2022.
    
6. Lucas D. Lo Vercio, Rebecca M. Green, Samuel Robertson, Sienna Guo, Andreas Dauter, Marta Marchini, Marta Vidal-García, Xiang Zhao, Anandita Mahika, Ralph S. Marcucio, Benedikt Hallgrímsson, Nils D. Forkert:
   Segmentation of tissues and proliferative cells in light-sheet microscopy images of mouse embryos using convolutional neural networks
   IEEE Access, 10, 105084-105100, 2022.
    
7. Peter Ludewig, Matthias Gräser, Nils D. Forkert, Florian Thieben, Javier Rández-Garbayo, Johanna Rieckhoff, Katrin Lessmann, Fynn Förger, Patryk Szwargulski, Tim Magnus, Tobias Knopp:
   Magnetic particle imaging for assessment of cerebral perfusion and ischemia
   WIREs Nanomedicine and Nanobiotechnology, 14, e1757, 2022.
    
8. Sarah J. MacEachern, Nils D. Forkert, Jean-Francois Lemay, Deborah Dewey:
   Physical activity participation and barriers for children and adolescents with disabilities
   International Journal of Disability, Development and Education, 69(1), 204-216, 2022.
    
9. Boris Modrau, Anthony Winder, Niels Hjort, Martin N. Johansen, Grethe Andersen, Jens Fiehler, Henrik Vorum, Nils D. Forkert:
   Perfusion changes in acute stroke treated with theophylline as an add-on to thrombolysis: A randomized clinical trial subgroup analysis
   Clinical Neuroradiology, 32(2), 345-352, 2022
   
    
10. Pauline Mouches, Matthias Wilms, Agampreet Aulakh, Sönke Langner, Nils D. Forkert:
    Multi-modal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors
    Frontiers in Neurology, 13, 979774, 2022.
     
11. Pauline Mouches, Matthias Wilms, Deepthi Rajashekar,Sönke Langner, Nils D. Forkert:
    Multi-modal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions
    Human Brain Mapping, 43(8), 2554-2566, 2022.
     
12. Pauline Mouches, Matthias Wilms, Jordan Bannister, Agampreet Aulakh, Sönke Langner, Nils D. Forkert:
    An exploratory causal analysis of the relationships between the brain age gap and cardiovascular risk factors
    Frontiers in Aging Neuroscience, 14, 941864, 2022.
     
13. Bernardo C. Pimentel, Thies Ingwersen, Karl Georg Haeusler, Eckhard Schlemm, Nils D. Forkert, Deepthi Rajashekar, Pauline Mouches, Alina Königsberg, Paulus Kirchhof, Claudia Kunze, Serdar Tütüncü, Manuel C. Olma, Michael Krämer, Dominik Michalski, Andrea Kraft, Timolaos Rizos, Torsten Helberg, Sven Ehrlich, Darius G. Nabavi, Joachim Röther, Ulrich Laufs, Roland Veltkamp, Peter U. Heuschmann, Bastian Cheng, Matthias Endres, Götz Thomalla:
    Association of stroke lesion shape with newly detected atrial fibrillation – Results from the MonDAFIS study
    European Stroke Journal, 7(3), 230-237, 2022.
     
14. Jennifer L. Quon, Pauline Mouches, Lily H. Kim, Rashad Jabarkheel, Yi Zhang, Gary Steinberg, Gerald Grant, Michael S.B. Edwards, Kristen Yeom, Nils D. Forkert:
    Age-dependent intracranial artery morphology in healthy children
    Clinical Neuroradiology, 32(1), 49-56, 2022.
     
15. Deepthi Rajashekar, Matthias Wilms, M. Ethan MacDonald, Serena Schimert, Michael D. Hill, Andrew M. Demchuk, Mayank Goyal, Sean P. Dukelow, Nils D. Forkert:
    Lesion-symptom mapping with NIHSS sub-scores in ischemic stroke patients
    Stroke & Vascular Neurology, 7(2), 124-131, 2022.
     
16. Jonathan D. Santoro, Peter Moon, Michelle Han, Emily McKenna, Elizabeth Tong, Sarah J. MacEachern, Nils D. Forkert, Kristen W. Yeom:
    Early onset diffusion abnormalities in refractory headache disorders
    Frontiers in Neurology, 13, 898219, 2022.
     
17. Emma A.M. Stanley, Matthias Wilms, Pauline Mouches, Nils D. Forkert:
    Fairness-related performance and explainability effects in deep learning models for brain image analysis
    Journal of Medical Imaging, 9(6), 061102, 2022.
     
18. Hristina Uzunova, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
    A systematic comparison of generative models for medical images
    International Journal of Computer Assisted Radiology and Surgery, 17(7), 1213-1224, 2022.
     
19. Daniella Vellone, Maryam Ghahremani, Zahra Goodarzi, Nils D. Forkert, Eric E. Smith, Zahinoor Ismail:
    Apathy and APOE in mild behavioral impairment, and risk for incident dementia
    Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 8, e12370, 2022.
     
20. Meng Wang, Matthew Greenberg, Nils D. Forkert, Thierry Chekouo, Gabriel Afriyie, Zahinoor Ismail, Eric E. Smith, Tolulope Sajobi:
    Dementia risk prediction in individuals with mild cognitive impairment: A comparison of Cox regression and machine learning models
    BMC Medical Research Methodology, 22, 284 (2022).
     
21. Meng Wang, Tolulope Sajobi, Zahinoor Ismail, Dallas Seitz, Thierry Chekouo, Nils D. Forkert, Karyn Fischer, Aaron Mackie, Dawn Pearson, David Patry, Alicja Cieslak, Bijoy Menon, Phil Barber, Brienne McLane, Robert Granger, David B. Hogan, Eric E. Smith:
    A pragmatic dementia risk score for patients with mild cognitive impairment in a memory clinic population: development and validation of a dementia risk score using routinely collected data
    Alzheimer's & Dementia: Translational Research & Clinical Interventions, 8(1), e12301, 2022.
     
22. Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
    Invertible modeling of bidirectional relationships in neuroimaging with normalizing flows: Application to brain aging
    IEEE Transactions on Medical Imaging, 41(9), 2331-2347, 2022.
     
23. Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
    Localized statistical shape models for large-scale problems with few training data
    IEEE Transactions on Biomedical Engineering, 69(9), 2947-2957, 2022
     
24. Anthony J. Winder, Matthias Wilms, Kimberly Amador, Fabian Flottmann, Jens Fiehler, Nils D. Forkert:
    Predicting the tissue outcome of acute ischemic stroke from acute 4D CT perfusion imaging using temporal features and deep learning
    Frontiers in Neuroscience, 16, 1009654, 2022.
     
25. Vivek S. Yedavalli, Jennifer L. Quon, Elizabeth Tong, Eric K. van Staalduinen, Pauline Mouches, Lily H. Kim, Gary K. Steinberg, Gerald A. Grant, Kristen W. Yeom, Nils D. Forkert:
    Intracranial artery morphology in pediatric Moya Moya disease and Moya Moya syndrome
    Neurosurgery, 91(5), 710-716, 2022.

Full-Length Proceedings Papers:

1. Kimberly Amador, Matthias Wilms, Anthony Winder, Jens Fiehler, Nils D. Forkert:
   Hybrid spatio-temporal transformer network for predicting ischemic stroke lesion outcomes from 4D CT perfusion imaging
   In: Wang, L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science, Vol 13433, 644–654, 2022.
    
2. Gabrielle Dagasso, Matthias Wilms, Nils D. Forkert:
   A morphometrics approach for inclusion of localized characteristics from medical imaging studies into genome-wide association studies
   IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 3622-3628, 2022.
    
3. Alejandro Gutierrez, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
   Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients
   In: Drukker K.et al. (eds) Computer-Aided Diagnosis, SPIE Medical Imaging 2022, Vol. 12033, 1203317, San Diego, USA, 2022.
    
4. Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze, Stephanie Wichuk, Nils D. Forkert, Jacob L. Jaremko, Janet L. Ronsky:
   Weakly supervised medical image segmentation with soft labels and noise robust loss
   International Workshop on Pattern Recognition in Healthcare Analytics held in Conjunction with the 26^th International Conference on Pattern Recognition (ICPR), Montreal, Canada, 2022.
    
5. Jasmine A. Moore, Matthias Wilms, Kayson Fakhar, Fatemeh Hadaghi, Claus C. Hilgetag, Nils D. Forkert:
   Adding neuroplasticity to a CNN-based in-silico model of neurodegeneration
   4^th Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop held in Conjunction with the Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.
    
6. Christopher Nielsen, Nils D. Forkert:
   Exploring the relationship between model prediction uncertainty and gradient inversion attack vulnerability for federated learning-based diabetic retinopathy grade classification
   Medical Imaging Meets NeurIPS Workshop held in Conjunction with the Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022.
    
7. Christopher Nielsen, Anup Tuladhar, Nils D. Forkert:
   Investigating the vulnerability of federated learning-based diabetic retinopathy grade classification to gradient inversion attacks
   In: Antony, B., Fu, H., Lee, C.S., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2022. Lecture Notes in Computer Science, Vol 13576, 183–192, 2022.
    
8. Samuel Robertson, Anup Tuladhar, Deepthi Rajashekar, Nils D. Forkert:
   Stroke lesion localization in 3D MRI datasets with deep reinforcement learning
   In: Drukker K.et al. (eds) Computer-Aided Diagnosis, SPIE Medical Imaging 2022, Vol. 12033, 120330M, San Diego, USA, 2022.
    
9. Raissa Souza, Agampreet Aulakh, Pauline Mouches, Anup Tuladhar, Matthias Wilms, Sönke Langner, Nils D. Forkert:
   A comparative analysis of the impact of data distribution on distributed learning with a traveling model for brain age prediction
   In: Deserno T.M., Park B.J. (eds), Imaging Informatics for Healthcare, Research, and Applications, SPIE Medical Imaging 2022, Vol. 12037, 1203702, San Diego, USA, 2022.
    
10. Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
    Multi-institutional travelling model for tumor segmentation in MRI datasets
    In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. 420–432, 2022.
     
11. Emma A.M. Stanley, Deepthi Rajashekar, Pauline Mouches, Matthias Wilms, Kira Plettl, Nils D. Forkert:
    A fully convolutional neural network for explainable classification of attention deficit hyperactivity disorder
    In: Drukker K.et al. (eds) Computer-Aided Diagnosis, SPIE Medical Imaging 2022, Vol. 12033, 1203315, San Diego, USA, 2022.
     
12. Emma Stanley, Matthias Wilms, Nils D. Forkert:
    Disproportionate subgroup impacts and other challenges of fairness in artificial intelligence for medical image analysis
    In: John S.H. Baxter, et al. Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging. Lecture Notes in Computer Science, vol 13755, 2022.
     
13. Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert:
    Federated learning using variable local training for brain tumor segmentation
    In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. 392–404, 2022.

Abstracts:

1. Hannes Almgren, Milton Camacho, Alexandru Hanganu, Nils D. Forkert, Oury Monchi:
   Prediction of cognitive decline in early Parkinson’s disease using machine learning
   28^th Annual Meeting of the Organization for Human Brain Mapping, Glasgow, UK, 2022.
    
2. Andreas Dauter, Rebecca M. Green, Lucas D. Lo Vercio, Elizabeth C. Barretto, Samuel Robertson, Anandita Mahika, Marta Vidal Garcia, Nils D. Forkert, Benedikt Hallgrímsson:
   Exploring the distribution and orientation of cell proliferation as drivers of mouse facial development
   Experimental Biology (EB) 2022, Philadelphia, USA, 2022.
    
3. Pauline de Jesus, Brandon T. Craig, Nils D. Forkert, Ryan D'Arcy, Marvin J. Fritzler, Karen Barlow, Michael J. Esser:
   Multi-modal analysis of outcomes in pediatric mild traumatic brain injury (mTBI)
   Canadian Neurological Sciences Federation (CNSF) 2022 Congress, Montreal, Canada, 2022.
    
4. Guillermo Delgado-García, Jordan D.T. Engbers, Pauline Mouches, Kimberly Amador, Samuel Wiebe, Nils D. Forkert, James White, Tolulope Sajobi, Karl Martin Klein, Colin B. Josephson:
   Multi-modal machine learning can predict incident and persistent depression in adults with epilepsy: A pilot study
   American Epilepsy Society Annual Meeting, Nashville, USA, 2022.
    
5. Jasmine Moore, Anup Tuladhar, Nils D. Forkert:
   Simulating neurodegeneration with noise in convolutional neural networks
   From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, USA, 2022.
    
6. Thomas Renson, Nils D. Forkert, Kimberley Amador, Paivi Miettunen, Simon Parsons, Muhammed Dhalla, Nicole A Johnson, Nadia Luca, Heinrike Schmeling, Rebeka Stevenson, Marinka Twilt, Lorraine Hamiwka, Susanne Benseler:
   Reading the waves: Identifying distinct phenotypes of multisystem inflammatory syndrome in children during the 2020-2021 COVID-19 pandemic
   American College of Rheumatology (ACR) Convergence 2022, Philadelphia, USA, 2022.
    
7. Anup Tuladhar, Jasmine Moore, Nils D. Forkert:
   Changes in representational structure within a degenerating neural network
   From Neuroscience to Artificially Intelligent Systems (NAISys), Cold Spring Harbor Laboratory, USA, 2022.
    
8. Anup Tuladhar, Jasmine A. Moore, Zahinoor Ismail, Nils D. Forkert:
   Simulating progressive neurodegeneration in silico with deep artificial neural networks
   Cognitive Science Society Conference (CogSci), Toronto, Canada, 2022.
    
9. Meng Wang, Tolulope Sajobi, Aravind Ganesh, Dallas Seitz, Thierry Chekouo, Nils D. Forkert, Michael J. Borrie, Richard Camicioli, Ging-Yuek Robin Hsiung, Mario Masellis, Paige Moorhouse, David B. Hogan, Carmela Tartaglia, Zahinoor Ismail, Eric E. Smith:
   Identifying prognostic factors of dementia in individuals with mild cognitive impairment (MCI): Are statistical models adequate?
   Alzheimer's Association International Conference 2022, San Diego, USA.

Journal Papers:

1. Bryce A. Besler, Andrew S. Michalski, Michael T. Kuczynski, Aleena Abid, Nils D. Forkert, Steven K. Boyd:
   Bone and joint enhancement filtering: Application to proximal femur segmentation from uncalibrated computed tomography datasets
   Medical Image Analysis, 67, 101887, 2021.
    
2. Sascha Gill, Meng Wang, Pauline Mouches, Deepthi Rajashekar, Tolulope Sajobi, Frank P. MacMaster, Eric E. Smith, Nils D. Forkert, Zahinoor Ismail:
   Neural correlates of impulse dyscontrol domain of mild behavioral impairment
   International Journal of Geriatric Psychiatry, 36(9), 1398-1406, 2021.
    
3. Zahinoor Ismail, Alexander McGirr, Sascha Gill, Sophie Hu, Nils D. Forkert, Eric E. Smith:
   Mild behavioral impairment and subjective cognitive decline predict cognitive and functional decline
   Journal of Alzheimer’s Disease, 80(1), 459-469, 2021.
    
4. Adrienne Kline, Nils D. Forkert, Bradley Goodyear, Janet Ronsky:
   fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface
   Journal of Neuroscience Methods, 363, 109339, 2021.
    
5. Sarah J. MacEachern, Nils D. Forkert:
   Machine learning for precision medicine
   Genome, 64(4), 416-425, 2021.
    
6. Marta Marchini, Diane Hu, Lucas Lo Vercio, Nathan Young, Nils D. Forkert, Benedikt Hallgrimsson, Ralph Marcucio:
   Wnt3a drives correlated changes in facial morphology and brain shape
   Frontiers in Cell and Developmental Biology, 9, 644099, 2021.
    
7. Boris Modrau, Anthony Winder, Niels Hjort, Grethe Andersen, Jens Fiehler, Henrik Vorum, Nils D. Forkert:
   Prediction of brain tissue infarction in patients with acute ischemic stroke treated with theophylline as an add-on to thrombolytic therapy: A subgroup analysis of the TEA-stroke trial
   Frontiers in Neurology, 12, 613029, 2021.
    
8. Boris Modrau, Anthony Winder, Niels Hjort, Martin N. Johansen, Grethe Andersen, Jens Fiehler, Henrik Vorum, Nils D. Forkert:
   Perfusion changes in acute stroke treated with theophylline as an add-on to thrombolysis: A randomized clinical trial subgroup analysis
   Clinical Neuroradiology, 2021 [Epub ahead of print]
    
9. Pauline Mouches, Sönke Langner, Martin Domin, Michael D. Hill, Nils D. Forkert:
   Influence of cardiovascular risk factors on morphological changes of cerebral arteries in healthy adults
   Scientific Reports, 11, 12236, 2021.
    
10. Samaneh Nobakht, Morgan Schaeffer, Nils D. Forkert, Sean Nestor, Sandra Black, Phillip Barber:
    An automatic atlas- and CNN-based tool for segmentation of the hippocampus in MRI datasets according to the ADNI harmonized hippocampal protocol
    Sensors, 21(7), 2427, 2021.
     
11. Shakeel Qazi, Emmad Qazi, Alexis T. Wilson, Connor McDougall, Fahad Al-Ajlan, James Evans, Henrik Gensicke, Michael D. Hill, Ting-Yim Lee, Mayank Goyal, Andrew M. Demchuk, Bijoy K. Menon, Nils D. Forkert:
    Identifying thrombus on non-contrast CT in patients with acute ischemic stroke
    Diagnostics, 11(10), 1919, 2021.
     
12. Deepthi Rajashekar, Michael D. Hill, Andrew M. Demchuk, Mayank Goyal, Jens Fiehler, Nils D. Forkert:
    Prediction of clinical outcomes in acute ischaemic stroke patients: A comparative study
    Frontiers in Neurology, 12, 663899, 2021.
    
     
13. Mehrafarin Ramezani, Pauline Mouches, Eunjin Yoon, Deepthi Rajashekar, Jennifer Anne Ruskey, Etienne Leveille, Kristina Martens, Mekale Kibreab, Tracy Hammer, Iris Kathol, Nadia Marouf, Justyna Sarna, Davide Martino, Gerald Pfeffer, Ziv Gan-Or, Nils D. Forkert, Oury Monchi:
    Investigating the relationship between the SNCA gene and cognitive abilities in idiopathic Parkinson’s disease using machine learning
    Scientific Reports, 11, 4917, 2021.
     
14. Nagesh Subbanna, Matthias Wilms, Anup Tuladhar, Nils D. Forkert:
    An analysis of the vulnerability of two common deep learning based medical image segmentation techniques to model inversion attacks
    Sensors, 21(11), 3874, 2021.
     
15. Aron S. Talai, Jan Sedlacik, Kai Boelmans, Nils D. Forkert:
    Utility of multi-modal MRI for differentiating of Parkinson's disease and progressive supranuclear palsy using machine learning
    Frontiers in Neurology, 12, 648548, 2021.
     
16. Anup Tuladhar, Jasmine A. Moore, Zahinoor Ismail, Nils D. Forkert:
    Modeling neurodegeneration in silico with deep learning
    Frontiers in Neuroinformatics, 15, 748370, 2021.
     
17. Meng Wang, Eric E. Smith, Nils D. Forkert, Thierry Chekouo, Zahinoor Ismail, Aravind Ganesh, Tolulope Sajobi:
    Integrating expert knowledge for dementia risk prediction in individuals with mild cognitive impairment (MCI): A study protocol
    BMJ Open, 11(11), e051185, 2021.
     
18. Anthony Winder, Matthias Wilms, Jens Fiehler, Nils D. Forkert:
    Efficacy analysis in acute ischemic stroke patients using in-silico modelling based on machine learning: A proof-of-principle
    Biomedicines, 9(10), 1357, 2021.
     
19. Vivek S. Yedavalli, Elizabeth Tong, Dann C. Martin, Kristen W. Yeom, Nils D. Forkert:
    Artificial intelligence in stroke imaging: current and future perspectives
    Clinical Imaging, 69, 246-254, 2021.

Full-Length Proceedings Papers:

1. Kimberly Amador, Matthias Wilms, Anthony Winder, Jens Fiehler, Nils D. Forkert:
   Stroke lesion outcome prediction based on 4D CT perfusion data using a temporal convolutional network
   Medical Imaging with Deep Learning, Lübeck, Germany, 2021.
    
2. Pauline Mouches, Matthias Wilms, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
   Unifying brain age prediction and age-conditioned template generation with a deterministic autoencoder
   Medical Imaging with Deep Learning, Lübeck, Germany, 2021.
    
3. Nagesh Subbanna, Anup Tuladhar, Matthias Wilms, Nils D. Forkert:
   Understanding privacy risks in typical deep learning models for medical image analysis
   In: Deserno T.M., Park B.J. (eds), Imaging Informatics for Healthcare, Research, and Applications, SPIE Medical Imaging 2021, Vol. 11601, 116010E, San Diego, USA, 2021.
    
4. Hristina Uzunova, Jesse Kruse, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
   Analysis of generative shape modeling approaches: Latent space properties and interpretability
   In: Palm C., Deserno T.M., Handels H., Maier A., Maier-Hein K.H., Tolxdorff T. (eds.), Bildverarbeitung für die Medizin 2021, Heidelberg, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 344-349, 2021.
    
