Marta Bianciardi

Assistant Professor

Brainstem Imaging Laboratory (Director)
Magnetic Resonance Physics and Instrumentation Group (Faculty member)

martab@mgh.harvard.edu
617-724-1723

Marta Bianciardi, PhD, joined the faculty at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) with the goal of developing an in vivo neuroimaging-based atlas and connectome of brainstem nuclei by 7 Tesla MRI, to enhance our knowledge and the quality of patient care in disorders of consciousness, sleep disorders, vestibular disorders and movement disorders (such as Parkinson’s disease). Her work also focuses on developing novel biomarkers of cerebrovascular and brain parenchymal compliance in response to physiological and neuronal pulsatile effects.

Education

PhD in Biophysics, University “La Sapienza,” Rome, Italy

Select Publications

View Dr. Bianciardi’s full PubMed publications list here.

Highlights

2016: K01 NIH NIBIB Research Scientist Career Development Award

2017: MGH Claflin Distinguished Scholar Award

2019: Mind Brain Behavior Harvard Faculty Award

Websites

Brainstem Imaging Laboratory
Magnetic Resonance Physics and Instrumentation Group

Iris Yuwen Zhou

Assistant Professor

The Caravan Lab

iris.zhou@mgh.harvard.edu

Dr. Iris Yuwen Zhou is an Assistant Professor of Radiology at Massachusetts General Hospital and Harvard Medical School and a faculty member in the Athinoula A. Martinos Center for Biomedical Imaging. She has a background in both electronic engineering and biomedical engineering, with specific training in research areas for magnetic resonance imaging (MRI). Her work focuses on developing novel MRI methodologies that quantify molecular, metabolic, functional, and structural alterations for stroke, fibrosis, and tumor imaging. Her research spans tissue characterization using molecularly targeted probes, chemical exchange saturation transfer (CEST), diffusion, functional MRI in animal models and clinical translation. More recently, her research topics also include the integration of multi-parametric MRI data with information from other imaging modalities, such as positron emission tomography (PET), through leveraging advances in multi-modal imaging, chemistry, data modeling, and image analysis. Her research is currently funded by an NIH-NHLBI K25 Grant. Her overall goal is to develop novel imaging approaches that quantify structural, physiological, and functional abnormalities in lung and liver diseases for improved diagnosis, and to deploy these techniques in clinical trials to accelerate the development of new therapeutics.

Education

PhD in Biomedical Engineering, The University of Hong Kong

Select Publications

1. Zhou IY, Ramsay IA, Ay I, Pantazopoulos P, Rotile NJ, Wong A, Caravan P, Gale EM. (2021) Positron Emission Tomography-Magnetic Resonance Imaging Pharmacokinetics, In Vivo Biodistribution, and Whole-Body Elimination of Mn-PyC3A. Invest Radiol 56(4):261-70.

2. Zhou IY, Clavijo Jordan V, Rotile NJ, Akam E, Krishnan S, Arora G, Krishnan H, Slattery H, Warner N, Mercaldo N, Farrar CT, Wellen J, Martinez R, Schlerman F, Tanabe KK, Fuchs BC, Caravan P. Advanced MRI of Liver Fibrosis and Treatment Response in a Rat Model of Nonalcoholic Steatohepatitis. Radiology. 2020 Apr 28;296(1):67-75

3. Zhou IY, Wang E, Cheung JS, Zhang X, Fulci G, Sun PZ. Quantitative chemical exchange saturation transfer (CEST) MRI of glioma using Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting. Sci Rep. 2017 Mar 7;7(1):84.

4. Zhou IY, Liang YX, Chan RW, Gao PP, Cheng JS, Hu Y, So KF, Wu EX. Brain resting-state functional MRI connectivity: morphological foundation and plasticity. Neuroimage. 2014 Jan 1;84:1-10.

Highlights

Young Investigator Award, CMITT, SNMMI 2020
K25 Mentored Quantitative Research Career Development Award, NIH/NHLBI
Over 50 peer-reviewed journal articles, 3 issued patents

Summa Cum Laude Merit Award, ISMRM-ESMRMB 2014

Website

The Caravan Lab

Robert Frost

Assistant Professor

Laboratory for Computational Neuroimaging

srfrost@mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Clinical, Diffusion MRI, fMRI, FreeSurfer, High-field Imaging, MRI, Software Development

Dr. Robert Frost’s research focuses on improving the quality and efficiency of brain MRI through modification of the acquisition and image reconstruction. He has developed methods to accelerate the acquisition of high-resolution diffusion MRI and has used real-time feedback techniques that adapt to head movements during scans to maintain high image quality.

Head motion during MRI of the brain is widely recognized as a major problem in both clinical practice and neuroimaging research that radically reduces the value of the soft tissue contrasts accessible with MRI. When a single image is encoded over several minutes, small movements can easily cause artifacts that hinder clinical diagnoses and increase bias and variance in brain imaging research. Dr. Frost’s recent work with André van der Kouwe and his group focuses on prospective correction for head motion. This approach adjusts the image encoding in real-time to compensate for head movement, so that high-quality images are available immediately after the scan.

Education

PhD, MRI Physics, University of Oxford

Select Publications

1. Frost R, Wighton P, Karahanoğlu FI, Robertson RL, Grant PE, Fischl B, Tisdall MD, van der Kouwe A. Markerless high-frequency prospective motion correction for neuroanatomical MRI. Magn Reson Med. 2019 Jul;82(1):126-144.

2. Frost R, Hess AT, Okell TW, Chappell MA, Tisdall MD, van der Kouwe AJ, Jezzard P. Prospective motion correction and selective reacquisition using volumetric navigators for vessel-encoded arterial spin labeling dynamic angiography. Magn Reson Med. 2016 Nov;76(5):1420-1430.

3. Frost R, Miller KL, Tijssen RH, Porter DA, Jezzard P. 3D multi-slab diffusion-weighted readout-segmented EPI with real-time cardiac-reordered K-space acquisition. Magn Reson Med. 2014 Dec;72(6):1565-79.

