Diffusion MRI · brain microstructure

Bridging the micro–macro gap with diffusion MRI

We build biophysical models and imaging tools that turn diffusion MRI into quantitative markers of brain tissue — and translate them from an ultra-strong-gradient research scanner to the clinic

Explore research →The Connectom.X
The group at the Connectom.X
News Connectom.X installed at NYU — only two such scanners in the world. Read the story →
Featured work
Nature Communications · 2026

Geometry of the cumulant series

RICE: orientation-independent biomarkers validated across a 1000+ subject MS cohort

Nature Communications · 2025

Axon morphology via scattering

Axonal shape parameters in seconds instead of months of simulation

Imaging Neuroscience · 2024

Mapping white-matter microstructure in health and disease

Biophysical axonal markers across development, stroke, and multiple sclerosis (N = 821)

Who we are

We are the MRI Biophysics Group at NYU Grossman School of Medicine, in the Center for Biomedical Imaging, co-directed by Els Fieremans and Dmitry Novikov. Our work spans the physics of diffusion in tissue, the algorithms that estimate microstructure from the MRI signal, and their translation into clinical markers for neurodegeneration, MS, traumatic brain injury, and aging.

Meet the team →

Research

Two PIs, complementary expertise across the whole pipeline

Acquisitionq-space, time-dependence, B-tensor shapes, high-b
Noise and artifact removalMPPCA, DESIGNER
Validationelectron microscopy, Monte Carlo
Biophysical modelingStandard Model, NEXI, RPBM, RICE
Parameter estimationmachine-learning estimators
Clinicbiomarkers, clinical translation
time-dependenceuniversalityRPBM

Time-dependent diffusion

Diffusion is not a single number — it changes with the timescale over which you observe it, and that time-dependence D(t) encodes the structural universality class of the tissue: how barriers and restrictions are arranged. Measuring how D(t) approaches its long-time limit reveals cell sizes, membrane permeability, surface-to-volume ratio — and, via scattering theory, the structural disorder along and across axons.

Novikov et al., Nature Physics 2011 · Novikov et al., PNAS 2014 · Fieremans et al., NeuroImage 2016 · Lee et al., Communications Biology 2020 · Abdollahzadeh et al., Nature Communications 2025

axon radiushigh-b

Axon diameter mapping

The diffusion signal carries a faint imprint of axon size — weighted toward the largest fibers and floored by the smallest, so the measured quantity is an effective radius reff, not a simple mean. We established how to recover it from the 1/√b signal scaling at high diffusion weighting, and how realistic axon shape — beading, undulations — biases it.

Veraart et al., eLife 2020 · Lee et al., NeuroImage 2020 · Lee et al., NMR in Biomedicine 2024

Standard ModelSMIfiber response

The Standard Model of diffusion in white matter

White matter behaves, to the diffusion signal, as multiple compartments — water inside narrow axons and water around them — sharing one fiber-orientation distribution. The Standard Model unifies WMTI, NODDI, ball-and-stick, Fiber Ball and other models of multiple Gaussian compartments as special cases of one framework, with reproducible clinical estimation and histological validation.

Novikov et al., NeuroImage 2018 · Coelho et al., NeuroImage 2022 · Coronado-Leija et al., Imaging Neuroscience 2024

NEXIKärger modelexchange time

Water exchange (NEXI)

In gray matter, water crosses neuronal membranes fast enough to carry information standard diffusion metrics cannot see. NEXI adds a fourth parameter — the exchange time τex (≈ 13 ms in human cortex). Because exchange this fast needs short diffusion times at high gradient strength, NEXI is the primary scientific rationale for the Connectom.X.

Jelescu et al., NeuroImage 2022 · Chan et al., Imaging Neuroscience 2025

multiple sclerosisAlzheimer's diseasestroke

Microstructure markers of neurodegeneration

Our biophysical markers translate from theory to the clinic — turning diffusion MRI into quantitative, interpretable biomarkers of disease: demyelination and axonal loss in multiple sclerosis, gray- and white-matter change in Alzheimer's disease and aging, and tissue injury in stroke and traumatic brain injury.

