Contrastive Learning Approach for Assessment of Phonological Precision in Patients with Tongue Cancer Using MRI Data.

Magnetic Resonance Imaging (MRI) allows analyzing speech production by capturing high-resolution images of the dynamic processes in the vocal tract. In clinical applications, combining MRI with synchronized speech recordings leads to improved patient outcomes, especially if a phonological-based approach is used for assessment. However, when audio signals are unavailable, the recognition accuracy of sounds is decreased when using only MRI data.

Quantifying articulatory variations across phonological environments: An atlas-based approach using dynamic magnetic resonance imaging.

Identification and quantification of speech variations in velar production across various phonological environments have always been an interesting topic in speech motor control studies. Dynamic magnetic resonance imaging has become a favorable tool for visualizing articulatory deformations and providing quantitative insights into speech activities over time. Based on this modality, it is proposed to employ a workflow of image analysis techniques to uncover potential deformation variations in the human tongue caused by changes in phonological environments by altering the placement of velar consonants in utterances.

Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR images.

Magnetic resonance images are often acquired as several 2D slices and stacked into a 3D volume, yielding a lower through-plane resolution than in-plane resolution. Many super-resolution (SR) methods have been proposed to address this, including those that use the inherent high-resolution (HR) in-plane signal as HR data to train deep neural networks. Techniques with this approach are generally both self-supervised and internally trained, so no external training data is required.

A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.

Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, network architectures, and uncertainty estimation.

Homonymous hemi-macular atrophy in multiple sclerosis.

Background: Retrograde trans-synaptic degeneration (TSD) following retro-chiasmal pathology, typically retro-geniculate in multiple sclerosis (MS), may manifest as homonymous hemi-macular atrophy (HHMA) of the ganglion cell/inner plexiform layer (GCIPL).

Objective: To determine the frequency, association with clinical outcomes, and retinal and radiological features of HHMA in people with MS (PwMS).

Methods: In this cross-sectional study, healthy controls (HC) and PwMS underwent retinal optical coherence tomography scanning.

Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement.

Purpose: Eye morphology varies significantly across the population, especially for the orbit and optic nerve. These variations limit the feasibility and robustness of generalizing population-wise features of eye organs to an unbiased spatial reference.

Approach: To tackle these limitations, we propose a process for creating high-resolution unbiased eye atlases.

Vector field attention for deformable image registration.

Purpose: Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to their speed advantage over traditional algorithms as well as their better accuracy. Most existing deep learning-based methods require neural networks to encode location information in their feature maps and predict displacement or deformation fields through convolutional or fully connected layers from these high-dimensional feature maps.

Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.

Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks.

Super-Resolution in Clinically Available Spinal Cord MRIs Enables Automated Atrophy Analysis.

Background And Purpose: Measurement of the mean upper cervical cord area (MUCCA) is an important biomarker in the study of neurodegeneration. However, dedicated high-resolution scans of the cervical spinal cord are rare in standard-of-care imaging due to timing and clinical usability. Most clinical cervical spinal cord imaging is sagittally acquired in 2D with thick slices and anisotropic voxels.

The Pattern and Staging of Brain Atrophy in Spinocerebellar Ataxia Type 2 (SCA2): MRI Volumetrics from ENIGMA-Ataxia.

Objective: Spinocerebellar ataxia type 2 (SCA2) is a rare, inherited neurodegenerative disease characterised by progressive deterioration in both motor coordination and cognitive function. Atrophy of the cerebellum, brainstem, and spinal cord are core features of SCA2, however the evolution and pattern of whole-brain atrophy in SCA2 remain unclear. We undertook a multi-site, structural magnetic resonance imaging (MRI) study to comprehensively characterize the neurodegeneration profile of SCA2.

Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI.

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion.

Speech Motion Anomaly Detection via Cross-Modal Translation of 4D Motion Fields from Tagged MRI.

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes-i.e., articulatory-acoustic relation-is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies.

Synaptic Protein Loss in Extracellular Vesicles Reflects Brain and Retinal Atrophy in People With Multiple Sclerosis.

Objectives: To assess whether the rate of change in synaptic proteins isolated from neuronally enriched extracellular vesicles (NEVs) is associated with brain and retinal atrophy in people with multiple sclerosis (MS).

Methods: People with MS were followed with serial blood draws, MRI (MRI), and optical coherence tomography (OCT) scans. NEVs were immunocaptured from plasma, and synaptopodin and synaptophysin proteins were measured using ELISA.

Cerebellar volume measures may differentiate multiple sclerosis fallers from non-fallers.

Introduction: The cerebellum is a common lesion site in persons with multiple sclerosis (PwMS). Physiologic and anatomic studies have identified a topographic organization of the cerebellum including functionally distinct motor and cognitive areas. This study implemented a recent parcellation algorithm developed by Han et al.

Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer.

The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map.

Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation.

Optical coherence tomography (OCT) is a valuable imaging technique in ophthalmology, providing high-resolution, cross-sectional images of the retina for early detection and monitoring of various retinal and neurological diseases. However, discrepancies in retinal layer thickness measurements among different OCT devices pose challenges for data comparison and interpretation, particularly in longitudinal analyses. This work introduces the idea of a recurrent self fusion (RSF) algorithm to address this issue.

Motor control similarity between speakers saying "a souk" using inverse atlas tongue modeling.

Finite element models (FEM) of the tongue have facilitated speech studies through analysis of internal muscle forces indirectly derived from imaging data. In this work, we build a uniform hexahedral FEM of a tongue atlas constructed from magnetic resonance imaging data of a healthy population. The FEM is driven by inverse internal tongue tissue kinematics of speakers temporally aligned and deformed into the same atlas space, while performing the speech task "a souk" allowing muscle activation predictions.

Reproducibility evaluation of the effects of MRI defacing on brain segmentation.

Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored.

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. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI.

Synthesizing Audio from Tongue Motion During Speech Using Tagged MRI Via Transformer.

Investigating the relationship between internal tissue point motion of the tongue and oropharyngeal muscle deformation measured from tagged MRI and intelligible speech can aid in advancing speech motor control theories and developing novel treatment methods for speech related-disorders. 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.

RAPID BRAIN MENINGES SURFACE RECONSTRUCTION WITH LAYER TOPOLOGY GUARANTEE.

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images.

Automatic Quantification of Enlarged Perivascular Space in Patients With Traumatic Brain Injury Using Super-Resolution of T2-Weighted Images.

The perivascular space (PVS) is important to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) becomes visible on magnetic resonance imaging (MRI) and is best appreciated on T2-weighted (T2w) images. However, quantification of ePVS is challenging because standard-of-care T1-weighted (T1w) and T2w images are often obtained via two-dimensional (2D) acquisition, whereas accurate quantification of ePVS normally requires high-resolution volumetric three-dimensional (3D) T1w and T2w images.