Highlights

2015: Partial Fourier and simultaneous multi-slice (SMS) extensions of readout-segmented EPI from PhD research have been distributed by Siemens

2018: Invited ISMRM Educational Talk: “Image Encoding for Diffusion MRI,” ISMRM

2021: NIBIB R21 Trailblazer Award

Associated Lab

Laboratory for Computational Neuroimaging

Martin Reuter

Assistant Professor

Laboratory for Computational Neuroscience (LCN)

mreuter@nmr.mgh.harvard.edu

Tagged:Aging, Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Data Science, Diffusion MRI, Eyes, FreeSurfer, Machine Learning, Magnetic Resonance Imaging, MRI, Optical Imaging, Optics, Preclinical, Software Development

Dr. Martin Reuter is an Assistant Professor of Radiology and of Neurology at Harvard Medical School and the Massachusetts General Hospital (Assistant in Neuroscience, Dept. of Radiology and Dept. of Neurology). He is affiliated with the Martinos Center for Biomedical Imaging and the German Center for Neurodegenerative Diseases. Dr. Reuter’s recent research on artificial intelligence in medical imaging focuses on deep learning-based method development for the automated analysis of human brain MRI to aid computer-aided diagnosis and prognosis of neurodegenerative disease. Many of his methods are widely employed as part of the FreeSurfer software package to study disease or assess disease modifying therapies, e.g., by the Alzheimer’s Disease Neuroimaging Initiative, the Rhineland Study, and other large cohort studies around the world.

During his postdoctoral research at MIT (2006-08), supported by a Feodor-Lynen fellowship of the Alexander von Humboldt Foundation, Dr. Reuter contributed novel methods for non-rigid shape analysis and processing, and received the 2009 most cited paper award of the Computer-Aided Design journal for his manuscript on spectral shape analysis. In 2006, he was awarded the Leibniz prize for outstanding scientific accomplishments by the University of Hanover, Germany, where he obtained his PhD in the area of computational and differential geometry from the Department of Electrical Engineering and Computer Science with summa cum laude in 2005. He obtained a ‘Diplom’ (MSc) in mathematics with a second major in computer science and a minor in business informatics from the Leibniz University of Hanover in 2001. His research interests include medical AI, computational neuroimaging, computational geometry and topology, computer and biomedical vision, computer-aided design, geometric modeling and computer graphics.

Education

PhD in Computational and Differential Geometry, University of Hanover, Germany

Select Publications

1. Wachinger C, Salat DH, Weiner M, Reuter M; Alzheimer’s Disease Neuroimaging Initiative. Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala. Brain. 2016 Dec;139(Pt 12):3253-3266.

2. Wachinger C, Golland P, Kremen W, Fischl B, Reuter M; Alzheimer’s Disease Neuroimaging Initiative. BrainPrint: a discriminative characterization of brain morphology. Neuroimage. 2015 Apr 1;109:232-48.

3. Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012 Jul 16;61(4):1402-18.

Highlights

2014 NIH K25 Career Award

2009 Most Cited Paper Award for “ShapeDNA”

2006 Feodor-Lynen Fellowship of the Alexander von Humboldt Foundation

2006 Research Prize of Leibniz University, Hanover, Germany

Website

Laboratory for Computational Neuroscience

Karl Helmer

Assistant Professor

helmer@nmr.mgh.harvard.edu
617-726-8636

Tagged:Algorithm Development, Brain, Clinical, Data Management Infrastructure Developmnet, Data Science, Diffusion MRI, fMRI, Functional Magnetic Resonance Imaging, Imaging Informatics, Magnetic Resonance Imaging, MRI, Software Development

Karl Helmer, PhD, maintains research interests in several areas: (1) the creation of data management and sharing infrastructure; (2) the development of imaging protocols for multi-site MR imaging-based studies; (3) the creation of quality control and assessment software for MRI data; and (4) development of terminologies/ontologies to describe neuroscience experiments and semantic-web-based tools to annotate data. He also has an ongoing research project to study the neural underpinnings of musical improvisation and expert performance.

Dr. Helmer is currently chiefly active in the field of biomedical informatics and is currently directing the data management efforts for multiple projects. He is currently the Director of the Data Core for the MarkVCID project. His role on this project is twofold: (1) design, contribute to and direct the building of a data management infrastructure for the project; and (2) work with project investigators to create harmonized imaging protocols for each biomarker that can be used across the sites involved in this consortium. In addition, he is designing, directing the development of, and contributing to a data-sharing system called Entrepôt™. This system is able to collect data from multiple sites, curate it, and make it available to users for reuse. It will mainly be used for medium-to-small projects, such as Pharma-sponsored drug trials. The system is currently being built to house the data from three funded projects. He and his team also investigate the use of Natural Language Processing to extract information from supporting material to better characterize and understand primary data.

Education

PhD in Physics, University of Rochester

Select Publications

1. Helmer KG, Dardzinski BJ, Sotak CH. The application of porous-media theory to the investigation of time-dependent diffusion in in vivo systems. NMR Biomed. 1995 Nov-Dec;8(7-8):297-306.

2. Helmer KG, Chou MC, Preciado RI, Gimi B, Rollins NK, Song A, Turner J, Mori S. Multi-site Study of Diffusion Metric Variability: Characterizing the Effects of Site, Vendor, Field Strength, and Echo Time using the Histogram Distance. Proc SPIE Int Soc Opt Eng. 2016 Feb 27;9788. pii: 97881G.

3. Warner GC, Helmer KG. Characterization of Diffusion Metric Map Similarity in Data From a Clinical Data Repository Using Histogram Distances. Front Neurosci. 2018 Mar 8;12:133.

Eric Gale

Assistant Professor

egale@mgh.harvard.edu

Tagged:Brain, Cardiovascular Disease / Disorders, Cardiovascular System, Chemistry, High-field Imaging, Liver, Low-field Imaging, Molecular Imaging, MRI, Multimodal Imaging, Preclinical, Probe Development

Dr. Gale applies chemistry to solve unmet challenges in radiology and biomedical imaging.

A major area of current focus is developing magnetic resonance imaging probes to non-invasively map and quantify pathologic change at the molecular level. His group is capitalizing on the transition metal properties of manganese and iron to rationally design molecules that change paramagnetic properties, and thus modulate MRI signal, in response to biochemical stimuli. Dr. Gale and his collaborators are applying his probes to interrogate the roles of oxidative stress and inflammation in the pathogenesis of disease states affecting abdominal organs. His team is currently developing new chemistry that will enable non-invasive quantification of aberrant metal ion flux and enzyme activity associated with inflammation and cancer.

Dr. Gale’s research has also demonstrated how rationally designed complexes of manganese can offer a viable and directly interchangeable alternative to gadolinium-based MRI contrast agents, which are indispensable to modern radiology but have come under increased regulatory scrutiny over concerns of gadolinium retention and delayed toxicity.