SO(3) topologySU(2) representationsRICE

Geometrization program

Diffusion MRI is, at root, a geometry problem. The orientations of both our probes — B-tensors, double-diffusion-encoding pairs — and of the tissue building blocks they measure live on the manifold of the rotation group SO(3): the three-sphere S³, not the familiar 2-sphere of directions. The representation theory of SU(2) gives the irreducible components of the cumulant tensors; the Hopf map S³→S² recovers ordinary shells for axially symmetric acquisitions. The payoff is unification — rotationally invariant contrasts (microscopic FA among them) fall out of one formalism, realized concretely in RICE.

ZSIPIPEq/k-space

Microstructure-inspired acquisitions

Understanding the structure of the diffusion signal lets us design smarter acquisitions, not just analyze whatever the scanner returns. Zero-Shell Imaging (ZSI) uses microstructure-inspired undersampling in q- and k-space to recover high-resolution whole-brain microstructure. PIPE estimates microstructure from non-uniform acquisitions, exploiting the full field of view of a strong-gradient scanner and harmonizing across systems.

MPPCARMT denoisingDESIGNER

From raw signal to reliable parameters

Quantitative diffusion MRI is only as trustworthy as the data going in. Random-matrix-theory denoising (MPPCA) removes thermal noise without blurring, grounded in Marchenko–Pastur statistics. With Gibbs/partial-Fourier (RPG), Rician bias, distortion, eddy, motion and B1 correction, it is unified in the DESIGNER pipeline (now v2), used by diffusion MRI labs worldwide. The same random-matrix-theory principles extend beyond denoising to image reconstruction.

Equipment · installed November 2025

The Connectom.X

Seeing what was previously invisible in the living human brain

The group at the Connectom.X
500 mT/m
gradient amplitude · 6–12× clinical
600 T/m/s
slew rate · 3–4× clinical
2
only two such scanners worldwide

A research-only, head-only Siemens Magnetom Connectom.X, installed at the NYU Center for Biomedical Imaging in the same facility as our clinical Prisma scanners. Funded by an NIH High-End Instrumentation award (S10OD034309).

Read the full CAI2R feature →
This level of microstructural detail lies well beyond the reach of a clinical scanner. It really allows you to look at brain microstructure in all its detail.Els Fieremans, PhD
We've developed so much understanding of brain microstructure by building tissue models of diffusion — and finally we have a machine that lets us see these effects experimentally.Dmitry Novikov, PhD

What it makes possible

  • Axon diameter mapping in vivo — reaching the high-b regime that isolates sub-2 µm axons
  • Time-dependent diffusion at the cellular scale — probing neuronal soma size, density, and beading of axons or other cell processes
  • Gray-matter water exchange on a ~13 ms timescale — resolvable here, biased by the noise floor on clinical scanners
  • Sub-millimeter whole-brain microstructure via Zero-Shell Imaging (ZSI) — microstructure-inspired undersampling in q- and k-space
  • High-resolution diffusion and functional MRI — faster k-space acquisitions shorten echo time and win SNR
Publications

Highlighted

Nature Communications · 2026

Geometry of the cumulant series (RICE)

Orientation-independent representation of the diffusion signal; biomarkers across a 1000+ subject MS cohort

[Nat Commun 2026]

Nature Communications · 2025

Axonal morphology via scattering theory

Axonal shape parameters in seconds instead of months of Monte-Carlo simulation

[Nat Commun 2025]

Hum Brain Mapp 2024 · Imaging Neurosci 2024 · NeuroImage 2022

Robust white matter microstructure mapping enabled by protocol optimization applied to clinical MRI

Clinical-protocol optimization improves robustness of white matter microstructure measures across standard diffusion and relaxometry acquisitions.

[Imaging Neurosci 2024] [Hum Brain Mapp 2024] [NeuroImage 2022]

Imaging Neurosci 2024 · Commun Biol 2020 · NeuroImage 2016

Validation of diffusion MRI white matter models

Empirical validation of diffusion MRI white matter models across microscopy, histology, and in vivo MRI measurements.