5. Matthias Wilms, Pauline Mouches, Jordan Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
   Towards self-explainable classifiers and regressors in neuroimaging with normalizing flows
   In: Abdulkadir A. et al. (eds) Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science, vol 13001. Springer, Cham. 23-33, 2021.

Abstracts:

1. Signe Bray, Christiane Rohr, Dennis Dimond, Shefali Rai, Nils D. Forkert, Ryann Tansey, Kirk Graff, Stephanie Deighton, Rylan Marianchuk:
   Connectome prediction modeling of weak effects in small samples: an empirical and simulation study
   27^th Annual Meeting of the Organization for Human Brain Mapping, Virtual Meeting, 2021.
    
2. Rebecca M. Green, Lucas Lo Vercio, Andreas Dauter, Si Han Guo, Samuel Robertson, Marta Marchini, Marta Vidal García, Xiang Zhao, Ralph S. Marcucio, Nils D. Forkert, Benedikt Hallgrímsson:
   Integration of cellular dynamics and morphology to understand mouse facial development
   Experimental Biology, Virtual Meeting, 2021.
    
3. A. Max Hamilton, Qandeel Shafqat, Nils D. Forkert, Ying Wu, Jeff F. Dunn:
   Grey matter atrophy measured in-vivo with 9.4T MRI in the cuprizone mouse model of demyelination
   29^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Virtual Meeting, 2021.
    
4. Jasmine A. Moore, Anup Tuladhar, Zahinoor Ismail, Nils D. Forkert:
   In silico modelling of neurodegeneration using deep convolutional neural networks
   3rd Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2021, Virtual Meeting, 2021.
    
5. Deepthi Rajashekar, Michael D. Hill, Andrew M. Demchuk, Mayank Goyal, Jens Fiehler, Nils D. Forkert:
   Selecting brain regions for predictive modeling of clinical outcomes in acute ischemic stroke
   59^th Annual Meeting of the American Society of Neuroradiology, Virtual Meeting, 2021.
    
6. Anup Tuladhar, Jasmine A. Moore, Nils D. Forkert:
   Modeling progressive neurodegeneration with deep convolutional neural networks
   27^th Annual Meeting of the Organization for Human Brain Mapping, Virtual Meeting, 2021.
    
7. Meng Wang, Nils D. Forkert, Thierry Chekouo, Zahinoor Ismail, Eric E. Smith, Tolulope Sajobi:
   A comparison of penalized cox regression and machine learning algorithms for dementia risk prediction
   Statistical Society of Canada Annual Meeting 2021, Virtual Meeting, 2021.

Journal Papers:

1. Jordan J. Bannister, Sebastian Crites, David Aponte, David Katz, Matthias Wilms, Ophir Klein, Francois P.J. Bernier, Richard A. Spritz, Benedikt Hallgrímsson, Nils D. Forkert:
   Fully automatic landmarking of syndromic 3D facial surface scans using 2D images
   Sensors, 20, 3171, 2020.
    
2. Gabriel Broocks, Uta Hanning, Tobias D. Faizy, Alexandra Scheibel, Jawed Nawabi, Gerhard Schön, Nils D. Forkert, Sönke Langner, Jens Fiehler, Susanne Gellißen, Andre Kemmling:
   Ischemic lesion growth in acute stroke: Water uptake quantification distinguishes between edema and tissue infarct
   Journal of Cerebral Blood Flow and Metabolism, 40(4), 823-832, 2020.
    
3. Elliot C. Brown, Darren L. Clark, Nils D. Forkert, Christine P. Molnar, Zelma H. T. Kiss, Rajamannar Ramasubbu:
   Metabolic activity in subcallosal cingulate predicts response to deep brain stimulation for depression
   Neuropsychopharmacology, 45(10), 1681–1688, 2020.
    
4. Helen L. Carlson, Brandon T. Craig, Alicia Hilderley, Jacquie Hodge, Deepthi Rajashekar, Pauline Mouches, Nils D. Forkert, Adam Kirton:
   Structural and functional connectivity of motor circuits after perinatal stroke: A machine learning study
   NeuroImage: Clinical, 28, 102508, 2020.
    
5. Christopher D. d’Esterre, Rani G. Sah, Zarina Aziz, Aron S. Talai, Andrew M. Demchuk, Michael D. Hill, Mayank Goyal, Ting-Yim Lee, Nils D. Forkert, Philip A. Barber:
   Defining reperfusion post endovascular therapy in ischemic stroke using MR-dynamic contrast enhanced perfusion
   British Journal of Radiology, 93(1116), 20190890, 2020.
    
6. Jay Devine, Jose D. Aponte, David C. Katz, Wei Liu, Lucas D. Lo Vercio, Nils D. Forkert, Ralph Marcucio, Christopher J. Percival, Benedikt Hallgrímsson:
   A registration and deep learning approach to automated landmark detection for geometric morphometrics
   Evolutionary Biology, 47, 246–259, 2020.
    
7. Sascha Gill, Pauline Mouches, Sophie Hu, Deepthi Rajashekar, Frank P. MacMaster, Eric E. Smith, Nils D. Forkert, Zahinoor Ismail:
   Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data
   Journal of Alzheimer's Disease, 75(1), 277-288, 2020.
    
8. Matthias Graeser, Peter Ludewig, Patryk Szwargulski, Fynn Förger, Tom Liebing, Nils D. Forkert, Florian Thieben, Tim Magnus, Tobias Knopp:
   Design of a head coil for high resolution mouse brain perfusion imaging using magnetic particle imaging
   Physics in Medicine and Biology, 65(23), 235007, 2020.
    
9. Malte Grosser, Susanne Gellissen, Patrick Borchert, Jan Sedlacik, Jawed Nawabi, Jens Fiehler, Nils D. Forkert:
   Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features
   PLOS One, 15(1), e0228113, 2020.
    
10. Malte Grosser, Susanne Gellissen, Patrick Borchert, Jan Sedlacik, Jawed Nawabi, Jens Fiehler, Nils D. Forkert:
    Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets
    PLOS One, 15(11), e0241917, 2020.
     
11. Benedikt Hallgrímsson, J. David Aponte, David C. Katz, Jordan J. Bannister, Sheri L. Riccardi, Nick Mahasuwan, Brenda L. McInnes, Tracey M. Ferrara, Danika M. Lipman, Amanda B. Neves, Jared A.J. Spitzmacher, Jacinda R. Larson, Gary A. Bellus, Anh M. Pham, Elias Aboujaoude, Timothy A. Benke, Kathryn C. Chatfield, Shanlee M. Davis, Ellen R. Elias, Robert W. Enzenauer, Brooke M. French, Laura L. Pickler, Joseph T.C. Shieh, Anne Slavotinek, A. Robertson Harrop, A. Micheil Innes, Shawn E. McCandless, Emily A. McCourt, Naomi J.L. Meeks, Nicole R. Tartaglia, Anne C.-H. Tsai, J. Patrick H. Wyse, Jonathan A. Bernstein, Pedro A. Sanchez-Lara, Nils D. Forkert, Francois P. Bernier, Richard A. Spritz, Ophir D. Klein:
    Automated syndrome diagnosis by three-dimensional facial imaging
    Genetics in Medicine, 22(10), 1682-1693, 2020.
     
12. Lucas Lo Vercio, Kimberly Amador, Jordan J. Bannister, Sebastian Crites, Alejandro Gutierrez, M. Ethan MacDonald, Jasmine Moore, Pauline Mouches, Deepthi Rajashekar, Serena Schimert, Nagesh Subbanna, Anup Tuladhar, Nanjia Wang, Matthias Wilms, Anthony Winder, Nils D. Forkert:
    Supervised machine learning tools: a tutorial for clinicians
    Journal of Neural Engineering, 17(6), 062001, 2020.
     
13. M. Ethan MacDonald, Rebecca J. Williams, Deepthi Rajashekar, Randall B. Stafford, Alexadru Hanganu, Hongfu Sun, Avery J.L. Berman, Cheryl M. McCreary, Richard Frayne, Nils D. Forkert, G. Bruce Pike:
    The effect of aging on cerebral blood flow and cortical thickness with application to age prediction
    Neurobiology of Aging, 95, 131-142, 2020.
     
14. Sarah J. MacEachern, Jonathan D. Santoro, Cara Hahn, Zack Madress, Ximena Stecher, Matthew D. Li, Jin S. Hahn, Kristen W. Yeom, Nils D. Forkert:
    Children with epilepsy demonstrate macro- and microstructural changes in the thalamus, putamen, and amygdala
    Neuroradiology, 62(3), 389-397, 2020.
     
15. Connor McDougall, Leona Chan, Surbhi Sachan, Jen Guo, Rani G. Sah, Bijoy Menon, Andrew M. Demchuk, Michael D. Hill, Nils D. Forkert, Christopher D. d’Esterre, Philip A. Barber:
    Dynamic CTA-derived perfusion maps predict final infarct volume – the simple perfusion reconstruction algorithm
    American Journal of Neuroradiology, 41(11), 2034-2040, 2020.
     
16. Peter K. Moon, Jason Z. Qian, Emily McKenna, Kevin Xi, Nathan C. Rowe, Nathan N. Ng, Jimmy Zheng, Lydia T. Tam, Sarah J. MacEachern, Iram Ahmad, Alan G. Cheng, Nils D. Forkert, Kristen W. Yeom:
    Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss
    NeuroImage: Clinical, 27, 102328, 2020.
     
17. James P. Naude, Sascha Gill, Sophie Hu, Alexander McGirr, Nils D. Forkert, Oury Monchi, Peter K. Stys, Eric E. Smith, Zahinoor Ismail:
    Plasma neurofilament light: a marker of neurodegeneration in mild behavioural impairment
    Journal of Alzheimer's Disease, 76(3), 1017-1027, 2020.
     
18. Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Clarissa L. Yasuda, Magdalena Sokolska, Rolf H. Jäger, Nils D. Forkert:
    Segmentation-based blood flow parameter refinement in cerebrovascular structures using 4D arterial spin labeling MRA
    IEEE Transactions on Biomedical Engineering, 67(7), 1936-1946, 2020.
     
19. Jennifer L. Quon, Lily H. Kim, Sarah J. MacEachern, Maryam Maleki, Gary Steinberg, Venkatesh Madhugiri, Michael S.B. Edwards, Gerald A. Grant, Kristen W. Yeom, Nils D. Forkert:
    Early diffusion magnetic resonance imaging changes in normal-appearing brain in pediatric Moyamoya disease
    Neurosurgery, 86(4), 530-537, 2020.
     
20. Deepthi Rajashekar, Pauline Mouches, Jens Fiehler, Bijoy K. Menon, Mayank Goyal, Andrew M. Demchuk, Michael D. Hill, Sean P. Dukelow, Nils D. Forkert:
    Structural integrity of white matter tracts as a predictor of acute ischemic stroke outcome
    International Journal of Stroke, 15(9), 965-972, 2020.
     