Education

PhD in Chemistry, University of Georgia

Select Publications

1. Wang H, Jordan VC, Ramsay IA, Sojoodi M, Fuchs BC, Tanabe KK, Caravan P, Gale EM. Molecular Magnetic Resonance Imaging Using a Redox-Active Iron Complex. J Am Chem Soc. 2019 Apr 10;141(14):5916-5925.

2. Wang J, Wang H, Ramsay IA, Erstad DJ, Fuchs BC, Tanabe KK, Caravan P, Gale EM. Manganese-Based Contrast Agents for Magnetic Resonance Imaging of Liver Tumors: Structure-Activity Relationships and Lead Candidate Evaluation. J Med Chem. 2018 Oct 11;61(19):8811-8824.

3. Gale EM, Atanasova IP, Blasi F, Ay I, Caravan P. A Manganese Alternative to Gadolinium for MRI Contrast. J Am Chem Soc. 2015 Dec 16;137(49):15548-57.

Highlights

2019: Named “One to Watch” by the Society for Nuclear Medicine and Molecular
Imaging

2017: Manganese-based MRI contrast agent may be safer alternative to gadolinium-based agents

Websites

The Caravan Lab

Farrar Research Group

Meher Juttukonda

Assistant Professor

Cerebrovascular Aging and Spin Labeling (CASL) Laboratory

mjuttukonda@mgh.harvard.edu

Tagged:Aging, Algorithm Development, Alzheimer's Disease, Brain, Cardiovascular System, fMRI, Functional Magnetic Resonance Imaging, High-field Imaging, Magnetic Resonance Imaging, MRI, PET-MRI, Stroke

Dr. Juttukonda’s research interests are in studying the balance between hemodynamic and metabolic function in the human brain as well as in the translation of these methods for characterizing microvascular health in cerebrovascular diseases. A principal objective of his work has been to develop quantitative MRI approaches, including arterial spin labeling (ASL)-based methods, for better characterizing cerebral hemodynamic and metabolic function. A central focus is the application of these methods for studying imaging-based biomarkers of impairment due to cerebrovascular diseases as well as markers of response to disease-modifying therapies, including as part of longitudinal human trials. Current work in the group involves elucidating the impact of aging on the microvasculature in the brain, particularly in the white matter, and how cerebrovascular health may portend risk for accumulating white matter damage that contributes to the burden of cognitive decline in aging and Alzheimer’s disease (AD).

Education

PhD in Biomedical Engineering, The University of North Carolina at Chapel Hill

Select Publications

1. Juttukonda MR, Stephens KA, Yen YF, Howard CM, Polimeni JR, Rosen BR, Salat DH. Oxygen extraction efficiency and white matter lesion burden in older adults exhibiting radiological evidence of capillary shunting. J Cereb Blood Flow Metab. 2022 Jun 8;271678X221105986. PMID: 35673981; Online before print.

2. Juttukonda MR, Li B, Almaktoum R, Stephens KA, Yochim KM, Yacoub E, Buckner RL, Salat DH. Characterizing cerebral hemodynamics across the adult lifespan with arterial spin labeling MRI data from the Human Connectome Project-Aging. Neuroimage. 2021 Apr 15;230:117807. PMID: 33524575.

3. Juttukonda MR, Davis LT, Lants SK, Waddle SL, Lee CA, Patel NJ, Jordan LC, Donahue MJ. A Prospective, Longitudinal Magnetic Resonance Imaging Evaluation of Cerebrovascular Reactivity and Infarct Development in Patients With Intracranial Stenosis. J Magn Reson Imaging. 2021
Sep;54(3):912-922. PMID: 33763922..

Highlights

2021: Mentored Research Scientist Development (K01) Award, National Institutes of Health / National Institute on Aging

2019: American Heart Association Career Development Award

Website

Cerebrovascular Aging and Spin Labeling (CASL) Laboratory

Nicole Zurcher Wimmer

Assistant Professor

Nicole Zürcher Wimmer

Zürcher Lab (Principal Investigator)
Chemical Neuroscience Program (Director of Human Imaging)

nzurcherwimmer@mgh.harvard.edu

Nicole Zürcher, PhD, is Assistant Professor of Radiology at Harvard Medical School and Massachusetts General Hospital, a faculty member at the A. A. Martinos Center for Biomedical Imaging and the Director of Human Imaging in the Chemical Neuroscience Program. She is Faculty at the MGH Lurie Center for Autism and Lurie Center for Autism Director of Neuroimaging. With a background in neuroscience and imaging, she is an expert in simultaneous positron emission tomography-magnetic resonance imaging. The major focus of her work is on autism spectrum disorder and socio-emotional behavior.

Education

PhD in Neuroscience, Brain Mind Institute, Ecole Polytechnique Federale de Lausanne (EPFL)

Select Publications

1. Tseng CJ, Canales C, Marcus RE, Parmar AJ, Hightower BG, Mullett JE, Makary MM, Tassone AU, Saro HK, Townsend PH, Birtwell K, Nowinski L, Thom RP, Palumbo ML, Keary C, Catana C, McDougle CJ, Hooker JM, Zürcher NR. In vivo translocator protein in females with autism spectrum disorder: a pilot study. Neuropsychopharmacology. 2024. PMID: 38615126.

2. Tseng CJ, Mc Dougle CJ, Hooker JM, Zürcher NR. Epigenetics of autism spectrum disorder: histone deacetylases. Biological Psychiatry. 2021. PMID 35120709.

3. Zürcher NR, Loggia ML, Mullett JE, Tseng CJ, Bhanot A, Richey L, Hightower BG, Wu C, Parmar AJ, Butterfield RI, Dubois JM, Chonde DB, Izquierdo-Garcia D, Wey HY, Catana C, Hadjikhani N, McDougle CJ, Hooker JM. [11C]PBR28 MR-PET imaging reveals lower regional brain expression of translocator protein (TSPO) in young adult males with autism spectrum disorder. Molecular Psychiatry. 2021. PMID: 32076115

4. Gilbert TM*, Zürcher NR*, Catanese MC, Tseng CJ, Di Biase MA, Lyall AE, Hightower BG, Parmar AJ, Bhanot A, Wu CJ, Hibert ML, Kim M, Mahmood U, Stufflebeam SM, Schroeder FA, Wang C, Roffman JL, Holt DJ, Greve DN, Pasternak O, Kubicki M, Wey HY, Hooker JM. Neuroepigenetic signatures of age and sex in the living human brain. Nature Communications. 2019. PMID: 31270332.

