Quantitative vibratory sensation measurement is related to sensory cortical thickness in MS.

Objective: Vibratory sensation is a quantifiable measure of physical dysfunction and is often related to spinal cord pathology; however, its association with relevant brain areas has not been fully explored. Our objective was to establish a cortical structural substrate for vibration sensation.

Methods: Eighty-four individuals with multiple sclerosis (MS) ( = 54 relapsing,  = 30 progressive) and 28 controls participated in vibratory sensation threshold quantification at the great toe and a 3T MRI evaluating volume of the thalamus and cortical thickness primary and secondary sensory cortices.

A 3D Computational Head Model Under Dynamic Head Rotation and Head Extension Validated Using Live Human Brain Data, Including the Falx and the Tentorium.

We employ an advanced 3D computational model of the head with high anatomical fidelity, together with measured tissue properties, to assess the consequences of dynamic loading to the head in two distinct modes: head rotation and head extension. We use a subject-specific computational head model, using the material point method, built from T1 magnetic resonance images, and considering the anisotropic properties of the white matter which can predict strains in the brain under large rotational accelerations. The material model now includes the shear anisotropy of the white matter.

Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis.

Background: The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear.

Objective: To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS.

Methods: A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively.

Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study.

On average, African Americans with multiple sclerosis demonstrate higher inflammatory disease activity, faster disability accumulation, greater visual dysfunction, more pronounced brain tissue damage and higher lesion volume loads compared to Caucasian Americans with multiple sclerosis. Neurodegeneration is an important component of multiple sclerosis, which in part accounts for the clinical heterogeneity of the disease. Brain atrophy appears to be widespread, although it is becoming increasingly recognized that regional substructure atrophy may be of greater clinical relevance.

Deformable model reconstruction of the subarachnoid space.

The subarachnoid space is a layer in the meninges that surrounds the brain and is filled with trabeculae and cerebrospinal fluid. Quantifying the volume and thickness of the subarachnoid space is of interest in order to study the pathogenesis of neurodegenerative diseases and compare with healthy subjects. We present an automatic method to reconstruct the subarachnoid space with subvoxel accuracy using a nested deformable model.

Falx Cerebri Segmentation via Multi-atlas Boundary Fusion.

The falx cerebri is a meningeal projection of dura in the brain, separating the cerebral hemispheres. It has stiffer mechanical properties than surrounding tissue and must be accurately segmented for building computational models of traumatic brain injury. In this work, we propose a method to segment the falx using T1-weighted magnetic resonance images (MRI) and susceptibility-weighted MRI (SWI).

Automatic falx cerebri and tentorium cerebelli segmentation from Magnetic Resonance Images.

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging.

Thalamus segmentation using multi-modal feature classification: Validation and pilot study of an age-matched cohort.

Automatic segmentation of the thalamus can be used to measure differences and track changes in thalamic volume that may occur due to disease, injury or normal aging. An automatic thalamus segmentation algorithm incorporating features from diffusion tensor imaging (DTI) and thalamus priors constructed from multiple atlases is proposed. Multiple atlases with corresponding manual thalamus segmentations are registered to the target image and averaged to generate the thalamus prior.

Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class.

Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors.

Segmentation of the thalamus and thalamic nuclei is useful to quantify volumetric changes from neurodegenerative diseases. Most thalamus segmentation algorithms only use T1-weighted magnetic resonance images and current thalamic parcellation methods require manual interaction. Smaller nuclei, such as the lateral and medial geniculates, are challenging to locate due to their small size.

PROBABILISTIC FIBER TRACKING USING A MODIFIED LASSO BOOTSTRAP METHOD.

Diffusion MRI (dMRI) provides a noninvasive tool for investigating white matter tracts. Probabilistic fiber tracking has been proposed to represent the fiber structures as 3D streamlines while taking the uncertainty introduced by noise into account. In this paper, we propose a probabilistic fiber tracking method based on bootstrapping a multi-tensor model with a fixed tensor basis.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems.

Automatic method for thalamus parcellation using multi-modal feature classification.

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several derived fea-fractional anisotrophy, fiber orientation from the 5D Knutsson representation of the principal eigenvectors, and connectivity between the thalamus and the cortical lobes, as features.

