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.
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.
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.
This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as a point that lies onto or close to a smooth manifold, and landmark …
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.
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 …
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.
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.
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.
Human brain development is rapid during infancy and early childhood. Many disease processes impair this development. Therefore, brain developmental age estimation (BDAE) is essential for all diseases affecting cognitive development. Brain magnetic resonance imaging (MRI) of infants shows brain growth and morphologic pa…
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.
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.
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.
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with different approaches is difficult to compare and these studies are also difficult to reproduce. In the …
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.
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 learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-s…
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this paper, we tackle both problems simultaneously for the specific case of 2D Cartesian sampli…
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.
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measure…
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.
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.
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been traditionally limited by its slow data acquisition. Recent advances in compressed sensing (CS) techniques for MRI reduce acquisition time while maintaining high image quality. Whereas classical CS assumes the images are sparse in know…
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), …
Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large poo…
Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learni…
Paper extends 2D ZSAD to 3D MRI without training, achieving robust anomaly detection.
problem Challenges in extending zero-shot anomaly detection to 3D medical images.
method Constructs localized volumetric tokens by aggregating 2D slices processed by 2D foundation models.
result Training-free, batch-based ZSAD effectively extends from 2D encoders to full 3D MRI volumes.
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolut…
A method for MRI brain tumor segmentation using feature vectors and kernel dictionary learning.
problem Segmenting brain tumor regions in MRI images.
method Extracting feature vectors, training kernel dictionaries, and selecting informative feature vectors.
result The method outperforms other methods in segmentation accuracy and reduces training time.
A deep learning framework segments deep cerebellar nuclei from 7T MRI, improving accuracy and consistency.
problem Challenging to accurately segment small deep cerebellar nuclei using standard MRI protocols.
method Proposes DCN-Net, a deep learning framework that encodes contextual information and handles imbalanced data.
result Improves segmentation accuracy and consistency compared to existing methods.
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a nat…
This study benchmarks algorithms for automatic segmentation of LGE-MRI images of the left atrium.
problem Challenging segmentation of LGE-MRI images due to low contrast.
method Organized a large-scale benchmarking challenge with 154 3D LGE-MRIs and 27 teams.
result Top method achieved 93.2% dice score and 0.7 mm mean surface to surface distance.
Challenge aims to develop automated meningioma MRI segmentation models.
problem Lack of automated, objective tools for meningioma assessment.
method Develop and evaluate models on largest annotated dataset.
result Improved care of patients with meningioma through automated segmentation.
Generative models create synthetic MRI brain scans for research.
problem Lack of large, diverse medical datasets for machine learning.
method Training generative models on 40,000 MRI scans to produce 18,000 synthetic samples.
result Generated samples improve machine learning model accuracy.
Automated detection of MS lesions improves to 67% with 7T MRI.
problem Accurate detection of small, scarce cortical lesions in MS patients.
method 3D U-Net with brain tissue segmentation, supervised training on 7T MRI.
result 67% lesion detection rate with 42% false positives.
Enhances deep neural networks for MRI reconstruction by increasing expressivity.
problem Balancing network complexity and performance in deep learning MRI reconstruction.
method Geometric approach using bootstrapping and subnetwork aggregation with attention module.
result Significant improvement in MRI reconstruction performance with minimal complexity increase.
Paper improves MRI reconstruction by separating target labels and prediction error.
problem Improving MRI reconstruction accuracy by estimating prediction error.
method Proposes a novel method to estimate target labels and prediction error separately.
result Significantly better MRI reconstruction results achieved compared to state-of-the-art methods.