Highlights

2025: Rising Mentor Award, Massachusetts General Hospital
2022: R01 from the National Institute of Mental Health
2022: Career Development Award, Autism Research Program, Department of Defense
2021: Travel Award, American College of Neuropsychopharmacology

Website

Chemical Neuroscience Program

Jason Stockmann

Assistant Professor

Magnetic Resonance Physics & Instrumentation Group
Low-field MRI and Hyperpolarized Media Laboratory

jstockmann@mgh.harvard.edu
315-450-3309

Tagged:Brain, Brain Mapping, Diffusion MRI, fMRI, Functional Magnetic Resonance Imaging, Hardware Development, High-field Imaging, Low-field Imaging, Magnetic Resonance Imaging, MRI, Probe Development, RF Coil Development, Spectroscopy, Spine

Jason Stockmann, PhD, is broadly interested in magnetic resonance imaging hardware and acquisition methods for improving data quality for both structural and functional imaging. He has worked on diverse MRI scanners ranging in field strength by two orders of magnitude, from low-field (80 mT) to ultra-high field (7 Tesla). He specializes in developing synergistic combinations of hardware, pulse sequences, and image reconstruction algorithms that address unmet needs in MRI research, especially for diffusion and functional brain imaging with echo planar imaging (EPI) acquisitions. The major thrust of this work has been to develop multi-coil (MC) shim arrays and associated amplifier hardware and optimization methods to improve magnetic field homogeneity inside the body, thus reducing image distortions and other artifacts. More recently, he and colleagues have applied MC arrays to perform dynamic local field control, creating tailored nonlinear field offsets for (1) improving lipid suppression in spectroscopy and (2) selectively exciting and imaging target anatomy with increased efficiency. He is currently supported by a K99/R00 Pathway to Independence Grant to implement real-time multi-coil shimming to improve EPI data quality in the deep brain and brainstem, where functional connectivity MRI (fcMRI) holds the potential to shed light on the role that deep brain nuclei play in modulating arousal, pain and sleep.

Dr. Stockmann is also interested in low-field, portable MRI for point-of-care brain imaging. He has contributed to Dr. Lawrence Wald and Dr. Clarissa Cooley’s program to build a lightweight prototype brain scanner based on a Halbach array of permanent magnets (80mT main magnetic field). His primary role in this project has been to design pulse sequences and RF pulses that are robust to extreme field inhomogeneity. He has also helped build a generalized reconstruction framework that incorporates the full signal forward model including field nonlinearity. In parallel with this work, he has developed an interest in open-source hardware for MRI research and education. To this end, he contributed to a team effort by Dr. Wald’s group to build 20 tabletop MRI scanners (0.2 Tesla) for an undergraduate engineering lab course at MIT, at a cost of less than $10K per scanner.

He is strongly committed to open-source science and reproducible research across sites. All of his hardware designs and software codes are available online at the sites listed below. Materials not posted to these sites are freely available upon request.

Radio Frequency Laboratory of the Wald Group at MGH
Tabletop MRI Scanner Wiki
Open Source Imaging

Education

PhD in Biomedical Engineering, Yale University

Select Publications

1. Stockmann JP, Witzel T, Keil B, Polimeni JR, Mareyam A, LaPierre C, Setsompop K, Wald LL. A 32-channel combined RF and B0 shim array for 3T brain imaging. Magn Reson Med. 2016 Jan;75(1):441-51.

2. Stockmann JP, Galiana G, Tam L, Juchem C, Nixon TW, Constable RT. In vivo O-Space imaging with a dedicated 12 cm Z2 insert coil on a human 3T scanner using phase map calibration. Magn Reson Med. 2013 Feb;69(2):444-55.

3. Stockmann JP, Wald LL. In vivo B(0) field shimming methods for MRI at 7T. Neuroimage. 2018 Mar;168:71-87.

Highlights

Int. Soc. Magnetic Resonance in Medicine, Young Investigator Award Finalist (2012)

United States Patent #10,261,145: System and method for improved radio-frequency detection or B0 field shimming in magnetic resonance imaging (US 20150323628 A1)

United States Patent #8,710,839: O-space imaging: highly efficient parallel imaging using complementary nonlinear encoding gradient fields and receive coil geometries (US 20110241675 A1)

Websites

Magnetic Resonance Physics & Instrumentation Group
Low-field MRI and Hyperpolarized Media Laboratory

Shahin Nasr

Assistant Professor

shahin@nmr.mgh.harvard.edu

Tagged:Brain, Brain Mapping, Clinical, fMRI, FreeSurfer, High-field Imaging, MRI, Preclinical, Vision

For the past 15 years, Shahin Nasr, PhD, has studied the visual system of humans and non-human primates using a variety of techniques, from psychophysics and event-related potentials (ERP) to conventional and high-resolution functional MRI, in order to understand the neural mechanisms underlying visual perception. His studies are focused around the idea that the visual system is a reflection of its surrounding environment. He believes that it is impossible to fully understand the neural mechanisms underlying visual perception without consideration of the statistics of natural scenes.

A large portion of his efforts are focused on studying neural mechanisms underlying scene and face perception. He and his colleagues have revealed the first evidence for homology between scene processing networks in humans and non-human primates. They have also shown the selective encoding of those features that are frequently found in natural and man-made scenes, such as rectilinear angles and lines in cardinal orientations, in scene-selective areas.

Currently, using high-resolution fMRI collected within an ultrahigh-field 7T scanner, he is studying the fine-scale organization of visual areas (in column and cluster levels), to clarify how different visual features (e.g. orientation, angle, depth and spatial frequency) are encoded within different visual areas. In collaboration with neurologists and ophthalmologists at Massachusetts General Hospital and Boston Children’s Hospital, he is assessing how neurodegenerative and developmental disorders impact the fine- and large-scale functional organization of visual cortical regions.

Education

PhD in Neuroscience, School of Cognitive Sciences, IPM, Tehran, Iran

Select Publications

1. Nasr S, Tootell RBH. Asymmetries in Global Perception Are Represented in Near- versus Far-Preferring Clusters in Human Visual Cortex. J Neurosci. 2020;40(2):355–368.

