BRAIN LabBILGIC RECONSTRUCTION ACQUISITION FOR IMAGING NEUROSCIENCE

Martinos Center · MGH · Harvard Medical School

Berkin Bilgic
BRAIN Lab

I am an Associate Professor in Radiology at Massachusetts General Hospital and Harvard Medical School. I head the BRAIN (Bilgic Reconstruction Acquisition for Imaging Neuroscience) lab at the Martinos Center for Biomedical Imaging.

Research interests

I am interested in MRI data acquisition and reconstruction, in particular:

01

Fast clinical imaging

02

Self-supervised machine learning

03

Quantitative parameter mapping

04

Diffusion imaging

05

Open-source pulse sequence development

Appointments & education

  • Nov 2023 – presentAssociate Professor in Radiology, MGH/Harvard
  • Jun 2018 – presentAffiliated Faculty, Health Sciences & Technology, Harvard-MIT
  • Jun 2019 – Nov 2023Assistant Professor in Radiology, MGH/Harvard
  • May 2016 – Jun 2019Instructor in Radiology, MGH/Harvard
  • Feb 2013 – May 2016Research Fellow in Radiology, MGH/Harvard
  • Feb 2010 – Feb 2013PhD in EECS, MIT
  • Sep 2008 – Feb 2010SM in EECS, MIT
  • Sep 2004 – Jun 2008BS in EE, Bogazici University
  • Sep 2004 – Jun 2008BS in Physics, Bogazici University
Berkin Bilgic

Berkin Bilgic

Building 75, 13th Street
Charlestown, MA 02129

bbilgic AT mgh.harvard.edu

617-866-8740

News, abstracts & software

2026

News from 2026

ISMRM ’23

News: Abstracts and software from the ISMRM'23 conference

  • H Yu et al: SubZero: Subspace Zero-Shot MRI Reconstruction, #0829, power pitch

  • A Heydari et al: Joint MR T1 and T2* Parameter Mapping with Scan Specific Unsupervised Networks, #1617, digital poster

  • IA Vurankaya et al: Self-Supervised Deep Learning Reconstruction for Highly Accelerated Diffusion Imaging, #0831, power pitch

  • Y Arefeen et al: Improved T1 and T2 mapping in 3D-QALAS using temporal subspaces and Cramer-Rao-bound flip angle optimization enabled by auto-differentiation, #0671, oral

  • X Wang et al: Model-based phase-difference reconstruction for accelerated phase-based T2 mapping, #4960, digital poster

  • X Wang et al: An Open-Source Self-navigated Multi-Echo Gradient Echo Acquisition for R2* and QSM mapping using Pulseq and Model-Based Reconstruction, #0420, combined educational & scientific session

  • Y Jun et al: Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Multiparametric Quantitative MRI Using QALAS, #1105, power pitch

  • Y Jun et al: SSL-QALAS: Self-Supervised Learning for Multiparametric Quantitative MRI Using QALAS, #2155, digital poster

  • TH Kim et al: Multi-echo MRI Reconstruction with Iteratively Refined Zero-shot Spatio-Temporal Deep Generative Prior, #0828, power pitch

  • J Cho et al: VUDU-SAGE: Efficient T2 and T2* Mapping using Joint Reconstruction for Motion-Robust, Distortion-Free, Multi-Shot, Multi-Echo EPI, #2202, digital poster

  • G Varela-Mattatall et al: Rapid Mesoscale MP2RAGE Imaging at Ultra High Field with Controlled Aliasing, #0539, oral

Sedona ’23

Abstracts and software from the Data Sampling and Image Reconstruction workshop, Sedona'23

  • Y Jun et al: Deep Subspace Reconstruction with Zero-Shot Learning for Multiparametric Quantitative MRI, oral

  • TH Kim et al: Zero-shot Prior Learning of Spatio-temporal Multi-echo/contrast MRI Reconstruction with Iterative Refinement

  • G Varela Mattatall et al: Parallel CS-Wave

  • X Wang et al: Model-Based Phase-Difference Reconstruction for Accelerated Phase-Based T2 Mapping

  • J Cho et al: VUDU-SAGE: Efficient T2 and T2* Mapping using Joint Reconstruction for Motion-Robust, Distortion-Free, Multi-Shot, Multi-Echo EPI

  • Y Arefeen et al: Improved T1 and T2 mapping in 3D-QALAS using temporal subspaces and flip angle optimization enabled by auto-differentiation

  • X Wang et al: Open-Source Self-navigated Multi-Echo GRE Acquisition for R2* and QSM mapping using Pulseq and Model-Based Reconstruction

  • X Wang et al: Model-Based Reconstruction for Joint Estimation of T1, T2 and B0 Inhomogeneity Maps Using Single-Shot Inversion-Recovery Multi-Echo Radial FLASH

ISMRM ’22

Software from the ISMRM'22 conference

  • Y Arefeen et al: Learning compact latent representations of signal evolution for improved shuffling reconstruction, #0247

  • J Cho et al: Variable Flip, Blip-Up and -Down Undersampling (VUDU) Enables Motion-Robust, Distortion-Free Multi-Shot EPI, #0757

  • J Cho et al: Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning for Joint Reconstruction, #0435

  • MY Avci et al: Quantifying the uncertainty of neural networks using Monte Carlo dropout for safer and more accurate deep learning based quantitative MRI, #4978

  • A Lin et al: Bayesian sensitivity encoding enables parameter-free, highly accelerated joint multi-contrast reconstruction, #3444

  • TH Kim et al: Accelerated MR Parameter Mapping with Scan-specific Unsupervised Networks, #4402

  • G Varela-Mattatall et al: Rapid CS-Wave MPRAGE acquisition with automated parameter selection, #1604

ISMRM ’22

Educational talk from ISMRM'22

  • Value of multi-contrast techniques (neuro), Wednesday session on Added Value of Sophisticated Multicontrast Techniques

We gratefully acknowledge our completed or current funding

NVIDIA GPU Grant to support machine learning researchChinese Scholarship Council (CSC) fellowship: (to Zijing Zhang)Office of China Postdoc Council (OCPC) fellowship: (to Zhifeng Chen)MIT International Science & Technology Initiatives (MISTI) GrantMGH ECOR Formulaic Bridge FundingNIH R01 EB028797NIH R03 EB031175ISMRM Research Exchange Grant Program: (to Gabriel Varela-Mattatall)NIH R01 EB032378NIH T32 EB001680 Neuroimaging training program fellowship: (to Yamin Arefeen)JSPS Overseas Research Fellowship: (to Shohei Fujita)NIH UG3 EB034875Zhejiang University Education Foundation: (to Yuting Cheng)Swiss National Science Foundation mobility grant: (to Quentin Uhl)NIH R01 EB034757NIH R21 AG082377NIH S10 OD036263NIH UH3 EB034875NIH P41 EB030006GE Healthcare: Rapid and high-fidelity abdominal diffusion MRI