[Imaging Neurosci 2024] [Commun Biol 2020] [NeuroImage 2016]

MRM · 2026

Protocol-independent estimation (PIPE)

Microstructure from non-uniform acquisitions; enables strong-gradient scanners and harmonization

[MRM 2026]

NeuroImage · 2018 & 2024

The DESIGNER pipeline & v2

Used by diffusion MRI labs worldwide

[NeuroImage 2018] [Imaging Neurosci 2024]

MRM & NeuroImage 2016 · 2023

Random matrix theory-based denoising

MPPCA denoising and its extension to non-Cartesian reconstruction (USD)

[MRM 2016] [NeuroImage 2016] [Invest Radiol 2023] [arXiv 2023]

PNAS 2014 · Nature Physics 2011

Time-dependent diffusion and structural universality

The theory linking the time-dependence of diffusion to the universality class of tissue structure

[PNAS 2014] [Nature Physics 2011]

2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
Auto-generated from a PubMed query (NCBI E-utilities); each entry links to PubMed and DOI. Mirrors the live site’s bibliography feed.
Software

Open-source tools

Raw dMRI DESIGNER v2 Signal representations: DTI, DKI, RICE Biophysical models: SMI, RPBM

MP-PCA denoising

MATLABMRtrix

Parameter-free noise removal grounded in random-matrix theory; also dwidenoise in MRtrix

GitHub · MRtrix dwidenoise

DESIGNER v2

PythonDockerPyPI

End-to-end preprocessing + estimation: MPPCA denoising, RPG Gibbs correction, Rician bias, EPI/eddy/motion, B1 correction

GitHub · Docs · RPG (de-Gibbs)

RICE

MATLAB

Orientation-independent scalar biomarkers under arbitrary gradient encoding; minimal 1–2 min protocols

GitHub · Nature Communications 2026

SMI — Standard Model Imaging

MATLAB

Standard Model parameter estimation

GitHub

RPBM — Random Permeable Barrier Model

MATLAB

Membrane permeability and cell size from the time-dependence of diffusion

GitHub

People

Co-directors

Els Fieremans

Els Fieremans, PhD

Associate Professor · Co-Director
View profile →
Dmitry Novikov

Dmitry Novikov, PhD

Associate Professor · Co-Director
View profile →
Postdoctoral fellows
Gabrielle Baxter
Gabrielle Baxter
PhD
RPBM in myofascial pain syndrome
Santiago Coelho
Santiago Coelho
PhD
Standard Model estimation, rotational invariants (RICE), and Zero Shell Imaging (ZSI)
Ricardo Coronado-Leija
Ricardo Coronado-Leija
PhD
Validation of microstructure models using animal imaging, histology and Monte Carlo simulations
Jessie Mosso
Jessie Mosso
PhD
Diffusion-weighted spectroscopy and microstructure model validation in animals and humans
Valentin Stepanov
Valentin Stepanov
MD
Clinical translation of microstructure imaging
Graduate students
Jenny Chen
Jenny Chen
MSc
Diffusion preprocessing and the DESIGNER pipeline
Omnia Hassanin
Omnia Hassanin
MSc
RMT / USD denoising
Jamie Wren-Jarvis
Jamie Wren-Jarvis
MSc
Data harmonization and reproducibility of dMRI maps
Jeffery Wong
Jeffery Wong
MSc
Acquisition and artifact removal for dMRI
Administration
Harold Stern
Harold Stern
Program Manager
NJ
Nalini Jeet
Research Coordinator
MK
Michael Kuranty
Research Coordinator
Alumni

We're proud of where our alumni have gone — leading their own labs, founding companies, and shaping industry

Ali Abdollahzadeh
Ali Abdollahzadeh
Univ. of East Finland · PI
Benjamin Ades-Aron
Benjamin Ades-Aron
Microstructure Imaging · CTO
Lauren Burcaw
Lauren Burcaw
DoubleVerify · Product Manager
Marios Georgiadis
Marios Georgiadis
Stanford University · Instructor
Ileana Jelescu
Ileana Jelescu
Lausanne Univ. Hospital · Asst. Professor
Michael Lan
Michael Lan
NYU · Radiology Resident
Hong-Hsi Lee
Hong-Hsi Lee
Harvard / MGH · Asst. Professor
Gregory Lemberskiy
Gregory Lemberskiy
Microstructure Imaging · CEO
Ying Liao
Ying Liao
Netflix · ML Engineer
Subah Mehrin
Subah Mehrin
NYU · Student
Antonios Papaioannou
Antonios Papaioannou
GE · MR Product Specialist
Jelle Veraart
Jelle Veraart
NYU · Asst. Professor
Photos pulled live from diffusion-mri.com. Research foci confirmed for most of the team; Stepanov and Chen still to confirm.
← People
Els Fieremans