2. Nasr S, Polimeni JR, Tootell RB. Interdigitated Color- and Disparity-Selective Columns within Human Visual Cortical Areas V2 and V3. J Neurosci. 2016;36(6):1841–1857.

3. Nasr S, Echavarria CE, Tootell RB. Thinking outside the box: rectilinear shapes selectively activate scene-selective cortex. J Neurosci. 2014;34(20):6721–6735.

Highlights

2020: Nominated By Harvard Medical School for Disney Award for Amblyopia Research

Website

Mesovision Laboratory

Kestas Kveraga

Assistant Professor

Vision Neurodynamics Lab (Director)

KKVERAGA@mgh.harvard.edu

Tagged:Aging, Autism, Brain, Brain Mapping, Diffusion MRI, EEG, Electroencephalography, fMRI, Functional Magnetic Resonance Imaging, Magnetic Resonance Imaging, Magnetoencephalography, MEG, MRI, Preclinical, Ultrahigh-field

Dr. Kveraga is a cognitive neuroscientist who studies the neural mechanisms of threat perception from naturalistic stimuli, with strong interests in visual pathway function and autism. He is also interested in neural aesthetics and how brain activity can be employed to predict and shape architectural design and art. He has expertise in neuroimaging methods, such as structural and functional MRI (including ultra-high-field high resolution 7T fMRI), MEG and EEG, psychophysical techniques (eye and limb tracking, visual pathway biasing), and in brain connectivity analyses (e.g., Dynamic Causal Modeling and biomagnetic phase synchrony).

Dr. Kveraga leads a research program as PI of NIH K01 and R01 grants to elucidate the neurodynamics of threat perception. Given that the brain is tasked with ensuring survival, a fundamental function of the brain predicting proximal events in environment and potential impact on the organism. This requires real-time inferences about the current environment from external sensory inputs, accomplished by combining incoming information with stimulus associations in memory and interoceptive signals that convey the current state of the perceiver. As this process is very complex, Dr. Kveraga’s focus has been initially on understanding what neural processes are involved in distinguishing different types of threats and non-threats from naturalistic visual stimuli. He has tested neural and behavioral responses with several types and combinations of threat cues embedded in visual stimuli – facial expression and identity, eye gaze direction, body posture and positioning of weapons and predatory animals. To better understand how the distinct visual channels (magnocellular, parvocellular, koniocellular) contribute to this process, his group developed methods to bias threat stimuli towards these visual pathways. He has also been exploring how perceivers’ anxiety levels, sex and age influence threat perception.

Education

PhD in Electrical Engineering, University of Minnesota

Select Publications

1. Kveraga K, Boshyan J, Bar M. Magnocellular projections as the trigger of top-down facilitation in recognition. J Neurosci. 2007 Nov 28;27(48):13232-40.

2. Kveraga K, Ghuman AS, Kassam KS, Aminoff EA, Hämäläinen MS, Chaumon M, Bar M. Early onset of neural synchronization in the contextual associations network. Proc Natl Acad Sci U S A. 2011 Feb 22;108(8):3389-94.

3. Kveraga K, Boshyan J, Adams RB Jr, Mote J, Betz N, Ward N, Hadjikhani N, Bar M, Barrett LF. If it bleeds, it leads: separating threat from mere negativity. Soc Cogn Affect Neurosci. 2015 Jan;10(1):28-35.

Highlights

2014: Editor of a book on scene perception (with Prof. Moshe Bar), “Scene Vision” by MIT Press.

1999: Fellow, The McDonnell Institute in Cognitive Neuroscience

1999: Fellow, Marine Biological Laboratory Summer School in Neuroinformatics

Bastien Guerin

Assistant Professor

Magnetic Resonance Physics and Instrumentation Group (MR-PIG)

guerin@nmr.mgh.harvard.edu

Tagged:Algorithm Development, Brain, Brain Mapping, Disorders-of-consciousness, Electromagnetic Modeling, fMRI, Functional Magnetic Resonance Imaging, Hardware Development, High-field Imaging, Magnetic Resonance Imaging, Movement Disorders, MR-PET, MRI, PET, Positron Emission Tomography, Psychiatric Disorders, RF Coil Development, RF Pulse Design

Dr. Guerin’s research focuses on MRI (and to some extent PET) technology development and translation to neuro-imaging to help better understand the human brain. He has several areas of specialization:

(i) Modeling and optimization of radio-frequency (RF) and gradient MR sub-systems. Dr. Geurin’s postdoctoral work focused on modeling of RF coils, especially parallel transmission (pTx) coils whereby nuclear spins are excited using several transmit channels as opposed to a single one (transmit phased array). He also developed methodology for the design of pTx RF pulses at low and large flip-angle with explicit control for the specific absorption rate, which is the main safety limitation in high duty-cycle MRI sequences. This work is relevant at 3 Tesla, 7 Tesla and will be crucial to the progress of MRI at 11.7 Tesla and higher. Recently, He has been working together with Prof. Lawrence Wald and Mathias Davids on a modeling and design methodology of gradient coils with an intrinsically lower propensity to create peripheral nerve stimulation (PNS). PNS is the main limitation of MRI acquisition speed in fast imaging sequences such as echo planar imaging and turbo spin echo, therefore such new gradient technology has the potential to dramatically improve speed and data acquisition rate in many clinical and research (fMRI, diffusion imaging) applications.

(ii) Neuro-imaging of patients with deep brain stimulation (DBS) implants. Generally speaking, Dr. Guerin is interested in the combined use of neurostimulation strategies and functional imaging methods such as fMRI. Neurostimulation using DBS allows pin-point excitation of the human brain in a very controlled manner and, when combined with fMRI, offers unique brain mapping possibilities. DBS is in itself a fascinating therapy that is poorly understood and holds promise for the treatment of many psychiatric disorders. He uses fMRI to study the mechanisms of action of DBS in patients with
Parkinson’s disease and depression. In addition, he studies the safety aspects of using DBS in the MR RF environment using electromagnetic modeling.

(iii) Iterative reconstruction, quantitative corrections and physics of positron emission tomography (PET). His interest in this area stems from his PhD work on fully 3D, list-mode reconstruction of PET data with corrections for scatter coincidences and non-rigid respiratory and cardiac motion. A central innovation of his work was to incorporate the information carried by the energy of the detected PET photons into the scatter correction process using a statistical model of the coincidence energy blurring incorporated in the likelihood function of the list-mode PET data. Another innovation was the use of tagged-MRI to track complex non-rigid motions due to respiration and cardiac beating and the incorporation of these motion vector fields in the 3D list-mode PET reconstruction.

