Qiang Liu刘蔷

Ph.D. Candidate · MRI Acquisition & Reconstruction
College of Engineering, Northeastern University · Brigham and Women's Hospital, Harvard Medical School

I build open-source MRI acquisition and reconstruction methods — with a focus on diffusion MRI that behaves the same on any scanner, from any vendor.

My research develops vendor-agnostic pulse sequences and reconstruction pipelines that reduce inter-scanner variability in diffusion MRI, so that multi-center neuroimaging studies can trust that a measurement means the same thing whether it was acquired on a Siemens, GE, Philips, or Bruker system. I work across the full imaging chain: sequence design in Pulseq, high-resolution and super-resolution acquisition, and learning-based reconstruction and denoising.

I am a Ph.D. candidate at Northeastern University, advised by Dr. Deniz Erdogmus, and co-advised by Dr. Yogesh Rathi at Harvard Medical School, where I work at the Brigham and Women's Hospital.

Research Directions

Vendor-agnostic dMRI
Reproducible diffusion MRI across scanners. Open-source Pulseq sequences that cut cross-scanner variability in FA and diffusivity for multi-center studies.
Super-resolution
Mesoscale whole-brain imaging. Rotating-view and implicit-neural-representation reconstruction for high-isotropic-resolution imaging in clinically feasible scan times.
Reconstruction
Consistent, scanner-adaptive pipelines. Unified reconstruction and coil-level denoising that preserve microstructure while removing scanner-specific noise.
Sequence design
Accelerated relaxation–diffusion. Multi-echo and time-division-multiplexing sequences for unbiased T2 and relaxation-diffusion measurement.

First-Author Publications

* denotes co-first authorship
  1. Tang L*, Liu Q*, Ning L, Rathi Y. Scanner-adaptive coil-level denoising for diffusion MRI using explicit noise priors.Co-first 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026:1–5.
  2. Liu Q, Zhu A, Wang X, et al. Effect of a consistent reconstruction algorithm on inter-scanner reproducibility in diffusion MRI. Medical Physics, 2025; 52:e70096.
  3. Liu Q, Gagoski B, et al. Time-division multiplexing (TDM) sequence removes bias in T2 estimation and relaxation-diffusion measurements. Magnetic Resonance in Medicine, 2024; 92(6):2506–2519.
  4. Liu Q, Ning L, et al. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magnetic Resonance in Medicine, 2024; 92(1):246–256.
  5. Liu Q, Xu Z, Zhao K, et al. Diffusion-weighted magnetic resonance imaging in rat kidney using two-dimensional navigated, interleaved echo-planar imaging at 7.0 T. NMR in Biomedicine, 2021; 34(4):e4652.

Co-authored Publications

  1. Lyu J, Ning L, Consagra W, Liu Q, Rushmore RJ, Bilgic B, Rathi Y. Rapid whole brain motion-robust mesoscale in-vivo MR imaging using multi-scale implicit neural representation. Medical Image Analysis, 2026; 107:103830.
  2. Jiang Y, Jun Y, Liu Q, Zhong W, et al. Multishot dual polarity GRAPPA: robust Nyquist ghost correction for multishot EPI. Magnetic Resonance in Medicine, 2026; 95(5):2726–2736.
  3. Jun Y, Liu Q, Gong T, et al. PRIME: Phase reversed interleaved multi-Echo acquisition enables highly accelerated distortion-corrected diffusion MRI. Medical Image Analysis, 2026; 111:104058.
  4. Zhang F, Chen Y, Ning L, Rushmore J, Liu Q, et al. Assessment of the depiction of superficial white matter using ultra-high-resolution diffusion MRI. Human Brain Mapping, 2024; 45(14):e70041.
  5. Ruan G, Liu J, An Z, Wu K, Tong C, Liu Q, Liang P, et al. Automated skull stripping in mouse functional magnetic resonance imaging analysis using 3D U-Net. Frontiers in Neuroscience, 2022; 16:801769.
  6. Yang Y, Li Z, Liu Q, Guo Y, et al. Carotid arterial wall MRI of apolipoprotein E-deficient mouse at 7 T using DANTE-prepared variable-flip-angle rapid acquisition with relaxation enhancement. Magnetic Resonance Imaging, 2022; 86:1–9.

Recent

  1. 2026
    Received the ISMRM Summa Cum Laude Merit Award.
  2. 2026 · Feb
    Invited talk: Pulseq for High-Resolution Diffusion Acquisitions — Vendor-Agnostic Pulse Sequence Programming with Pulseq II, ISMRM Virtual Meeting.
  3. 2026
    ROVER-MRI — rapid whole-brain mesoscale imaging via implicit neural representation — published in Medical Image Analysis.
  4. 2026 · May
    Oral (AMPC-selected): High-SNR whole-brain mesoscale isotropic diffusion MRI using rotating-view acquisitions (ROVER-dMRI), ISMRM & SMRT Annual Meeting.
  5. 2025
    First-author paper on consistent reconstruction and inter-scanner reproducibility published in Medical Physics.
  6. 2024
    Two first-author diffusion-MRI sequence papers (TDM & vendor-agnostic dMRI) published in Magnetic Resonance in Medicine.

Photographs from the road — cities, gardens, and light, made between conferences and scans.

Rose Garden, Boston
Rose GardenBoston
Midtown at Night, New York
Midtown at NightNew York
Space Center, Houston
Space CenterHouston
Lake Austin, Austin
Lake AustinAustin
State Capitol, Austin
State CapitolAustin
Desert Dusk, Colorado
Desert DuskColorado

Get in touch

Open to collaborations on open-source MRI acquisition, reproducible diffusion imaging, and reconstruction.

Email
qliu30@mgh.harvard.edu · liu.qiang1@northeastern.edu
Affiliation
Northeastern University · Brigham and Women's Hospital, Harvard Medical School · Boston, MA
Scholar
Google Scholar
Code
HarmonizedMRI / Pulseq-diffusion · github.com/QiangLiu0310