21. Deepthi Rajashekar, Matthias Wilms, Kent Hecker, Michael D. Hill, Sean Dukelow, Jens Fiehler, Nils D. Forkert:
    The impact of covariates in voxel-wise lesion-symptom mapping
    Frontiers in Neurology, 11, 854, 2020.
     
22. Deepthi Rajashekar, Matthias Wilms, M. Ethan MacDonald, Jan Ehrhardt, Pauline Mouches, Richard Frayne, Michael D. Hill, Nils D. Forkert:
    High-resolution normative T2-FLAIR and CT brain atlas of the elderly
    Scientific Data, 7, 56, 2020.
     
23. Rani G. Sah, Samaneh Nobakht, Deepthi Rajashekar, Pauline Mouches, Nils D. Forkert, Amith Sitaram, Adrian Tsang, Michael D. Hill, Andrew M. Demchuk, Christopher D. d’Esterre, Philip A. Barber:
    Temporal evolution and spatial distribution of quantitative T2 MRI following acute ischemia reperfusion injury
    International Journal of Stroke, 15(5), 495-506, 2020.
     
24. Trevor A. Seeger, Jason Tabor, Stacy Sick, Kathryn J. Schneider, Craig Jenne, Parker La, Aron S. Talai, Deepthi Rajashekar, Pauline Mouches, Nils D. Forkert, Carolyn Emery, Chantel T. Debert:
    The association of saliva cytokines and pediatric sport-related concussion outcomes
    Journal of Head Trauma Rehabilitation, 35(5), 354-362, 2020.
     
25. Giles Tetteh, Velizar Efremov, Nils D. Forkert, Matthias Schneider, Jan Kirschke, Bruno Weber, Claus Zimmer, Marie Piraud, Björn H. Menze:
    DeepVesselNet: Vessel segmentation, centerline prediction, and bifurcation detection in 3D angiographic volumes
    Frontiers in Neuroscience, 14, 592352, 2020.
     
26. Anup Tuladhar, Sascha Gill, Zahinoor Ismail, Nils D. Forkert:
    Distributed learning in medicine: building machine learning models without sharing patient data
    Journal of Biomedical Informatics, 106, 103424, 2020.
     
27. Anup Tuladhar, Serena Schimert, Deepthi Rajashekar, Helge Kniep, Jens Fiehler, Nils D. Forkert:
    Automatic segmentation of stroke lesions in non-contrast computed tomography datasets with convolutional neural networks
    IEEE Access, 8, 94871 – 94879, 2020.
     
28. Anthony Winder, Christopher D. d’Esterre, Bijoy K. Menon, Jens Fiehler, Nils D. Forkert:
    Automatic arterial input function selection in CT and MR perfusion datasets using deep convolutional neural networks
    Medical Physics, 47(9), 4199-4211, 2020.
     
29. Jimmy Zheng, Jennifer Frankovich, Emily S. McKenna, Nathan C. Rowe, Sarah J. MacEachern, Nathan N. Ng, Lydia T. Tam, Peter K. Moon, Jaynelle Gao, Margo Thienemann, Nils D. Forkert, Kristen W. Yeom:
    Association of pediatric acute-onset neuropsychiatric syndrome with microstructural differences in brain regions detected via diffusion-weighted magnetic resonance imaging
    JAMA Network Open, 3(5), e204063, 2020.

Full-Length Proceedings Papers:

1. Hristina Uzunova, Paul Kaftan, Matthias Wilms, Nils D. Forkert, Heinz Handels, Jan Ehrhardt:
   Quantitative comparison of generative shape models for medical images
   In: Tolxdorff T. Deserno T.M., Handels H., Maier A., Maier-Hein K.H., Palm C. (eds.), Bildverarbeitung für die Medizin 2020, Heidelberg, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 201-207, 2020.
    
2. Matthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
   Bidirectional modeling and analysis of brain aging with normalizing flows
   In: Kia S.M. et al. (eds) Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. Lecture Notes in Computer Science, Vol 12449, 23--33, 2020.
    
3. Matthias Wilms, Jan Ehrhardt, Nils D. Forkert:
   A kernelized multi-level localization method for flexible shape modeling with few training data
   In: Martel A.L. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science, Vol 12264, 765-775, 2020.

Abstracts:

1. Helen, L. Carlson, Brandon T. Craig, Jacquie Hodge, Deepthi Rajashekar, Pauline Mouches, Nils D. Forkert, Adam Kirton:
   Neuroimaging can predict personalized motor function after perinatal stroke: A machine learning study
   26^th Annual Meeting of the Organization for Human Brain Mapping, Montreal, Canada, 2020.
    
2. Rebecca M Green, Lucas Lo Vercio, Si Han Guo, Andreas Dauter, Marta Marchini, Marta Vidal-García, Zhang Zhao, Ralph S. Marcucio, Nils D. Forkert, Benedikt Hallgrímsson:
   Mapping the relationship between proliferation and morphology in the mouse face
   Society for Craniofacial Genetics and Developmental Biology (SCGDB) Annual Meeting, virtual meeting, 2020.
    
3. Sarah J. MacEachern, Deepthi Rajashekar, Pauline Mouches, Nathan C. Rowe, Emily McKenna, Kristen W. Yeom, Nils D. Forkert:
   Image-based classification of children with autism spectrum disorder
   34^th International Computer Assisted Radiology and Surgery (CARS) Congress, Munich, Germany, 2020.
    
4. Peter K. Moon, Nathan N. Ng, Jimmy Zheng, Emily McKenna, Katie Shpanskaya, Nathan C. Rowe, Sarah J. MacEachern, Alan Cheng, Nils D. Forkert, Kristen Yeom:
   Atlas-based regional brain diffusion MRI changes are present in children with sensorineural hearing loss
   American Society of Pediatric Neuroradiology (ASPNR) 2^nd Annual Meeting, Miami Beach, USA, 2020.
    
5. Pauline Mouches, Banafshe Felfeliyan, Sönke Langner, Nils D. Forkert:
   Biological brain age prediction ability of different subcortical structures using deep learning
   26^th Annual Meeting of the Organization for Human Brain Mapping, Montreal, Canada, 2020.
    
6. Nathan N. Ng, Jimmy Zheng, Lydia Tam, Katie Shpanskaya, Emily McKenna, Kenneth N. Huynh, Sarah J. MacEachern, Cynthia J. Campen, Nils D. Forkert, Kristen W. Yeom:
   Atlas-based multi-parametric diffusion and arterial spin-labeled perfusion changes in children with neurofibromatosis type 1
   American Society of Pediatric Neuroradiology (ASPNR) 2^nd Annual Meeting, Miami Beach, USA, 2020.
    
7. Lydia Tam, Nathan Ng, Peter Moon, Jimmy Zheng, Emily McKenna, Nils D. Forkert, Cynthia Campen, Kristen W. Yeom:
   Examining diffusion, arterial spin-labeled perfusion, and volumetric changes in the neurofibromatosis type 1 brain using an atlas-based, multi-parametric approach
   19^th International Symposium on Pediatric Neuro-Oncology (ISPNO), Karuizawa, Japan, 2020.
    
8. Jimmy Zheng, Jennifer Frankovich, Nathan C. Rowe, Sarah J. MacEachern, Nathan N. Ng, Peter K. Moon, Jaynelle Gao, Margo Thienemann, Nils D. Forkert, Kristen W. Yeom:
   Microstructural changes in the deep grey matter in pediatric acute-onset neuropsychiatric syndrome
   American Society of Pediatric Neuroradiology (ASPNR) 2^nd Annual Meeting, Miami Beach, USA, 2020.

Journal Papers:

1. Jonathan Doucette, Luxi Wei, Enedino Hernández-Torres, Christian Kames, Nils D. Forkert, Rasmus Aamand, Torben E. Lund, Brian Hansen, Alexander Rauscher:
   Rapid solution of the Bloch-Torrey equation in anisotropic tissue: Application to dynamic susceptibility contrast MRI of cerebral white matter
   NeuroImage, 185, 198-207, 2019.
    
2. Jens Fiehler, Götz Thomalla, Martina Bernhardt, Helge Kniep, Ansgar Berlis, Franziska Dorn, Bernd Eckert, Andre Kemmling, Sönke Langner, Luca Remonda, Wolfgang Reith, Stefan Rohde, Markus Möhlenbruch, Martin Bendszus, Nils D. Forkert, Susanne Gellissen:
   ERASER: A thrombectomy study with predictive analytics end point
   Stroke, 50(5), 1275-1278, 2019.
    
3. A. Max Hamilton, Nils D. Forkert, Runze Yang, Ying Wu, James A. Rogers, V. Wee Yong, Jeff F. Dunn:
   Central nervous system targeted autoimmunity causes regional atrophy: a 9.4T MRI study of the EAE mouse model of multiple sclerosis
   Scientific Reports, 9, 8488, 2019.
    
4. Devon Livingstone, Aron S. Talai, Justin Chau, Nils D. Forkert:
   Building an otoscopic screening tool using deep learning image recognition
   Journal of Otolaryngology-Head & Neck Surgery, 48, 66, 2019.
    
5. M. Ethan MacDonald, Rebecca J. Williams, Nils D. Forkert, Avery J.L. Berman, Cheryl R. McCreary, Richard Frayne, G. Bruce Pike:
   Interdatabase variability in cortical thickness measurements
   Cerebral Cortex, 29(8), 3282–3293, 2019.
    
6. Pauline Mouches, Nils D. Forkert:
   A statistical atlas of cerebral arteries generated using multi-center MRA datasets from healthy subjects
   Scientific Data, 6(1), 29, 2019.
    
7. Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Thomas Okell, Nils D. Forkert:
   A methodology for generating four-dimensional arterial spin labeling MR angiography virtual phantoms
   Medical Image Analysis, 56, 184-192, 2019.
    
8. Rajamannar Ramasubbu, Elliot C. Brown, Lorenzo Marcil, Aron S. Talai, Nils D. Forkert:
   Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements
   Psychiatry and Clinical Neurosciences, 73(8), 486-493, 2019.
    
9. Meaghan Reid, Akinrinola O. Famuyide, Nils D. Forkert, Aron S. Talai, James W. Evans, Amith Sitaram, Moiz Hafeez, Mohamed Najm, Bijoy Menon, Andrew Demchuk, Mayank Goyal, Rani G. Sah, Christopher D. d’Esterre, Philip Barber:
   Accuracy and reliability of multiphase CTA perfusion for identifying ischemic core
   Clinical Neuroradiology, 29(3), 543-552, 2019.
    
10. Paul Reidler, Kolja M. Thierfelder, Lukas T. Rotkopf, Matthias P. Fabritius, Daniel Puhr-Westerheide, Franziska Dorn, Nils D. Forkert, André Kemmling, Wolfgang G. Kunz:
    Attenuation changes in ASPECTS regions: A surrogate for CT perfusion-based ischemic core in acute ischemic stroke
    Radiology, 291(2), 451-458, 2019.
     
