Search Results for author: Lianrui Zuo

Found 22 papers, 7 papers with code

Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

no code implementations31 Jan 2025 Chenyu Gao, Kaiwen Xu, Michael E. Kim, Lianrui Zuo, Zhiyuan Li, Derek B. Archer, Timothy J. Hohman, Ann Zenobia Moore, Luigi Ferrucci, Lori L. Beason-Held, Susan M. Resnick, Christos Davatzikos, Jerry L. Prince, Bennett A. Landman

To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs.

Computed Tomography (CT)

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

1 code implementation29 Oct 2024 Chenyu Gao, Michael E. Kim, Karthik Ramadass, Praitayini Kanakaraj, Aravind R. Krishnan, Adam M. Saunders, Nancy R. Newlin, Ho Hin Lee, Qi Yang, Warren D. Taylor, Brian D. Boyd, Lori L. Beason-Held, Susan M. Resnick, Lisa L. Barnes, David A. Bennett, Katherine D. Van Schaik, Derek B. Archer, Timothy J. Hohman, Angela L. Jefferson, Ivana Išgum, Daniel Moyer, Yuankai Huo, Kurt G. Schilling, Lianrui Zuo, Shunxing Bao, Nazirah Mohd Khairi, Zhiyuan Li, Christos Davatzikos, Bennett A. Landman

We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI), but younger in participants already diagnosed with Alzheimer's disease (AD).

Age Estimation Diffusion MRI +1

Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

no code implementations7 Oct 2024 Jinwei Zhang, Lianrui Zuo, Yihao Liu, Samuel Remedios, Bennett A. Landman, Jerry L. Prince, Aaron Carass

The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks.

Denoising Image Inpainting +2

Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI

no code implementations20 Sep 2024 Zhiyuan Li, Tianyuan Yao, Praitayini Kanakaraj, Chenyu Gao, Shunxing Bao, Lianrui Zuo, Michael E. Kim, Nancy R. Newlin, Gaurav Rudravaram, Nazirah M. Khairi, Yuankai Huo, Kurt G. Schilling, Walter A. Kukull, Arthur W. Toga, Derek B. Archer, Timothy J. Hohman, Bennett A. Landman

We hypothesize that by this design the proposed framework can enhance the imputation performance of the dMRI scans and therefore be useful for repairing whole-brain tractography in corrupted dMRI scans with incomplete FOV.

Diffusion MRI Imputation

Influence of Early through Late Fusion on Pancreas Segmentation from Imperfectly Registered Multimodal MRI

1 code implementation6 Sep 2024 Lucas W. Remedios, Han Liu, Samuel W. Remedios, Lianrui Zuo, Adam M. Saunders, Shunxing Bao, Yuankai Huo, Alvin C. Powers, John Virostko, Bennett A. Landman

We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images.

Image Registration Pancreas Segmentation +1

Vector Field Attention for Deformable Image Registration

1 code implementation14 Jul 2024 Yihao Liu, Junyu Chen, Lianrui Zuo, Aaron Carass, Jerry L. Prince

VFA uses neural networks to extract multi-resolution feature maps from the fixed and moving images and then retrieves pixel-level correspondences based on feature similarity.

Image Registration Retrieval

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

no code implementations19 Feb 2024 Savannah P. Hays, Lianrui Zuo, Yihao Liu, Anqi Feng, Jiachen Zhuo, Jerry L. Prince, Aaron Carass

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.

Anatomy Image Registration +1

Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

no code implementations3 Dec 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Dzung L. Pham, Aaron Carass, Jerry L. Prince

Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.

Lesion Segmentation Segmentation

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

no code implementations31 Oct 2023 Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Dzung L. Pham, Jerry L. Prince, Aaron Carass

Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.

Domain Generalization Lesion Segmentation

Optimal operating MR contrast for brain ventricle parcellation

1 code implementation4 Apr 2023 Savannah P. Hays, Lianrui Zuo, Yuli Wang, Mark G. Luciano, Aaron Carass, Jerry L. Prince

Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy.

Anatomy Image Harmonization

Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly

no code implementations3 Mar 2023 Yuli Wang, Anqi Feng, Yuan Xue, Lianrui Zuo, Yihao Liu, Ari M. Blitz, Mark G. Luciano, Aaron Carass, Jerry L. Prince

Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.

Management

A latent space for unsupervised MR image quality control via artifact assessment

1 code implementation1 Feb 2023 Lianrui Zuo, Yuan Xue, Blake E. Dewey, Yihao Liu, Jerry L. Prince, Aaron Carass

Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.

Contrastive Learning

Disentangling A Single MR Modality

no code implementations10 May 2022 Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Aaron Carass

Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.

Anatomy Disentanglement +3

Coordinate Translator for Learning Deformable Medical Image Registration

1 code implementation5 Mar 2022 Yihao Liu, Lianrui Zuo, Shuo Han, Yuan Xue, Jerry L. Prince, Aaron Carass

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.

Deformable Medical Image Registration Image Registration +1

Self domain adapted network

1 code implementation7 Jul 2020 Yufan He, Aaron Carass, Lianrui Zuo, Blake E. Dewey, Jerry L. Prince

However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.

Self-Supervised Learning Unsupervised Domain Adaptation

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