Paper proposes a new method for MRI data recovery using bi-linear modeling.
problem Recovering high-fidelity MRI data from dynamic sequences.
method Bi-linear modeling framework for manifold learning and sparse approximation.
result The method improves MRI data recovery over existing techniques.
CSGM framework applied to clinical MRI data for robust reconstructions.
problem Applying deep generative priors to clinical MRI data for high-quality reconstructions.
method Training a generative prior on brain scans from the fastMRI dataset and using Langevin dynamics for posterior sampling.
result Posterior sampling via Langevin dynamics achieves high quality reconstructions in clinical MRI data.
Generative adversarial networks reconstruct MRI images without full data.
problem Lack of fully-sampled ground truth data for supervised MRI reconstruction.
method Generative adversarial networks for unsupervised MRI reconstruction.
result Reconstructed images show more anatomical structure than conventional methods.
Paper develops a novel kernel-based method for MRI data recovery.
problem Reconstructing dynamic MRI data on manifolds.
method Kernel bi-linear modeling in reproducing kernel Hilbert spaces.
result Validated on synthetic dMRI data, the method outperforms state-of-the-art approaches.
Deep model integrates MRI and DTI for autism severity prediction.
problem Predicting spectrum-level deficits in autism using multimodal brain imaging.
method Generative deep-learning framework combining rs-fMRI and DTI data.
result Hybrid model outperforms existing methods in predicting autism severity.
Study characterizes bladder motion using dynamic MRI and statistical analysis.
problem Limited volume coverage in dynamic MRI sequences hinders 3D shape reconstruction.
method 3D dense velocity measurements, LDDMM framework, statistical characterization, mean curvature changes, surface deformation analysis.
result Stable shape descriptor for characterizing bladder surface dynamics.
GNNs improve brain activity forecasting in fMRI studies.
problem Understanding neural dynamics in the brain.
method Comparison of GNN architectures for modeling fMRI data.
result GNNs outperform VAR models in robustly scaling to large network studies.
Deep learning predicts response to HER2-targeted breast cancer therapy.
problem Predicting response to HER2-targeted neoadjuvant chemotherapy.
method Developed and validated a deep learning approach using pre-treatment dynamic breast MRI.
result Deep learning model achieved strong performance in predicting pathological complete response.
Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sha…
Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method…
Intratumor heterogeneity is often manifested by vascular compartments with distinct pharmacokinetics that cannot be resolved directly by in vivo dynamic imaging. We developed tissue-specific compartment modeling (TSCM), an unsupervised computational method of deconvolving dynamic imaging series from heterogeneous tumor…
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
Simple 1-D CNNs classify Autism from rsfMRI.
problem Classifying Autism from high-dimensional rsfMRI data.
method Sub-sampled 1-D convolutional network.
result 1-D CNNs perform as well as state-of-the-art methods.
RODEO speeds up MRI and CT image reconstruction from sparse data.
problem Real-time reconstruction of dynamic medical images from limited data.
method Autoencoder with robust l1-norm cost function and Split Bregman method.
result Real-time image reconstruction with minimal quality loss.
Transforms input design for probabilistic models into optimal control of a Hamiltonian system.
problem Designing inputs for probabilistic models with intractable posterior distributions.
method Representing posterior as Hamiltonian system trajectories, solving optimal control problem.
result Parameter posterior concentrates around true parameter values.
XPDNet wins MRI reconstruction challenge with neural network.
problem MRI reconstruction from under-sampled data.
method Inspired by MRI and computer vision best practices, XPDNet uses a neural network.
result XPDNet achieved state-of-the-art results in the 2020 fastMRI challenge.
Understanding the functional architecture of the brain in terms of networks is becoming increasingly common. In most fMRI applications functional networks are assumed to be stationary, resulting in a single network estimated for the entire time course. However recent results suggest that the connectivity between brain …
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
fastMRI dataset helps machine learning for MRI faster, cheaper.
problem Accelerating MRI to reduce costs and patient stress.
method Open dataset and benchmarks for machine learning.
result Machine learning can reconstruct MRI images from fewer data.