11. Rani G. Sah, Christopher D. d’Esterre, Michael D. Hill, Moiz Hafeez, Sana Tariq, Nils D. Forkert, Andrew M. Demchuk, Mayank Goyal, Philip A. Barber:
    Diffusion-weighted MRI stroke lesion evolution following recanalization treatment is threshold-dependent
    Clinical Neuroradiology, 29(1), 135-141, 2019.
     
12. Rani G. Sah, Christopher D. d’Esterre, Michael D. Hill, Moiz Hafeez, Sana Tariq, Nils D. Forkert, Richard Frayne, Andrew Demchuk, Mayank Goyal, Philip A. Barber:
    Diffusion-weighted imaging lesion growth occurs despite recanalization in acute ischemic stroke: Implications for future treatment trials
    International Journal of Stroke, 14(3), 257-264, 2019.
     
13. Michael H. Schönfeld, Nils D. Forkert, Jens Fiehler, Young Dae Cho, Moon Hee Han, Hyun-Seung Kang, Thomas W. Peach, James V. Byrne:
    Hemodynamic differences between recurrent and non-recurrent intracranial aneurysms: fluid dynamics simulations based on MR angiography
    Journal of Neuroimaging, 29(4), 447-453, 2019.
     
14. Nagesh K. Subbanna, Deepthi Rajashekar, Bastian Cheng, Götz Thomalla, Jens Fiehler, Tal Arbel, Nils D. Forkert:
    Stroke lesion segmentation in FLAIR MRI datasets using customized Markov random fields
    Frontiers in Neurology, 10, Article 541, 2019.
     
15. Aron S. Talai, Zahinoor Ismail, Jan Sedlacik, Kai Boelmans, Nils D. Forkert:
    Improved automatic morphology-based classification of Parkinson’s disease and progressive supranuclear palsy - Richardson's syndrome
    Clinical Neuroradiology, 29(4), 605-614, 2019.
     
16. Anthony Winder, Susanne Siemonsen, Fabian Flottmann, Götz Thomalla, Jens Fiehler, Nils D. Forkert:
    Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients
    Scientific Reports, 9, 13208, 2019.
     
17. Derek Yecies, Tej D. Azad, Rogelio Esparza, Jennifer Quon, Nils D. Forkert, Sarah J. MacEachern, Lisa Bruckert, Maryam Maleki, Michael Edwards, Gerald Grant, Kristen W. Yeom:
    Long-term supratentorial radiological effects of surgery and local radiation in children with infratentorial ependymoma
    World Neurosurgery, 122, e1300-e1304, 2019.

Full-Length Proceedings Papers:

1. Bryce A. Besler, Leigh Gabel, Lauren A. Burt, Nils D. Forkert, Steven K. Boyd:
   Bone adaptation as level set motion
   In: Vrtovec T., Yao, J., Zheng, G., Pozo, J.M. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2018. Lecture Notes in Computer Science, Vol. 11404, 58-72, 2019.
    
2. Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils D. Forkert:
   The effect of labeling duration and temporal resolution on arterial transit time estimation accuracy in 4D ASL MRA datasets - a flow phantom study
   In: Liao H. et al. (eds) Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. MLMECH 2019, CVII-STENT 2019. Lecture Notes in Computer Science, Vol 11794, 141-148, 2019.

Abstracts:

1. Bryce A. Besler, Leigh Gabel, Lauren A. Burt, Nils D. Forkert, Steven K. Boyd:
   Level set motion as a framework for computational modeling of bone adaptation
   22nd International Workshop on Quantitative Musculoskeletal Imaging (QMSKI), Lake Louise, Canada, 2019.
    
2. Elliot Brown, Nils D. Forkert, Darren Clark, Zelma Kiss, Rajamannar Ramasubbu:
   Using multimodal neuroimaging and machine learning to determine response to subcallosal cingulate deep brain stimulation (SCC-DBS) for depression
   74^th Annual Meeting of the Society of Biological Psychiatry, Chicago, USA, 2019.
    
3. Banafshe Felfeliyan, Jessica C. Küpper, Nils D. Forkert, Janet Ronsky:
   Bone and cartilage segmentation from multiplanar MR images using state-of-the-art convolutional neural networkInternational Workshop on Osteoarthritis Imaging 2019, Charlottetown, Canada, 2019.
    
4. Sascha Gill, Meng Wang, Nils D. Forkert, Frank MacMaster, Eric Smith, Zahinoor Ismail:
   Diffusion tensor imaging in pre-dementia risk states: white matter atrophy findings in mild behavioral impairment
   American Academy of Neurology (AAN) Annual Meeting 2019, Philadelphia, USA, 2019.
    
5. Sascha Gill, Pauline Mouches, Meng Wang, Frank P. MacMaster, Eric E. Smith, Nils D. Forkert, Zahinoor Ismail:
   Using machine learning to identify neuroimaging and clinical features of mild behavioral impairment (MBI)
   Alzheimer's Association International Conference 2019, Los Angeles, USA.
    
6. Sascha Gill, Pauline Mouches, Sophie Hu, Muhammad Ahmad, Nils D. Forkert, Zahinoor Ismail:
   A machine learning approach to predict change in diagnostic category in pre-dementia
   15^th Annual Meeting of the International Society for CNS Clinical Trials and Methodology (ISCTM), Washington, USA, 2019.
    
7. A. Max Hamilton, Nils D. Forkert, Runze Yang, Ying Wu, James A. Rogers, V. Wee Yong, Jeff F. Dunn:
   Grey matter atrophy measured with MRI correlates with reduced neuronal density in the experimental autoimmune encephalomyelitis model of multiple sclerosis
   27^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Montreal, Canada, 2019.
    
8. Zahinoor Ismail, Sophie Hu, Sascha Gill, Nils D. Forkert, Eric Smith:
   Subjective cognitive decline and mild behavioral impairment together predict mild cognitive impairment at 3 years better than either syndrome alone
   Alzheimer's Association International Conference 2019, Los Angeles, USA.
    
9. Wolfgang G. Kunz, Nils D. Forkert, Steffen Tiedt, Frank A. Wollenweber, Lars Kellert, Thomas Liebig, André Kemmling, Paul Reidler:
   Automated regional density measurements on baseline non-contrast CT predict final infarction in acute ischemic stroke patients
   International Stroke Conference 2019, Honolulu, USA, 2019.
    
10. Devon M. Livingstone, Aron S. Talai, Justin K. Chau, Nils D. Forkert:
    Building an otoscopic screening tool using deep learning image recognition
    Triological Society 122^nd Annual Meeting, Austin, USA.
     
11. M. Ethan MacDonald, Deepthi Rajashekar, Rebecca J. Williams, Hongfu Sun, Cheryl McCreary, Richard Frayne, Nils D. Forkert, G. Bruce Pike:
    Machine learning methods for age prediction using cortical thickness and cerebral blood flow
    25^th Annual Meeting of the Organization for Human Brain Mapping, Rome, Italy, 2019.
     
12. Renzo Phellan, Lívia Rodrigues, Gustavo Retuci Pinheiro, Andrés Quiroga Soto, Igor Duarte Rodrigues, Leticia Rittner, Ricardo Ferrari, Matthew R. G. Brown, Nils D. Forkert, Roberto Souza, Mariana Bento:
    Automatic detection of age- and sex-related differences in human brain morphology
    27^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Montreal, Canada, 2019.
     
13. Jennifer L. Quon, Lily H. Kim, Pauline Mouches, Rashad Jabarkheel, Yi Zhang, Gary Steinberg, Gerald Grant, Michael S.B. Edwards, Kristen Yeom, Nils D. Forkert:
    Automated evaluation of intracranial vessel morphology in normals versus pediatric moyamoya disease
    The Congress of Neurological Surgeons Annual Meeting 2019, San Francisco, USA, 2019
     
14. Deepthi Rajashekar, Ravinder Singh, Bijoy Menon, Nils D. Forkert:
    Eloquence of white matter tracts in acute ischemic stroke patients
    International Stroke Conference 2019, Honolulu, USA, 2019.
     
15. Meaghan Reid, Connor McDougall, Nils D. Forkert, Richard Frayne, Shelagh Coutts, Rani G. Sah, Christopher D. d’Esterre, Philip Barber:
    The association between decreased cerebral blood flow in transient ischemic attack patients and cognition
    International Stroke Conference 2019, Honolulu, USA, 2019.
     
16. Sasha Rogers, W. Bradley Jacobs, Steven Casha, Nils D. Forkert, Pauline Mouches, David W. Cadotte:
    Machine Learning to predict a single patient clinical course: how will your life change after a diagnosis of degenerative cervical myelopathy?
    19th Annual Scientific Conference of the Canadian Spine Society, Toronto, Canada, 2019.
     
17. Nagesh Subbanna, Alexander Rauscher, David Li, Anthony Traboulsee, G. Bruce Pike, Nils D. Forkert:
    Morphology-based estimation of disease duration in multiple sclerosis patients using T1-weighted MRI datasets
    33^rd International Computer Assisted Radiology and Surgery (CARS) Congress, Rennes, France, 2019.
    
    
    
    
    
    
    
    
    
    
    
    
     

Journal Papers:

1. Marielle Ernst, Anna M.M. Boers, Nils D. Forkert, Olvert A. Berkhemer, Yvo B. Roos, Diederik W.J. Dippel, Aad van der Lugt, Robert J. van Oostenbrugge, Wim H. van Zwam, Eik Vettorazzi, Jens Fiehler, Henk A. Marquering, Charles B.L.M. Majoie, Susanne Gellissen:
   Impact of ischemic lesion location on the mRS score in patients with ischemic stroke: A voxel-based approach
   American Journal of Neuroradiology, 39(11), 1989-1994, 2018.
    
2. Lauren E. Mak, B. Anne Croy_, James N. Reynolds, Matthew T. Rätsep, Nils D. Forkert, Graeme N. Smith, Angelina Paolozza, Patrick W. Stroman, Ernesto A. Figueiró-Filho:
   Resting-state functional connectivity in children born from gestations complicated by preeclampsia: A pilot study cohort
   Pregnancy Hypertension, 12, 23-28, 2018.
    
3. Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcao, Nils D. Forkert:
   Automatic temporal segmentation of vessels of the brain using 4D ASL MRA images
   IEEE Transactions on Biomedical Engineering, 65(7), 1486-1494, 2018.
    
4. Chressen C. Remus, Fabian Kording, Petra Arck, Emilia Solano, Jan Sedlacik, Gerhard Adam, Kurt Hecher, Nils D. Forkert:
   DCE MRI reveals early decreased and later increased placenta perfusion after a stress challenge during pregnancy in mice
   Placenta, 65, 15-19, 2018.
    