Els Fieremans, PhD

Associate Professor of Radiology · Co-Director

Els Fieremans co-directs the MRI Biophysics Group at NYU Grossman School of Medicine, in the Department of Radiology and Center for Biomedical Imaging. She graduated magna cum laude in Engineering Physics from Ghent University and earned her PhD there in 2008, on validation methods for diffusion-weighted MRI of brain white matter, before joining NYU as a postdoctoral fellow (2008–2011).

Her research combines Monte Carlo simulations and physical phantoms with biophysical modeling to design imaging markers of white-matter tract integrity — capturing axonal loss, demyelination, and axonal injury — and to study time-dependent diffusion across tissues, with applications in neurodegeneration, aging, and traumatic brain injury. WMTI, the random permeable barrier model (RPBM), and the DESIGNER pipeline are among the methods she has helped establish.

Selected honors

2024 Fellow, ISMRM · Vilcek Laboratories Fellow (2014–15) · Hirschl-Weill-Caulier Career Scientist Award (2020) · Junior Investigator, Charleston Conference on Alzheimer's Disease (2013) · Henri Benedictus Fellowship (2009)

← People
Dmitry Novikov

Dmitry Novikov, PhD

Associate Professor of Radiology · Co-Director

Dmitry Novikov co-directs the MRI Biophysics Group at NYU Grossman School of Medicine, in the Department of Radiology and Center for Biomedical Imaging. He earned his PhD in theoretical condensed-matter physics at MIT in 2003 and held research-fellow positions at Princeton and Yale — where his work spanned elastic scattering in graphene and electron transport in carbon nanotubes — before turning the tools of mesoscopic physics onto biological tissue.

His research develops the biophysics of diffusion and NMR transverse relaxation at the mesoscopic scale into quantitative maps of tissue microstructure: the Standard Model of diffusion in white matter, the effective-medium theory of time-dependent diffusion and structural universality, and the scattering-theory approach to diffusion to reveal cell morphology and water exchange. He pioneered the geometrization program for multidimensional diffusion acquisition and interpretation, involving the topology, irreducible representations and harmonic analysis on the SO(3) group manifold. Dmitry combines theoretical physics paradigms with the estimation and acquisition methods that make this physics usable and clinically relevant (MPPCA denoising based on random matrix theory; PIPE, and Zero-Shell Imaging for q/k-space undersampling).

Selected honors

2024 Fellow, ISMRM · Vilcek Laboratories Fellow (2012–2015)

Selected publications

Standard Model / rotational invariants (NeuroImage 2018) · Time-dependent diffusion and structural universality (Nature Physics 2011 / PNAS 2014) · Geometrization program (Nature Communications 2026) · Random matrix theory for denoising (MRM 2016 / NeuroImage 2016 / Invest Radiol 2023) · axon morphology via scattering (Nature Communications 2025)

Join us

Work at the frontier of brain microstructure

We recruit PhD students, postdocs, and research staff in diffusion MRI physics, modeling, and clinical translation. Our alumni have moved on to Harvard/MGH, NYU, Stanford, and industry, including the Microstructure Imaging spinoff.

Get in touch → Download the flyer (PDF) →

News

Latest

November 2025

Connectom.X arrives at NYU

One of only two Connectome-2.0 systems in the world, now installed at the Center for Biomedical Imaging

Nature Communications · 2026

RICE in Nature Communications

A complete orientation-independent representation of the diffusion signal

Nature Communications · 2025

Axon morphology via scattering theory

Quantifying axonal damage directly from the diffusion signal — in seconds, not months