Radiomics approach improves cardiac CVD diagnosis from cine-MRI.
problem Inaccurate expert visualization or clinical indices for CVD classification.
method Estimating radiomic features from cine-MRI, feature selection, advanced machine learning.
result Radiomics features correctly classified 100 cases of five cardiac classes.
New MRI method maps tissue parameters more accurately by ignoring voxel independence.
problem Voxel independence assumption limits model fitting reliability and repeatability.
method Self-supervised deep variational approach with Gaussian mixture prior.
result Our method outperforms current techniques in dMRI simulations and real data.
Deep learning network matches radiologists in breast cancer segmentation.
problem Automating radiologist-level cancer segmentation from breast MRI.
method 3D U-Net architecture trained on 382,290 breast scans, compared to 255,500 benign cases.
result Network performance matched radiologists' on 2D segmentation of breast cancers.
Improved 2D cardiac MRI with less data using deep learning.
problem Reducing artefacts in undersampled 2D radial cine MRI.
method Modified U-net trained on spatio-temporal slices.
result Outperforms existing methods in image quality and training efficiency.
StaPLR improves Alzheimer's disease classification by identifying important MRI scan types and measures.
problem Classifying Alzheimer's disease using multi-source MRI data.
method Stacked penalized logistic regression (StaPLR) with hierarchical multi-view structure and new view importance measure.
result StaPLR identifies the most important MRI scan types and measures for Alzheimer's disease classification.
Paper improves reproducibility of AD classification using diffusion MRI.
problem Difficulty in comparing and reproducing classification performance of AD studies using diffusion MRI.
method Extended a framework to ADNI data, including preprocessing and feature extraction. Used non-nested validation and compared different components.
result Diffusion MRI features can achieve comparable performance to T1w MRI, with proper feature selection and validation methods.
Survey of structured low-rank algorithms for MR signal recovery.
problem Recovering multidimensional signals from few non-uniform measurements.
method Structured low-rank matrix completion formulation.
result Performance guarantees and fast algorithms for large-scale MR problems.
LOUPE optimizes MRI under-sampling patterns for faster scans.
problem Accelerating MRI scans while maintaining image quality.
method End-to-end learning framework that trains on full-resolution scans.
result LOUPE-optimized masks yield superior reconstructions with 8x faster scans.
Deep learning speeds up MRI image reconstruction from sparse data.
problem Efficiently reconstructing MRI images from limited data.
method Data-driven, model-driven, and integrated deep learning approaches.
result Potential for deep learning to significantly speed up MRI reconstruction.
Enhances MRI image quality with Conditional WGAN and adaptive balancing.
problem Struggles to reconstruct sharp images with fine detail.
method Conditional Wasserstein Generative Adversarial Network (WGAN) with Adaptive Gradient Balancing.
result Produces sharper images than other techniques.
Transform learning improves MRI image reconstruction from sparse data.
problem Efficiently reconstruct MRI images from limited data.
method TL-based methods using learned models and transform domains.
result TL-based methods outperform classical CS methods in MRI reconstruction.
Framework integrates brain connectivity data for clinical predictions.
problem Predicting clinical outcomes from brain connectivity data.
method Structurally-regularized Dynamic Dictionary Learning (sr-DDL) and LSTM-ANN block.
result Framework outperforms state-of-the-art approaches in clinical outcome prediction.
The paper characterizes brain states and transitions using functional MRI data.
problem Characterizing the dynamic reconfiguration of neural systems in brain states.
method Bayesian model-based characterization of latent brain states and posterior predictive discrepancy using the latent block model.
result The model detects transitions between latent brain states and identifies distinctive community patterns in task-fMRI data.