5. Jonathan D. Santoro, Nils D. Forkert, Qian-Zhou Yang, Sarah Pavitt, Sarah J. MacEachern, Michael Moseley, Kristen W. Yeom:
   Brain diffusion abnormalities in children with tension-type and migraine-type headaches
   American Journal of Neuroradiology, 39(5), 935-941, 2018.
    
6. Aron S. Talai, Jan Sedlacik, Kai Boelmans, Nils D. Forkert:
   Widespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy
   NeuroImage: Clinical, 20, 1037-1043, 2018.
    
7. Sana Tariq, Christopher D. d’Esterre, Tolulope T. Sajobi, Eric E. Smith, Richard Stewart Longman, Richard Frayne, Shelagh B. Coutts, Nils D. Forkert, Philip A. Barber:
   A longitudinal magnetic resonance imaging study of neurodegenerative and small vessel disease, and clinical cognitive trajectories in non demented patients with transient ischemic attack: The PREVENT study
   BMC Geriatrics, 18, 163, 2018.
    
8. Wilby Williamson, Adam J. Lewandowski, Nils D. Forkert, Ludovica Griffanti, Thomas W. O’Kell, Jill Betts, Henry Boardman, Timo Siepmann, David McKean, Odaro Huckstep, Jane M. Francis, Stefan Neubauer, Renzo Phellan, Mark Jenkinson, Aiden Doherty, Helen Dawes, Eleni Frangou, Christina Malamateniou, Charlie Foster, Paul Leeson:
   Association of cardiovascular risk factors with MRI indices of cerebrovascular structure and function and white matter hyperintensities in young adults
   Journal of the American Medical Association, 320(7), 665-673, 2018.

Full-Length Proceedings Papers:

1. Bryce A. Besler, Andrew Michalski, Nils D. Forkert, Steven K. Boyd:
   Automatic full femur segmentation from computed tomography datasets using an atlas-based approach
   In: Glocker B., Yao J., Vrtovec T., Frangi A., Zheng G. (eds) Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science, Vol. 10734, 120-132, 2018.
    
2. Renzo Phellan, Thomas Lindner, Michael Helle, Alexandre X. Falcão, Nils D. Forkert:
   Robust cerebrovascular segmentation in 4D ASL MRA images
   In: Proceedings of the 2018 IEEE International Symposium on Biomedical Imaging (ISBI), 1348 - 1351, Washington, USA, 2018.
    
3. Renzo Phellan, Thomas Lindner, Michael Helle, Thiago V. Spina, Alexandre X. Falcão, Nils D. Forkert:
   Four-dimensional ASL MR angiography phantoms with noise learned by neural styling
   In: Stoyanov D. et al. (eds) Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS 2018, CVII-STENT 2018. Lecture Notes in Computer Science, Vol. 11043, 131-140, 2018.
   
    

Abstracts:

1. Bryce A. Besler, Nils D. Forkert, Lauren A. Burt, Steven K. Boyd:
   Machine learned features and classifier for automatic HR-pQCT cortical and trabecular compartment segmentation
   Annual Meeting fo the American Society for Bone and Mineral Research (ASBMR), 2018, Montreal, Canada, 2018.
    
2. Elliot Brown, Nils D. Forkert, Lorenzo Marcil, Aron Sahand Talai, Rajamannar Ramasubbu:
   The use of arterial spin labeling perfusion MRI for automated classification of major depression disorder
   73rd Annual Meeting of the Society of Biological Psychiatry, New York, USA, 2018.
    
3. Christopher D. d’Esterre, Zarina Aziz, Rani G. Sah, Moiz Hafeez, Nils D. Forkert, Philip A. Barber:
   Successful reperfusion post endovascular therapy in ischemic stroke with MR-DCE perfusion
   European Stroke Organisation Conference 2018, Gothenburg, Sweden, 2018.
    
4. Marielle Ernst, Anna M. M. Boers, Nils D. Forkert, Olvert A. Berkhemer, Yvo B. Roos, Diederik W.J. Dippel, Aad van der Lugt, Robert J. van Oostenbrugge, Wim H. van Zwam, Jens Fiehler, Henk A. Marquering, Charles B.L.M. Majoie, Susanne Siemonsen:
   Impact of CT ischemic lesion location on modified Rankin scale score in patients with acute ischemic stroke – a voxel-based approach
   56nd Annual Meeting of the American Society of Neuroradiology, Vancouver, Canada, 2018.
    
5. Jens Fiehler, Helge Kniep, Martina Bernhard, Martin Bendszus, Markus Möhlenbruch, Götz Thomalla, Nils D. Forkert, Susanne Gellißen:
   ERASER: First thrombectomy study with predictive analytics endpoint
   European Stroke Organisation Conference 2018, Gothenburg, Sweden, 2018.
    
6. Evgenia Klourfeld, Aron S. Talai, Cheryl R. McCreary, Nils D. Forkert, Eric E. Smith:
   Texture analysis of normal appearing white matter and white matter hyperintensities in cerebral amyloid angiopathy
   6th International Cerebral Amyloid Angiopathy Conference, Lille, France, 2018.
    
7. Wolfgang G. Kunz, Lukas Rotkopf, Daniel Puhr-Westerheide, Matthias P. Fabritius, Nils D. Forkert, Paul Reidler, Franziska Dorn, Steffen Tiedt, Kolja M. Thierfelder, Frank Wollenweber, Andre Kemmling:
   Non-contrast ASPECTS region density predicts final infarction in acute ischemic stroke
   Annual Meeting of the Radiological Society of North America (RSNA), Chicago, USA, 2018.
    
8. M. Ethan MacDonald, Nils D. Forkert, Yuhan Ma, Rebecca J. Williams, Alexandru Hanganu, Hongfu Sun, Randall Stafford, Cheryl R. McCreary, Richard Frayne, G. Bruce Pike:
   Cerebrovascular Brain Aging Examined with Arterial Spin Labelling and Applied to Age Prediction
   26th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Paris, France, 2018.
    
9. Marta Marchini, Diane Hu, Nils D. Forkert, Rebecca Green, Ralph Marcucio, Benedikt Hallgrimsson:
   Testing the relationship between the Shh expression domain and head shape
   7th meeting of the European Society for Evolutionary Developmental Biology, Galway, Ireland, 2018.
    
10. Samaneh Nobakht, Nils D. Forkert, Sean Nestor, Sandra Black, Phillip Barber:
    Pre-training and training of a convolutional neural network for automatic and accurate hippocampus segmentation from T1-weighted MRI datasets
    26th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Paris, France, 2018.
     
11. Paul Reidler, Daniel Puhr-Westerheide, Matthias P. Fabritius, Lukas Rotkopf, Felix Schuler, Wolfgang G. Kunz, Daniel Apel, Nils D. Forkert, Steffen Tiedt, Frank Wollenweber, Kolja M. Thierfelder, Andre Kemmling:
    Clinical decision support based on automated non-contrast CT density measurements in patients with acute ischemic stroke
    Annual Meeting of the Radiological Society of North America (RSNA), Chicago, USA, 2018.

Journal Papers:

1. Bastian Cheng, Christian Knaack, Nils D. Forkert, Renate Schnabel, Christian Gerloff, Götz Thomalla:
   Stroke subtype classification by geometrical descriptors of lesion shape
   PLOS ONE, 12(12), e0185063, 2017.
    
2. Bastian Cheng, Nikolaus Schröder, Nils D. Forkert, Peter Ludewig, André Kemmling, Tim Magnus, Jens Fiehler, Christian Gerloff, Götz Thomalla:
   Hypointense vessels detected by susceptibility-weighted imaging identifies tissue at risk of infarction in anterior circulation stroke
   Journal of Neuroimaging, 27(4), 414-420, 2017.
    
3. Christopher D. d’Esterre, Anurag Trivedi, Pooneh Pordeli, Mari Boesen, Shivanand Patil, Seong Hwan Ahn, Mohammed Najm, Enrico Fainardi, Jai Shankar, Marta Rubiera, Mohammed A. Almekhlafi, Jennifer Mandzia, Alexander V. Khaw, Philip Barber, Shelagh Coutts, Michael D. Hill, Andrew M. Demchuk, Tolupe Sajobi, Nils D. Forkert, Mayank Goyal, Ting-Yim Lee, Bijoy K. Menon:
   Regional comparison of multiphase computed tomography angiography and computed tomography perfusion for prediction of tissue fate in ischemic stroke
   Stroke, 48(4), 939-945, 2017.
    
4. Ernesto A. Figueiró-Filho, B. Anne Croy, James N. Reynolds, Frances Dang, Dorart Piro, Matthew T. Rätsep, Nils D. Forkert, Angelina Paolozza, Graeme N. Smith, Patrick W. Stroman:
   Diffusion tensor imaging of white matter in children born from preeclamptic gestations
   American Journal of Neuroradiology, 38(4), 801-806, 2017.
    
5. Ernesto A. Figueiró-Filho, Lauren E. Mak, James N. Reynolds, Patrick W. Stroman, Graeme N. Smith, Nils D. Forkert, Angelina Paolozza, Matthew T. Rätsep, B. Anne Croy:
   Neurological function in children born to preeclamptic and hypertensive mothers - A systematic review
   Pregnancy Hypertension, 10, 1-6, 2017.
    
6. Fabian Flottmann, Gabriel Broocks, Tobias D. Faizy, Marielle Ernst, Nils D. Forkert, Malte Grosser, Götz Thomalla, Susanne Siemonsen, Jens Fiehler, Andre Kemmling:
   CT-perfusion stroke imaging: a threshold free probabilistic approach to predict infarct volume compared to traditional ischemic thresholds
   Scientific Reports, 7, 6679, 2017.
    
7. Enedino Hernández-Torres, Nora Kassner, Nils D. Forkert, Luxi Wei, Vanessa Wiggermann, Madeleine Daemen, Lindsay Machan, Anthony Traboulsee, David Li, Alexander Rauscher:
   Anisotropic cerebral vascular architecture causes orientation dependency in cerebral blood flow and volume measured with dynamic susceptibility contrast MRI
   Journal of Cerebral Blood Flow and Metabolism, 37(3), 1108-1119, 2017.
    
8. Mao Li, Joanne B. Cole, Mange Manyama, Jacinda Larson, Denise K. Liberton, Sheri L. Riccardi, Tracey M. Ferrara, Stephanie A. Santorico, Jordan J. Bannister, Nils D. Forkert, Richard A. Spritz, Washington Mio, Benedikt Hallgrimsson:
   Rapid automated landmarking for morphometric analysis of three dimensional facial scans
   Journal of Anatomy, 230(4), 607-618, 2017.
    
9. Matthew D. Li, Nils D. Forkert, Palak Kundu, Cheryl Ambler, Robert M. Lober, Terry C. Burns, Patrick D. Barnes, Iris C. Gibbs, Gerald A. Grant, Paul G. Fisher, Samuel H. Cheshier, Cynthia J. Campen, Michelle Monje, Kristen W. Yeom:
   Brain perfusion and diffusion abnormalities in children treated for posterior fossa brain tumors
   Journal of Pediatrics, 185, 173-180, 2017.
    