Education

PhD in Medical Physics, University of Paris VI

Select Publications

1. Guerin B, Gebhardt M, Cauley S, Adalsteinsson E, Wald LL. Local specific absorption rate (SAR), global SAR, transmitter power, and excitation accuracy trade-offs in low flip-angle parallel transmit pulse design. Magn Reson Med. 2014;71(4):1446-57. [April 2014 issue Editor pick]

2. Guerin B, Gebhardt M, Serano P, Adalsteinsson E, Hamm M, Pfeuffer J, et al. Comparison of simulated parallel transmit body arrays at 3 T using excitation uniformity, global SAR, local SAR, and power efficiency metrics. Magn Reson Med. 2015;73(3):1137-50.

3. Guerin B, Villena JF, Polimeridis AG, Adalsteinsson E, Daniel L, White JK, et al. The ultimate signal-to-noise ratio in realistic body models. Magn Reson Med. 2017;78(5):1969-80.

4. Davids M, Guerin B, Malzacher M, Schad LR, Wald LL. Predicting Magnetostimulation Thresholds in the Peripheral Nervous System using Realistic Body Models. Sci Rep. 2017;7(1):5316.

Highlights

2005: Fulbright fellowship (Arthur-Sachs program)

2006: Ecole Centrale-Harvard University Jean Gaillard Memorial Fellowship

2008: Society of Nuclear Medicine Young Investigator Award (Computer and Instrumentation Council)

Website

Magnetic Resonance – Physics & Instrumentation Group

Yinching (Iris) Chen

Assistant Professor

iris@nmr.mgh.harvard.edu
617-726-7873

Y. Iris Chen, PhD, is an Assistant Professor at the Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School in Boston, MA. She received her PhD in radiology science from the Harvard-MIT Health Sciences and Technology program and the department of radiology at MIT. Dr. Chen is a MRI physicist and she has pioneered the use of non-invasive neuroimaging techniques to better understand brain neurotransmitter function, particularly in neurodegeneration (such as Parkinson’s disease and Huntington’s disease) and drug addiction. She is currently focusing on topics in mild traumatic brain injury. In addition to neuroscience, her research covers brown fat, cardiac and molecular imaging of fibrosis in the kidney and lungs.

Education

PhD in Radiology Science, Harvard-MIT Health Sciences and Technology program and the department of radiology at MIT

Select Publications

1. Chen YC, Cypess AM, Chen YC, Palmer M, Kolodny G, Kahn CR, Kwong KK. Measurement of human brown adipose tissue volume and activity using anatomic MR imaging and functional MR imaging. J Nucl Med. 2013 Sep;54(9):1584-7.

2. Chen YI, Cypess AM, Sass CA, Brownell AL, Jokivarsi KT, Kahn CR, Kwong KK. Anatomical and functional assessment of brown adipose tissue by magnetic resonance imaging. Obesity (Silver Spring). 2012 Jul;20(7):1519-26.

3. Chen YC, Galpern WR, Brownell AL, Matthews RT, Bogdanov M, Isacson O, Keltner JR, Beal MF, Rosen BR, Jenkins BG. Detection of dopaminergic neurotransmitter activity using pharmacologic MRI: correlation with PET, microdialysis, and behavioral data. Magn Reson Med. 1997 Sep;38(3):389-98.

Highlights

NIH award(s) to study changes in dopaminergic function after mild traumatic brain injury

NIH award to study impact of acupuncture on brain neurotransmitter function

ISMRM Young investigator award finalist for pioneer studies in pharmacological MRI

Bin Deng

Assistant Professor

Optics @ Martinos

bin.deng@mgh.harvard.edu
617-726-9338

Tagged:Algorithm Development, Brain, Breast, Cancer, Clinical, Hardware Development, Machine Learning, MRI, Multimodal Imaging, Optical Imaging, Optics, Preclinical, Probe Development, Spectroscopy

Bin Deng, PhD, is a biomedical scientist whose research interests revolve around near-infrared (NIR) spectroscopy, functional optical imaging, the interactions between NIR light and tissue, noninvasive optical biomarkers and the pathophysiology of diseases. Dr. Deng investigates the intersection of physics, engineering and medicine to seek novel NIR spectroscopic and imaging technologies to address unmet clinical needs. Over the past five years, her research has focused on translating multimodal diffuse optical tomography (DOT) technology for the early assessment and therapy monitoring of breast cancer. Using a combination of technology development, theoretical modeling and clinical studies in her research, Dr. Deng searches for the answers to critical questions, such as whether, when and how clinical interventions should be introduced in the management of breast cancer.

Education

PhD in Bioengineering, Syracuse University

Select Publications

1. Deng B, Fradkin M, Rouet JM, Moore RH, Kopans DB, Boas DA, et al. Characterizing breast lesions through robust multimodal data fusion using independent diffuse optical and x-ray breast imaging. J Biomed Opt. 2015;20(8):80502.

2. Deng B, Brooks DH, Boas DA, Lundqvist M, Fang Q. Characterization of structural-prior guided optical tomography using realistic breast models derived from dual-energy x-ray mammography. Biomed Opt Express. 2015;6(7):2366-79.

3. Deng B, Lundqvist M, Fang Q, Carp SA. Impact of errors in experimental parameters on reconstructed breast images using diffuse optical tomography. Biomed Opt Express. 2018;9(3):1130-50.

Highlights

DigiBreast: This open-access complex digital breast phantom, derived from dual-energy X-ray mammograms of human subject, addresses the needs for simulation-based validations for a wide range of model-based imaging modalities, such as simulations of breast deformation, 2D and 3D X-ray breast imaging, and tomographic imaging of a compressed breast using tomographic optical, microwave, thermal and electrical impedance methods. DigiBreast actively used by researchers in the US and abroad.

Awards: NVIDIA GPU Grant; Best Poster Award at the Second Britton Chance International Symposium; etc.

Website

Optics @ Martinos

Iman Aganj

Assistant Professor

Laboratory for Computational Neuroimaging

iman@nmr.mgh.harvard.edu
617-724-5652

Tagged:Abdominal Organs, Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, Computed Tomography, CT, Data Science, Diabetes, Diffusion MRI, fMRI, FreeSurfer, Functional MRI, Machine Learning, Magnetic Resonance Imaging, MRI, Pancreas, Software Development

Dr. Aganj’s research objectives are focused on developing new medical image analysis techniques and improving existing ones so researchers and clinicians can extract the maximal amount of useful information from the images they acquire, with the goal of improving human health. These efforts range from deriving mathematical formulae via signal processing approaches to designing robust algorithms, all with the long-term goal of helping alleviate patients’ suffering from disease.

