Papers Diffusion MRI
“Diffusion MRI” 태그가 달린 논문 213편 · 필터 해제
NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms
Deformable image registration is crucial for aligning medical images in a nonlinear fashion across different modalities, allowing for precise spatial correspondence between varying anatomical structures. This paper prese…
DecoderDeformable Medical Image RegistrationDiffusion MRIImage Registration+2Sensitivity of quantitative diffusion MRI tractography and microstructure to anisotropic spatial sampling
Purpose: Diffusion weighted MRI (dMRI) and its models of neural structure provide insight into human brain organization and variations in white matter. A recent study by McMaster, et al. showed that complex graph measure…
Diffusion MRISensitivityMulti-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI
An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. Although existing works have investigated …
Diffusion MRIImputationEstimating Neural Orientation Distribution Fields on High Resolution Diffusion MRI Scans
The Orientation Distribution Function (ODF) characterizes key brain microstructural properties and plays an important role in understanding brain structural connectivity. Recent works introduced Implicit Neural Represent…
Diffusion MRIDDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI
In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning frame…
Diffusion MRIEnsemble LearningDeep multivariate autoencoder for capturing complexity in Brain Structure and Behaviour Relationships
<div><p>Diffusion MRI is a powerful tool that serves as a bridge between brain microstructure and cognition. Recent advancements in cognitive neuroscience have highlighted the persistent challenge of understanding how in…
DecoderDiffusion MRIDetailed delineation of the fetal brain in diffusion MRI via multi-task learning
Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain in-utero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in…
Diffusion MRIMulti-Task LearningWhen Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation
Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need…
Diffusion MRImodelImage Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI
Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans po…
Diffusion MRISCREENER: A general framework for task-specific experiment design in quantitative MRI
Quantitative magnetic resonance imaging (qMRI) is increasingly investigated for use in a variety of clinical tasks from diagnosis, through staging, to treatment monitoring. However, experiment design in qMRI, the identif…
Binary ClassificationDeep Reinforcement LearningDiffusion MRIMulti-class Classification+1Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion MRI tractography parcellation methods have been successful in defining major cerebellar fibre …
Diffusion MRIWhite matter tract crossing and bottleneck regions in the fetal brain
There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality h…
Diffusion MRITractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Classification from Diffusion MRI Tractography
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked…
Diffusion MRIMultimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuum
Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease. It is well-known that the ide…
Diffusion MRIHippocampusSpecificityDiffusion Model-based FOD Restoration from High Distortion in dMRI
Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the cor…
Diffusion MRIImage RestorationPhy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers through undersampled dMRI. To generate mor…
Diffusion MRIDiversityGenerative Adversarial NetworkA Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse prob…
Diffusion MRIparameter estimationUncertainty QuantificationEddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
Modern diffusion MRI sequences commonly acquire a large number of volumes with diffusion sensitization gradients of differing strengths or directions. Such sequences rely on echo-planar imaging (EPI) to achieve reasonabl…
Diffusion MRIdistortion correctionImage RegistrationOvercoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, t…
Deep LearningDiffusion MRISchedulingField-of-View Extension for Brain Diffusion MRI via Deep Generative Models
Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (FOV). This work aims to develop a method …
Diffusion MRIImputation