10. Peter Ludewig, Nadine Gdaniec, Jan Sedlacik, Nils D. Forkert, Patryk Szwargulski, Matthias Graeser, Gerhard Adam, Michael G. Kaul, Kannan M. Krishnan, R. Matthew Ferguson, Amit P. Khandhar, Piotr Walczak, Jens Fiehler, Götz Thomalla, Christian Gerloff, Tobias Knopp, Tim Magnus:
    Magnetic particle imaging for real-time perfusion imaging in acute stroke
    ACS Nano, 11(10), 10480-10488, 2017.
     
11. Sarah J. MacEachern, Sabrina D´Alfonso, Roman J. McDonald, Nancy Thornton, Nils D. Forkert, Jeffrey R. Buchhalter:
    Most children with epilepsy experience postictal phenomena, often preventing a return to normal activities of childhood
    Pediatric Neurology, 72, 42-50, 2017.
     
12. Renzo Phellan, Nils D. Forkert:
    Comparison of vessel enhancement algorithms applied to Time-of-Flight MRA images for cerebrovascular segmentation
    Medical Physics, 44(11), 5901-5915, 2017.
     
13. Susanne Siemonsen, Nils D. Forkert, Martina Bernhardt, Götz Thomalla, Martin Bendszus, Jens Fiehler:
    ERic Acute StrokE Recanalization (ERASER): A study using predictive analytics to assess a new device for mechanical thrombectomy
    International Journal of Stroke, 12(6), 659-666, 2017.

Full-Length Proceedings Papers:

1. Renzo Phellan, Thomas Lindner, Alexandre X. Falcão, Nils D. Forkert:
   Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain
   In: Armato S.G., Petrick N.A. (eds), Computer-Aided Diagnosis, SPIE Medical Imaging 2017, Vol. 10134, 101341B1-9, Orlando, USA, 2017.
    
2. Renzo Phellan, Alan Peixinho, Alexandre X. Falcão, Nils D. Forkert:
   Vascular segmentation in TOF MRA images of the brain using a deep convolutional neural network
   In: Cardoso M. et al. (eds) Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS 2017, CVII 2017, STENT 2017. Lecture Notes in Computer Science, Vol. 10552, 39-46, 2017.
    
3. Jan Sedlacik, Andreas Frölich, Johanna Spallek, Nils D. Forkert, Franziska Werner, Tobias Knopp, Dieter Krause, Jens Fiehler, Jan-Hendrik Buhk:
   Detection of flow dynamic change in a 3D printed aneurysm model after treatment
   7th International Workshop on Magnetic Particle Imaging (IWMPI), Prague, Czech Republic, International Journal on Magnetic Particle Imaging, 3(1), 1703005, 2017.
    
4. Sahand Talai, Kai Boelmans, Jan Sedlacik, Nils D. Forkert:
   Automatic classification of patients with idiopathic Parkinson’s disease and progressive nuclear palsy using diffusion MRI datasets
   In: Armato S.G., Petrick N.A. (eds), Computer-Aided Diagnosis, SPIE Medical Imaging 2017, Vol. 10134, 101342H1-6, Orlando, USA, 2017.
    
5. Javier Villafruela, Sebastian Crites, Bastian Cheng, Christian Knaack, Götz Thomalla, Jens Fiehler, Bijoy Menon, Nils D. Forkert:
   Automatic classification of cardioembolic and arteriosclerotic ischemic strokes from apparent diffusion coefficient datasets using texture analysis and deep learning
   In: Armato S.G., Petrick N.A. (eds), Computer-Aided Diagnosis, SPIE Medical Imaging 2017, Vol. 10134, 101342K1-6, Orlando, USA, 2017.
    
6. René Werner, Daniel Schetelig, Thilo Sothmann, Eike Mücke, Matthias Wilms, Bastian Cheng, Nils D. Forkert:
   Low rank-&-sparse matrix decomposition as stroke segmentation prior: useful or not?
   In: Maier-Hein K.H., Deserno T.M., Handels H., Tolxdorff T. (eds.), Bildverarbeitung für die Medizin 2017, Heidelberg, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 161-166, 2017.
    
7. Anthony Winder, Susanne Siemonsen, Fabian Flottmann, Jens Fiehler, Nils D. Forkert:
   Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention
   In: Armato S.G., Petrick N.A. (eds), Computer-Aided Diagnosis, SPIE Medical Imaging 2017, Vol. 10134, 101344B1-6, Orlando, USA, 2017.

Abstracts:

1. Jordan J. Bannister, Jacinda R. Larson, Benedikt Hallgrimsson, Poay Hoon Lim, Richard A. Spritz, Ophir D. Klein, Francois P.J. Bernier, Nils D. Forkert:
   Registration and landmarking of polygonal mesh facial scans using a point feature based iterative point correspondence algorithmI
   International Computer Assisted Radiology and Surgery (CARS) Congress, Barcelona, Spain, International Journal of Computer Assisted Radiology and Surgery, 12, Supp. 1, S216-S218, 2017.
    
2. Bastian Cheng, Christian Knaack, Nils D. Forkert, Christian Gerloff, Götz Thomalla:
   Stroke subtype classification by geometrical descriptors of lesion shape
   European Stroke Organisation Conference 2017, Prague, Czech Republic, 2017.
    
3. A. Max Hamilton, Nils D. Forkert, Runze Yang, Ying Wu, James Rogers, V. Wee Yong, Jeff F. Dunn:
   Grey matter atrophy measured in-vivo with 9.4T MRI in the experimental autoimmune encephalomyelitis mouse model of multiple sclerosis
   25^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, USA, 2017, Proceedings of the International Society for Magnetic Resonance 25: 0188, 2017.
    
4. M. Ethan MacDonald, Rebecca. J. Williams, Nils D. Forkert, Avery Berman, Cheryl R. McCreary, Richard Frayne, G. Bruce Pike:
   Consistency of intra-database cortical thinning with age
   25^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Honolulu, USA, 2017, Proceedings of the International Society for Magnetic Resonance 25: 0604, 2017.
    
5. Yasmine Mackie, Nils D. Forkert, Stephanie Borgland:
   High sucrose diet predicts alterations in brain macrostructure
   25^th Annual Meeting of the Society for the Study of Ingestive Behavior, Montreal, Canada.
    
6. Jennifer Quon, Kristen W. Yeom, Gary Steinberg, Sarah J. MacEachern, Venkatesh Madguiri, Michael Edwards, Nils D. Forkert:
   Early apparent diffusion coefficient changes in normal-appearing brain in pediatric Moyamoya disease
   85^th American Association of Neurological Surgeons (AANS) Annual Scientific Meeting, Los Angeles, USA, Journal of Neurosurgery, 126(4), A1398, 2017.
    
7. Rani G. Sah, Christopher D. d’Esterre, Nils D. Forkert, Moiz Hafeez, Philip A. Barber:
   Diffusion-weighted imaging of early secondary lesion growth after endovascular therapy or thrombolysis in patients with ischemic stroke
   European Stroke Organisation Conference 2017, Prague, Czech Republic, 2017.
    
8. Susanne Siemonsen, Nils D. Forkert, Martina Bernhardt, Götz Thomalla, Martin Bendszus, Jens Fiehler:
   Eraser: A study using predictive analytics to assess mechanical thrombectomy
   International Stroke Conference 2017, Houston, USA, 2017.
    
9. Derek Yecies, Rogelio Esparza, Tej D. Azad, Jennifer Quon, Nils D. Forkert, Sarah J. MacEachern, Samuel Cheshier, Michael Edwards, Gerald Grant, Kristen Yeom:
   Long-term effects of posterior fossa surgery and local radiation on supratentorial brain development in ependymoma survivors
   Congress of Neurological Surgeons Annual Meeting 2017, Boston, USA, 2017.
    
10. Wilby Williamson, Nils D. Forkert, Adam J. Lewandowski, Renzo Phellan, Henry Boardman, Ashley Verburg, Odaro Huckstep, Thomas Okell, Charlie Foster, Paul Leeson:
    Cardiovascular risk in young adults and the effect on the cerebrovascular tree and cerebral perfusion
    American Heart Association's annual Scientific Sessions meeting 2017, Anaheim, USA, 2017.

Journal Papers:

1. Nils D. Forkert, Matthew D. Li, Robert Lober, Kristen Yeom:
   Gray matter growth is accompanied by increasing blood flow and decreasing apparent diffusion coefficient during childhood
   American Journal of Neuroradiology, 37(9), 1738-1744, 2016.
    
2. Ulrike Löbel, Nils D. Forkert, Peter Schmitt, Maria Schroeder, Tim Magnus, Stefan Kluge, Christina Weiler-Normann, Xiaoming Bi, Jens Fiehler,Jan Sedlacik:
   Cerebral hemodynamics in patients with hemolytic uremic syndrome assessed by susceptibility weighted imaging and four-dimensional non-contrast MR angiography
   PLOS ONE, 11(11), e0164863, 2016.
    
3. M. Ethan MacDonald, Nils D. Forkert, G. Bruce Pike, Richard Frayne:
   Phase error correction in time-averaged 3D phase contrast magnetic resonance imaging of the cerebral vasculature
   PLOS ONE, 11(2), e0149930, 2016.
    
4. Matthew T. Rätsep, Angelina Paolozza, Andrew Hickman, Brandon Maser, Vanessa R. Kay, Shuhiba Mohammad, Jessica Pudwell, Graeme N. Smith, Donald Brien, Patrick W. Stroman, Michael A. Adams, James N. Reynolds, B. Anne Croy, Nils D. Forkert:
   Brain structural and vascular anatomy is altered in offspring of preeclamptic pregnancies: A pilot study
   American Journal of Neuroradiology, 37(5), 939-945, 2016.
    
5. Jan Sedlacik, Andreas Frölich, Johanna Spallek, Nils D. Forkert, Tobias D. Faizy, Franziska Werner, Tobias Knopp, Dieter Krause, Jens Fiehler, Jan-Hendrik Buhk:
   Magnetic particle imaging for high temporal resolution assessment of aneurysm hemodynamics
   PLOS One, 11(8), e0160097, 2016.
    
6. Melissa Zavaglia, Nils D. Forkert, Bastian Cheng, Christian Gerloff, Götz Thomalla, Claus C. Hilgetag:
   Technical considerations of a game-theoretical approach for lesion symptom mapping
   BMC Neuroscience, 17(1), 40, 2016.