His research interests include medical image registration and segmentation, diffusion MR image reconstruction and analysis, and brain connectivity quantification.

Education

PhD in Electrical Engineering, University of Minnesota

Select Publications

1. Frau-Pascual A, Fogarty M, Fischl B, Yendiki A, Aganj I, Alzheimer’s Disease Neuroimaging I. Quantification of structural brain connectivity via a conductance model. Neuroimage. 2019;189:485-96.

2. Aganj I, Iglesias JE, Reuter M, Sabuncu MR, Fischl B. Mid-space-independent deformable image registration. Neuroimage. 2017;152:158-70.

3. Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N. Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn Reson Med. 2010;64(2):554-66.

Highlights

2016: Alzheimer’s Disease Research Award, BrightFocus Foundation

2015: Mentored Research Scientist Development (K01) Award, National Institutes of Health / National Institute of Diabetes and Digestive and Kidney Diseases

2014: Neurodegenerative Diseases Pilot Study Grant, Massachusetts Alzheimer’s Disease Research Center (ADRC)

Websites

Iman Aganj
Laboratory for Computational Neuroimaging

Ilknur Ay

Assistant Professor

Vagus Nerve Stimulation Laboratory (Director)
The Analog Brain Imaging Lab
The Caravan Lab

iay@mgh.harvard.edu
617-724-1145

Tagged:Brain, Cardiovascular Disease / Disorders, Cardiovascular System, Electroceuticals, MRI, Neuromodulation, PET, Preclinical, Stroke

Dr. Ay is an Assistant Professor at the Martinos Center for Biomedical Imaging at Massachusetts General Hospital and Harvard Medical School. She has a broad background in vascular pharmacology with fellowship training in neuroscience at the Massachusetts General Hospital.

Dr. Ay’s main research interest has been on the use of electrical and magnetic stimulation of neural structures to activate the endogenous neuroprotective system in the brain. Her laboratory has pioneered the research on using vagus nerve stimulation (VNS) in experimental models of acute stroke. They have shown that VNS using surgically implanted electrodes reduces lesion size in rats. Given that any therapy that requires surgery is a nonstarter in the setting of acute stroke, they have started testing minimally invasive and non-invasive VNS approaches in acute stroke models. As a direct result of her studies, a multicenter phase-II trial has been launched to test the safety and feasibility of VNS in acute human stroke.

Education

MD in Medicine, Faculty of Medicine, Hacettepe University, Turkey
PhD in Pharmacology, Hacettepe University, Turkey

Select Publications

1. Ay I, Lu J, Ay H, Gregory Sorensen A. Vagus nerve stimulation reduces infarct size in rat focal cerebral ischemia. Neurosci Lett. 2009;459(3):147–151.

2. Ay I, Napadow V, Ay H. Electrical stimulation of the vagus nerve dermatome in the external ear is protective in rat cerebral ischemia. Brain Stimul. 2015;8(1):7–12.

3. Ay I, Nasser R, Simon B, Ay H. Transcutaneous Cervical Vagus Nerve Stimulation Ameliorates Acute Ischemic Injury in Rats. Brain Stimul. 2016;9(2):166–173.

Highlights

2010: Scientist Development Award, American Heart Association

2015: Best Paper Award: Basic Science, Circulation: Cardiovascular Imaging

Website

The Caravan Lab

Caroline Magnain

Assistant Professor

Laboratory for Computational Neuroimaging
Optics @ Martinos

cmagnain@mgh.harvard.edu

Tagged:Algorithm Development, Alzheimer's Disease, Brain, Brain Mapping, FreeSurfer, MRI, Multimodal Imaging, Optical Imaging, Optics, Software Development, TBI

Dr. Magnain is an assistant professor in Radiology at Harvard Medical School and assistant in physics at Massachusetts General Hospital in Boston. Throughout her career, she has strived to apply optical imaging to various domains, from cultural heritage to the biomedical science, and has developed experimental set-ups, optical simulations, and image analysis software.

In 2011, Dr. Magnain joined the Athinoula A. Martinos Center for Biomedical Imaging as a postdoctoral fellow to initiate a collaboration between the Optics Division and the Laboratory for Computational Neuroimaging. She was the first to apply optical coherence tomography and microscopy (OCT/OCM) to image the human brain postmortem to visualize its cellular and fiber organizations. Dr. Magnain is involved in improving the imaging system, automating the acquisition, and writing image processing software. She also dedicates significant time to comparing and validating OCT/OCM data with gold standard methods used by collaborators, mostly doctors, neuroanatomists, and neuropathologists.

Education

PhD in Physics, Universite Pierre et Marie Curie, France

Select Publications

1. Magnain C, Augustinack JC, Tirrell L, Fogarty M, Frosch MP, Boas D, Fischl B, Rockland KS. Colocalization of neurons in optical coherence microscopy and Nissl-stained histology in Brodmann’s area 32 and area 21. Brain Struct Funct. 2019 Jan;224(1):351-362.

2. Magnain C, Augustinack JC, Konukoglu E, Frosch MP, Sakadžić S, Varjabedian A, Garcia N, Wedeen VJ, Boas DA, Fischl B. Optical coherence tomography visualizes neurons in human entorhinal cortex. Neurophotonics. 2015 Feb 9;2(1):015004.

3. Magnain C, Augustinack JC, Reuter M, Wachinger C, Frosch MP, Ragan T, Akkin T, Wedeen VJ, Boas DA, Fischl B. Blockface histology with optical coherence tomography: a comparison with Nissl staining. Neuroimage. 2014 Jan 1;84:524-33.

Highlights

2019: Chan Zuckerberg Initiative Imaging Scientist grantee

Website

Laboratory for Computational Neuroimaging

Optics @ Martinos

Maria Mody

Assistant Professor

Language and Reading Research Lab (Director)

maria@nmr.mgh.harvard.edu
617-726-6913

Tagged:Autism, Brain, Clinical, Diffusion MRI, FreeSurfer, MEG, MRI, Software Development

Maria Mody, PhD, is a cognitive neuroscientist specializing in neuroimaging of communication abilities in children and adults, with a focus on autism and dyslexia.The goal of her research is to identify core behaviors in developmental disorders of speech and language and the underlying neural mechanisms that could serve as targets for improved intervention. Dr. Mody uses a combination of MEG, MRI and DTI to examine top-down and bottom-up processes in the brain to better understand domain general vs. domain specific influences on cognitive development.

