Papers Quantitative MRI
“Quantitative MRI” 태그가 달린 논문 53편 · 필터 해제
Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Quantitative magnetic resonance imaging (qMRI) provides tissue-specific parameters vital for clinical diagnosis. Although simultaneous multi-parametric qMRI (MP-qMRI) technologies enhance imaging efficiency, robustly rec…
Image ReconstructionMRI ReconstructionQuantitative MRIZero-Shot LearningGuiding Quantitative MRI Reconstruction with Phase-wise Uncertainty
Quantitative magnetic resonance imaging (qMRI) requires multi-phase acqui-sition, often relying on reduced data sampling and reconstruction algorithms to accelerate scans, which inherently poses an ill-posed inverse prob…
MRI ReconstructionQuantitative MRIAccelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)
Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple…
Quantitative MRIUtilizing 3D Fast Spin Echo Anatomical Imaging to Reduce the Number of Contrast Preparations in $T_{1ρ}$ Quantification of Knee Cartilage Using Learning-Based Methods
Purpose: To propose and evaluate an accelerated $T_{1\rho}$ quantification method that combines $T_{1\rho}$-weighted fast spin echo (FSE) images and proton density (PD)-weighted anatomical FSE images, leveraging deep lea…
Quantitative MRIFoundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement
This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) p…
Decision MakingDimensionality ReductionManagementQuantitative MRIUnified 3D MRI Representations via Sequence-Invariant Contrastive Learning
Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI, where data are scarce and pre-trained 2D backbones cannot capture volumetric context. We present a s…
AnatomyARCBrain SegmentationContrastive Learning+3Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations
Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for parameter estimation in various quantitative MRI (QMRI) models. However, current deep learning implementations are not robust to ch…
Deep Learningparameter estimationQuantitative MRIA Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification
Deep learning-based techniques have potential to optimize scan and post-processing times required for MRI-based fat quantification, but they are constrained by the lack of large training datasets. Generative models are a…
Data AugmentationQuantitative MRIDomain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approaches to generate synthetic quantitative MRI…
Lesion SegmentationQuantitative MRISegmentationSynthetic Data GenerationMRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels
We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biolo…
Diffusion MRIQuantitative MRIDenoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI, enabling the mapping of multiple tissue properties from a single, accelerated scan. However, achieving accurate reconstructions re…
compressed sensingComputational EfficiencyDeep LearningDenoising+3Coordinate-Based Neural Representation Enabling Zero-Shot Learning for 3D Multiparametric Quantitative MRI
Quantitative magnetic resonance imaging (qMRI) offers tissue-specific physical parameters with significant potential for neuroscience research and clinical practice. However, lengthy scan times for 3D multiparametric qMR…
Quantitative MRIZero-Shot LearningMBSS-T1: Model-Based Subject-Specific Self-Supervised Motion Correction for Robust Cardiac T1 Mapping
Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges…
Image RegistrationQuantitative MRIFast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks
Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI f…
Quantitative MRITransfer LearningSCREENER: 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+1StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations
Magnetic Resonance Fingerprinting (MRF) is a time-efficient approach to quantitative MRI for multiparametric tissue mapping. The reconstruction of quantitative maps requires tailored algorithms for removing aliasing arte…
Image ReconstructionMagnetic Resonance FingerprintingQuantitative MRIqMRI Diffuser: Quantitative T1 Mapping of the Brain using a Denoising Diffusion Probabilistic Model
Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantit…
Denoisingparameter estimationQuantitative MRI3D MR Fingerprinting for Dynamic Contrast-Enhanced Imaging of Whole Mouse Brain
Quantitative MRI enables direct quantification of contrast agent concentrations in contrast-enhanced scans. However, the lengthy scan times required by conventional methods are inadequate for tracking contrast agent tran…
Quantitative MRIPhysics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI
We propose PHIMO, a physics-informed learning-based motion correction method tailored to quantitative MRI. PHIMO leverages information from the signal evolution to exclude motion-corrupted k-space lines from a data-consi…
Quantitative MRIRelaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks …
Image ReconstructionMRI ReconstructionQuantitative MRI