Full-Length Proceedings Papers:

1. Poay Hoon Lim, Jordan Bannister, Francois Bernier, Richard Spritz, Ophir Klein, Nick Mahasuwan, Sheri Riccardi, Jacinda Larson, David Aponte, Benedikt Hallgrimsson, Nils D. Forkert:
   A novel classification framework for detecting dysmorphologic syndromes using 3D facial topography
   Thirtieth Annual Conference on Neural Information Processing Systems (NIPS) Workshop on  Machine Learning For Healthcare (MLHC), Barcelona, Spain, 2016.
    
2. René Werner, Matthias Wilms, Bastian Cheng, Nils D. Forkert:
   Beyond cost function masking: RPCA-based non-linear registration in the context of VLSM
   6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), Trento, Italy, 2016.

Abstracts:

1. Bastian Cheng, Niko Schröder, Nils D. Forkert, Andre Kemmling, Jens Fiehler, Christian Gerloff, Götz Thomalla
   Susceptibility weighted imaging identifies tissue at risk of infarction in acute anterior circulation stroke
   European Stroke Organisation Conference 2016, Barcelona, Spain, 2016.
    
2. Christopher D. d'Esterre, Mari Boesen, Khayam Khan, Pooneh Pordeli, Seong Hwan Ahn, Mohamed Njam, Enrico Fainardi, Marta Rubiera, Michael D. Hill, Jennifer Mandzia, Andrew M. Demchuk, Tolulope T. Sajobi, Ting-Yim Lee, Nils D. Forkert, Mayank Goyal, Bijoy K. Menon:
   Optimal CT perfusion ischemic core thresholds from patients with CTP-to-TICI 2B/3 Reperfusion within 60 minutes
   European Stroke Organisation Conference 2016, Barcelona, Spain, 2016.
    
3. Christopher D. d'Esterre, Anurag Trivedi, Khayam Khan, Pooneh Pordeli, Seong Hwan Ahn, Mohamed Njam, Enrico Fainardi, Marta Rubiera, Jennifer Mandzia, Alexander Khaw, Michael D. Hill, Andrew M. Demchuk, Tolulope Sajobi, Nils D. Forkert, Mayank Goyal, Ting-Yim Lee, Bijoy K Menon:
   Multi-phase CTA and CT perfusion have equal predictive accuracies for follow-up infraction regionally
   European Stroke Organisation Conference 2016, Barcelona, Spain, 2016.
    
4. Jens Fiehler, Nils D. Forkert, Jan Buhk, Andre Kemmling, Götz Thomalla:
   Eric acute stroke recanalisation
   International Stroke Conference 2016, Los Angeles, USA, 2016.
    
5. Emmad Qazi, Alexis T. Wilson, Connor McDougall, Mari Boesen, Fahad S. Al-Ajlan, Pooneh Pordeli, Connor Batchelor, Khayam Khan, Tolulope T. Sajobi, Ting-Yim Lee, Michael D. Hill, Andrew M. Demchuk, Mayank Goyal, Christopher D. d'Esterre, Bijoy K. Menon, Nils D. Forkert:
   One threshold does not fit all: Hounsfield unit thresholds to segment clot on NCCT are patient specific
   International Stroke Conference 2016, Los Angeles, USA, 2016.
    
6. Rani Gupta Sah, Saad Khan, Ajay Mahajan_, Nils D. Forkert, Moiz Hafeez, Adrian Tsang,Christopher D. d’Esterre, Philip A. Barber:
   Rate of infarct growth in acute ischemic stroke is independent of apparent diffusion coefficient threshold
   International Stroke Conference 2016, Los Angeles, USA, 2016.
    
7. Rani Gupta Sah, Saad Khan, Ajay Mahajan_, Nils D. Forkert, Adrian Tsang, Moiz Hafeez, Sana Tariq, Christopher D. d’Esterre, Philip A. Barber:
   Assessing acute infarct growth and evolution using apparent diffusion coefficient and quantitative R2 imaging
   International Stroke Conference 2016, Los Angeles, USA, 2016.
    
8. Jan Sedlacik, Andreas Frölich, Johanna Spallek, Nils D. Forkert, Tobias D. Faizy, Franziska Werner, Tobias Knopp, Dieter Krause, Jens Fiehler, Jan-Hendrik Buhk:
   Flow dynamics in a 3D printed brain aneurysm model assessed by magnetic particle imaging, magnetic resonance imaging and dynamic subtraction angiography
   24^th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Singapore, 2016.
    
9. Javier Villafruela, Bijoy K. Menon, Nils D. Forkert:
   Automatic skull stripping in CT images based on k­means statistical classifier for radiotherapy planning
   Annual Meeting of the American Society for Radiation Oncology (ASTRO), Boston, 2016, International Journal of Radiation Oncology * Biology * Physics, 96(2S), E699, 2016.
   
   
   
   
    

Journal Papers:

1. Maxim Bester, Nils D. Forkert, Jan-Patrick Stellmann, Lilian Aly, Anna Drabik, Kim Lea Young, Christoph Heesen, Jens Fiehler, Susanne Siemonsen:
   Increased perfusion in normal appearing white matter in high inflammatory multiple sclerosis patients
   PLOS ONE, 10(3), e0119356, 2015.
    
2. Christopher D. d’Esterre, Mari Boesen, Seong Hwan Ahn, Pooneh Pordeli, Mohammed Najm, Priyanka Minhas, Paniz Davari, Enrico Fainardi, Marta Rubiera, Alexander V. Khaw, Andrea Zini, Richard Frayne, Michael D. Hill, Andrew M. Demchuk, Tolupe T. Sajobi, Nils D. Forkert, Mayank Goyal, Ting Y Lee, Bijoy K. Menon: 
   Time-dependent computed tomographic perfusion thresholds for patients with acute ischemic stroke
   Stroke, 46(12), 3390-3397, 2015.
    
3. Marielle Ernst, Nils D. Forkert, Laurin Brehmer, Götz Thomalla, Jens Fiehler, André Kemmling:
   Prediction of infarction and reperfusion in stroke by flow- and volume-weighted collateral signal in MR-angiography
   American Journal of Neuroradiology, 36(2), 275-282, 2015.
    
4. Nils D. Forkert:
   Model-Based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets
   it - Information Technology, 57(3), 208–212, 2015.
    
5. Nils D. Forkert, Tobias Verleger, Bastian Cheng, Götz Thomalla, Claus C. Hilgetag, Jens Fiehler:
   Multiclass support vector machine-based lesion mapping predicts functional outcome in ischemic stroke patients
   PLOS ONE, 10(6), e0129569, 2015.
    
6. André Kemmling, Fabian Flottmann, Nils D. Forkert, Jens Minnerup, Götz Thomalla, Walter Heindel, Bernd Eckert, Michael Knauth, Marios Psychogios, Sönke Langner, Jens Fiehler:
   Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization
   Journal of Cerebral Blood Flow & Metabolism, 35(9), 1397-1405, 2015.
    
7. Fabian Kording, Nils D. Forkert, Jan Sedlacik, Gerhard Adam, Kurt Hecher, Petra Arck, Chressen C. Remus:
   Automatic differentiation of placental perfusion compartments by time-to-peak  analysis in mice
   Placenta, 36(3), 255-261, 2015.
    
8. Oskar Maier, Christoph Schröder, Nils D. Forkert, Thomas Martinetz, Heinz Handels:
   Classifiers for ischemic stroke lesion segmentation: A comparison study
   PLOS ONE, 10(12), e0145118, 2015.
    
9. Melissa Zavaglia, Nils D. Forkert, Bastian Cheng, Christian Gerloff, Götz Thomalla, Claus C. Hilgetag:
   Mapping causal functional contributions derived from the clinical assessment of brain damage after stroke
   NeuroImage: Clinical, 9, 83-94, 2015.

Full-Length Proceedings Papers:

1. Nils D. Forkert:
   Blind estimation of the arterial input function from DSC perfusion-weighted MRI datasets of the brain
   Proceedings of the joint MICCAI-Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT), 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, 58-65, 2015.
    
2. Nils D. Forkert, Jens Fiehler:
   Effect of sample size on multi-parametric prediction of tissue outcome in acute ischemic stroke using a random forest classifier
   In: Gimi B, Molthen R.C. (eds), Biomedical Applications in Molecular, Structural, and Functional Imaging, SPIE Medical Imaging 2015, Vol. 9417, 94172H1-7, Orlando, USA, 2015.

Abstracts:

1. B. Anne Croy, Matthew T. Rätsep, Vanessa R. Kay, Rayana L. Leal, Bruno Zavan, Brandon Maser, Graeme N. Smith, James N. Reynolds, Angelina Paolozza, Patrick W. Stroman, John G. Sled, Christian Beaulieu, Dongming Zhou, Nils D. Forkert:
   Journeying from reproductive immunology to children’s brains
   35th Annual Meeting of the American Society for Reproductive Immunology, Kingston, Canada, 2015, American Journal of Reproductive Immunology, 73 (Suppl. 2): 27, 2015.
    
2. Philipp Kaesemann, Götz Thomalla, Bastian Cheng, Andras Trészl, Jens Fiehler, Nils D. Forkert:
   Influence of a severe internal carotid artery stenosis on diffusion and perfusion values in acute stroke patients
   23^rd Annual Meeting of the International Society for Magnetic Resonance in Medicine, Toronto, Canada, 2015, Proceedings of the International Society for Magnetic Resonance 23: 3572, 2015.
    
3. M. Ethan MacDonald, Nils D. Forkert, G. Bruce Pike, Richard Frayne:
   The impact of phase errors on mapping the flow of the cerebral vasculature with phase contrast MRI
   21^st Annual Meeting of the Organization for Human Brain Mapping, Honolulu, USA, 2015.
    
4. Matthew T. Rätsep, Vanessa R. Kay, Rayana Luna, Bruno Zavan, Andrew Hickman, Brandon Maser, Graeme N. Smith, James N. Reynolds, Angelina Paolozza, Patrick W.  Stroman, John G. Sled, Jacob Ellegood, Christian Beaulieu, Dongming Zhou, Nils D. Forkert, Michael A. Adams, B. Anne Croy:
   Impact of placental growth factor and preeclampsia on brain development, cognition, and behavior
   International Federation of Placenta Associations Conference, Brisbane, Australia, Placenta 36(9): A37, 2015.
   
   
   
   
   
   
    

Abstracts:

1. Chressen C. Remus, Nils D. Forkert, Jan Sedlacik, Gerhard Adam, Petra Arck, Fabian Kording:
   Automatic differentiation of functional placental compartments for perfusion analysis in the mouse model using the time-to-peak model
   Annual Meeting of the Radiological Society of North America (RSNA), Chicago, USA, 2014.
    
2. Chressen C. Remus, Fabian Kording, Nils D. Forkert, Jan Sedlacik, Emilia Solano, Gerhard Adam, Petra Arck:
   DCE MRI of the placenta reveals alterations of placenta perfusion after a stress challenge during pregnancy in mice
   Annual Meeting of the Radiological Society of North America (RSNA), Chicago, USA, 2014.