Education

PhD in Speech and Hearing Science, City University of New York

Select Publications

1. Mody M, Shui AM, Nowinski LA, Golas SB, Ferrone C, O’Rourke JA, McDougle CJ. Communication Deficits and the Motor System: Exploring Patterns of Associations in Autism Spectrum Disorder (ASD). J Autism Dev Disord. 2017 Jan;47(1):155-162.

2. Mody M, Wehner DT, Ahlfors SP. Auditory word perception in sentence context in reading-disabled children. Neuroreport. 2008 Oct 29;19(16):1567 71.

3. Sahyoun CP, Belliveau JW, Soulières I, Schwartz Mody M. Neuroimaging of the Functional and Structural Networks Underlying Visuospatial versus Linguistic Reasoning in High-Functioning Autism. Neuropsychologia 2010; 48:86-95.

Highlights

Mody, M. (ed) (2017). Neural Mechanisms of Language. New York, NY: Springer

Fellow, International Academy for Research in Learning Disabilities

Member, Harvard Joint Committee on the Status of Women

Website

Language and Reading Research Lab

Hsiao-Ying (Monica) Wey

Assistant Professor

Wey Lab

hsiaoying.wey@mgh.harvard.edu
617-724-1384

Dr. Hsiao-Ying (Monica) Wey is currently an Assistant Professor of Radiology at Harvard Medical School and Massachusetts General Hospital. She received her PhD in Medical Physics from the University of Texas Health Science Center at San Antonio in 2011 and completed her postdoctoral training at the Department of Radiology (Martinos Center for Biomedical Imaging) at Massachusetts General Hospital. Her research interests are centered on the technical development of multimodal PET/MR imaging and its applications investigating alternations of neurochemistry and function in the healthy and diseased brain. Specifically, she is developing methods to understand in vivo mu-opioid receptor regulation, such as receptor desensitization and internalization, in response to pain medications. Her research is currently funded by an NIH-NIDA K99/R00 Grant. Another major area of her research focuses on translating novel PET radiotracers from preclinical setting to a first-in-human study, and ultimately, to disease applications. Dr. Wey is an expert in PET/MR imaging experiment design, has extensive experience in small and large animal PET/MRI scanning, and is a leader in PET/MR imaging analysis of complex multi-modal data.

Education

PhD in Medical Physics, The University of Texas Health Science Center at San Antonio

Select Publications

1. Wey HY, Gilbert TM, Zürcher NR, She A, Bhanot A, Taillon BD, Schroeder FA, Wang C, Haggarty SJ, Hooker JM. Insights into neuroepigenetics through human histone deacetylase PET imaging. Sci Transl Med. 2016 Aug 10;8(351):351ra106.

2. Wey HY, Catana C, Hooker JM, Dougherty DD, Knudsen GM, Wang DJ, Chonde DB, Rosen BR, Gollub RL, Kong J. Simultaneous fMRI-PET of the opioidergic pain system in human brain. Neuroimage. 2014 Nov 15;102 Pt 2:275-82.

3. Wey HY, Wang DJ, Duong TQ. Baseline CBF, and BOLD, CBF, and CMRO2 fMRI of visual and vibrotactile stimulations in baboons. J Cereb Blood Flow Metab. 2011 Feb;31(2):715-24.

Highlights

Young Investigator Award, American College of Neuropsychopharmacology, 2016

Distinguished Alumni Award, Chang Gung University, Taiwan, 2015

ISMRM Junior Fellow, 2013-present

Website

Wey Lab

David Izquierdo

Assistant Professor

davidizq@nmr.mgh.harvard.edu

Tagged:Algorithm Development, Brain, Cardiovascular Disease / Disorders, Cardiovascular System, Clinical, Data Science, Machine Learning, Molecular Imaging, Multimodal Imaging, PET, PET-MRI, Preclinical, Software Development, Stroke

Dr. David Izquierdo is an Instructor in the Department of Radiology at Massachusetts General Hospital / Harvard Medical School with interest in improving non-invasive molecular imaging quantification with combined PET/MRI scanners. In particular most of Dr. Izquierdo’s research is applied to brain and cardiovascular imaging to provide useful diagnostic tools for early detection of cardiovascular disease and brain disorders.

Since Dr. Izquierdo’s initial steps in medical imaging at the University of Cambridge, UK, he has been working on improving PET image quantification, mostly using MR-based techniques. Among them, Ihe implemented for the first time a partial volume effect correction method for cardiovascular imaging. Dr. Izquierdo has also focused on improving simultaneous PET/MR image quantification by applying MR-based attenuation correction (AC) for PET imaging. Currently more than a dozen international groups have implemented this method for brain AC. Dr. Izquierdo is currently involved in a novel line of research using artificial intelligence on cardiovascular applications, that aims to provide improved cardiac motion detection and quantification.

Education

PhD Signal and Image Processing, Universite Bordeaux I

Select Publications

1. Izquierdo-Garcia D, Davies JR, Graves MJ, Rudd JH, Gillard JH, Weissberg PL, Fryer TD, Warburton EA. Comparison of methods for magnetic resonance-guided [18-F]fluorodeoxyglucose positron emission tomography in human carotid arteries: reproducibility, partial volume correction, and correlation between methods. Stroke. 2009 Jan;40(1):86-93.

2. Izquierdo-Garcia D, Hansen AE, Förster S, Benoit D, Schachoff S, Fürst S, Chen KT, Chonde DB, Catana C. An SPM8-based approach for attenuation correction combining segmentation and nonrigid template formation: application to simultaneous PET/MR brain imaging. J Nucl Med. 2014 Nov;55(11):1825-30.

3. Morales M*, Izquierdo-Garcia D*, Aganj I, Kalpathy-Cramer J, Rosen BR, Catana C. Implementation and Validation of a 3D Cardiac Motion Estimation Network (CarMEN). Radiology: Artificial Intelligence. In press. 2019.

Highlights

2019: Martinos Spark Award for Collaboration

2019: World Molecular Imaging Conference: co-Chair of Cardiology Category organization committee