Deep learning improves MRI image quality from down-sampled data.
problem Improving MRI image quality from accelerated, down-sampled k-space data.
method Deep Residual Dense U-Net architecture with Residual Dense Block and new loss function.
result The proposed method achieves better performance in reconstructing high-quality images from down-sampled k-space data.
3D CNNs interpret brain MRI differences between men and women.
problem Interpreting 3D CNNs for voxel-wise brain MRI analysis.
method Three interpretation methods: Meaningful Perturbations, Grad CAM, and Guided Backpropagation.
result Voxel-wise 3D CNN interpretation of brain MRI data.
Study identifies key MRI features for predicting cognitive performance after mTBI.
problem Identify relevant diffusion MRI metrics for cognitive functions in mTBI patients.
method Proposes a novel feature selection method combining best-first search with genetic algorithm crossover.
result Achieves significantly more accurate predictions than other feature selection algorithms.
A fast deep learning method for parallel MRI without calibration.
problem Calibration issues in parallel MRI reconstruction.
method Model-based deep learning, self-learning non-linear annihilation filters, Fourier domain pre-learning.
result Significantly faster than SLR methods (3 orders of magnitude), improved performance with spatial domain prior.
New neural network improves MRI reconstruction for non-Cartesian data.
problem Improving MRI reconstruction for non-Cartesian data acquisitions.
method Density-compensated unrolled neural networks.
result Density-compensated unrolled neural networks outperform baselines.
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.
method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.
result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
problem Accurate lung tumor segmentation in mice MRI scans for drug discovery.
method Optimized nnU-Net 3D model for lung tumor segmentation with minimal annotations.
result nnU-Net 3D models outperform 2D models in MRI mice scans, requiring fewer annotations.
Deep neural network improves CT synthesis from MRI.
problem Generating accurate CT images from MRI for PET reconstruction.
method Deep fully convolutional neural network that recursively reduces residuals.
result Decreased PET reconstruction error from 14.3% to 7.2%.
New method MRI improves machine learning models' ability to generalize to unseen data.
problem Machine learning models often fail to generalize well to out-of-distribution data.
method Introduces a new notion of invariance (MRI) and a practical version (MRI-v1) to improve model generalization.
result MRI-v1 guarantees invariant predictors and outperforms IRM-v1 in various settings.
New method optimizes MRI sampling patterns for faster scans.
problem Accelerate MRI scans without sacrificing image quality.
method Joint learning of adaptive sampling patterns and model-based recovery.
result Improved MR image quality compared to other methods.
MRI identifies chronic symptoms in mTBI patients.
problem Chronic symptoms in mTBI patients are hard to characterize.
method Multi-parametric MRI and low-dimensional projection.
result MRI metrics correlate with patient symptoms.
New method removes MRI banding without post-processing.
problem MRI banding in reconstructed images.
method Adversarial training to penalize banding structures.
result Superior banding removal compared to baseline.
Deep learning improves MRI analysis of MSK disorders.
problem Accurate and rapid analysis of musculoskeletal disorders from MRI scans.
method Convolutional neural networks (CNN) for automatic classification of knee abnormalities.
result Multi-view deep learning showed promising performance in classifying MSK abnormalities.
Machine learning for ASD diagnosis using morphological MRI networks.
problem Challenging to diagnose ASD using MRI due to heterogeneity and incomplete network neuroscience.
method Crowdsourced Kaggle competition to develop and benchmark ML pipelines.
result First-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity.
A central question for active learning (AL) is: "what is the optimal selection?" Defining optimality by classifier loss produces a new characterisation of optimal AL behaviour, by treating expected loss reduction as a statistical target for estimation. This target forms the basis of model retraining improvement (MRI), …
New method corrects MRI biases across scanners and sites.
problem Site and scanner biases in diffusion MRI data.
method Learning invariant representations using variational auto-encoders (VAE).
result Improvements on test data relative to a baseline method.