Search Results for author:
Found 31 papers, 7 papers with code
Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly
Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.
FastCod: Fast Brain Connectivity in Diffusion Imaging
Connectivity information derived from diffusion-weighted magnetic resonance images~(DW-MRIs) plays an important role in studying human subcortical gray matter structures.
Synthesizing audio from tongue motion during speech using tagged MRI via transformer
However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i. e., 4D motion fields) and one-dimensional audio waveforms.
New starting point registration method for tagged MRI tongue motion estimation
Accurate tongue motion estimation is essential for tongue function evaluation.
A latent space for unsupervised MR image quality control via artifact assessment
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.
DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue
However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields.
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI
After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Deep filter bank regression for super-resolution of anisotropic MR brain images
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Coordinate Translator for Learning Deformable Medical Image Registration
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.
Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement
Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities.
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.
MR Slice Profile Estimation by Learning to Match Internal Patch Distributions
To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm.
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
A Structural Causal Model for MR Images of Multiple Sclerosis
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
A Deep Joint Sparse Non-negative Matrix Factorization Framework for Identifying the Common and Subject-specific Functional Units of Tongue Motion During Speech
Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.
Self domain adapted network
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.
Finding novelty with uncertainty
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.
Validating uncertainty in medical image translation
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.
A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI
We then use three-dimensional synthetic and \textit{in vivo} tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.
Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size
To evaluate image synthesis, we investigated dependency of image synthesis accuracy on 1) the number of training data and 2) the gradient consistency loss.
Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks
Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina.
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.
Fiber Orientation Estimation Guided by a Deep Network
In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN).
Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images
Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications.
Estimation of Fiber Orientations Using Neighborhood Information
Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter.
