Lianrui Zuo, Ph.D.

Lianrui Zuo, Ph.D.

he/his/him

Postdoctoral scholar at Vanderbilt University

Publications

Find the full list of my publications on Google Scholar.

Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis

Published in SPIE Medical Imaging, 2024

  • Authors: Savannah P. Hays, Lianrui Zuo, Yihao Liu, Anqi Feng, Jiachen Zhuo, Jerry L. Prince, Aaron Carass
  • Paper:
  • Abstract: Deep learning (DL) has led to significant improvements in medical image synthesis, enabling advanced image-to-image translation to generate synthetic images. However, DL methods face challenges such as domain shift and high demands for training data, limiting their generalizability and applicability. Historically, image synthesis was also carried out using deformable image registration (DIR), a method that warps moving images of a desired modality to match the anatomy of a fixed image. However, concerns about its speed and accuracy led to its decline in popularity. With the recent advances of DL-based DIR, we now revisit and reinvigorate this line of research. In this paper, we propose a fast and accurate synthesis method based on DIR. We use the task of synthesizing a rare magnetic resonance (MR) sequence, white matter nulled (WMn) T1-weighted (T1-w) images, to demonstrate the potential of our approach. During training, our method learns a DIR model based on the widely available MPRAGE sequence, which is a cerebrospinal fluid nulled (CSFn) T1-w inversion recovery gradient echo pulse sequence. During testing, the trained DIR model is first applied to estimate the deformation between moving and fixed CSFn images. Subsequently, this estimated deformation is applied to align the paired WMn counterpart of the moving CSFn image, yielding a synthetic WMn image for the fixed CSFn image. Our experiments demonstrate promising results for unsupervised image synthesis using DIR. These findings highlight the potential of our technique in contexts where supervised synthesis methods are constrained by limited training data.
  • Cite:
    @article{hays2024revisiting,
title={Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis},
author={Hays, Savannah P and Zuo, Lianrui and Liu, Yihao and Feng, Anqi and Zhuo, Jiachen and Prince, Jerry L and Carass, Aaron},
journal={arXiv preprint arXiv:2402.12288},
year={2024}
}

HACA3: A unified approach for multi-site MR image harmonization

Published in Computerized Medical Imaging and Graphics, 2023

  • Authors: Lianrui Zuo, Yihao Liu, Yuan Xue, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Murat Bilgel, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Susan M. Resnick, Jerry L. Prince, and Aaron Carass
  • Paper:
  • Code:
  • Abstract: The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects.
  • Cite:
       @article{zuo2023haca3,
     title={HACA3: A unified approach for multi-site MR image harmonization},
     author={Zuo, Lianrui and Liu, Yihao and Xue, Yuan and Dewey, Blake E and Remedios, Samuel W and
            Hays, Savannah P and Bilgel, Murat and Mowry, Ellen M and Newsome, Scott D and Calabresi, Peter A and
            Renick, Susan M and Prince, Jerry L and Carass, Aaron},
     journal={Computerized Medical Imaging and Graphics},
     volume={109},
     pages={102285},
     year={2023},
     publisher={Elsevier}}