Segmentation of skin lesions from digital images using joint statistical texture distinctiveness.

Melanoma is the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, especially among non-Hispanic white males and females, but survival rates are high if detected early. Due to the costs for dermatologists to screen every patient, there is a need for an automated system to assess a patient's risk of melanoma using images of their skin lesions captured using a standard digital camera.

Monte Carlo bias field correction in endorectal diffusion imaging.

Prostate cancer is one of the leading causes of cancer death in the male population. The detection of prostate cancer using imaging has been challenging until recently. Multiparametric magnetic resonance imaging (MRI) has been shown to allow accurate localization of the cancers and can help direct biopsies to cancer foci, which is required to plan the treatment.

Stochastic speckle noise compensation in optical coherence tomography using non-stationary spline-based speckle noise modelling.

Optical coherence tomography (OCT) allows for non-invasive 3D visualization of biological tissue at cellular level resolution. Often hindered by speckle noise, the visualization of important biological tissue details in OCT that can aid disease diagnosis can be improved by speckle noise compensation. A challenge with handling speckle noise is its inherent non-stationary nature, where the underlying noise characteristics vary with the spatial location.

Correlated diffusion imaging.

Background: Prostate cancer is one of the leading causes of cancer death in the male population. Fortunately, the prognosis is excellent if detected at an early stage. Hence, the detection and localization of prostate cancer is crucial for diagnosis, as well as treatment via targeted focal therapy.

MSIM: multistage illumination modeling of dermatological photographs for illumination-corrected skin lesion analysis.

Melanoma is the most deadly form of skin cancer and it is costly for dermatologists to screen every patient for melanoma. There is a need for a system to assess the risk of melanoma based on dermatological photographs of a skin lesion. However, the presence of illumination variation in the photographs can have a negative impact on lesion segmentation and classification performance.

Quantitative investigative analysis of tumour separability in the prostate gland using ultra-high b-value computed diffusion imaging.

High b-value diffusion-weighted imaging is a promising approach for diagnosing and localizing cancer in the prostate gland. However, ultra-high b-value imaging is difficult to achieve at a high signal-to-noise ratio due to hardware limitations. An alternative approach being recently discussed is computed diffusion-weighted imaging, which allows for estimation of ultra-high b-value images from a set of diffusion-weighted acquisitions with different magnetic gradient strengths.

Non-parametric Bayesian estimation of apparent diffusion coefficient from diffusion-weighted magnetic resonance imaging data.

A promising approach to prostate cancer diagnosis is multi-parametric MRI. One of the key modalities used in multi-parametric MRI is diffusion weighted MRI. Using multiple diffusion weighted MR acquisitions taken with different magnetic gradient strengths, the apparent diffusion coefficient (ADC) is calculated and can be used to identify tumors in the prostate.

Illumination correction in dermatological photographs using multi-stage illumination modeling for skin lesion analysis.

A novel algorithm for correcting illumination variation in dermatological photographs via a multi-stage modeling of the underlying illumination is proposed for the purpose of skin lesion analysis. First, an initial illumination estimate is obtained via a non-parametric modeling strategy based on Monte Carlo sampling. Next, a subset of pixels from the non-parametric estimate is used to determine a parametric estimate of the illumination based on a quadratic surface model.

Improved education and training for nursing assistants: keys to promoting the mental health of nursing home residents.

The mental health of older adults contributes to their overall well-being. However, numerous studies have reported substantial prevalence of mental health problems, especially depression, in nursing home residents. Due to the poor quality of education and training provided to nursing home front-line caregivers, most of whom are nursing assistants, many residents experiencing depression are not recognized as such and consequently receive no treatment.

Making the most of nursing homes' number one resource.

Nursing assistants are the number one resource in nursing homes. The challenge is to recruit, educate, and retain nursing assistants who consistently provide the highest quality of resident care.

Evaluation of an informal peer group experience on baccalaureate nursing students' emotional well-being and professional socialization.

Although studies have documented the importance of the academic environment in promoting positive outcomes among students, few quantitative studies in nursing have been performed to identify strategies through which a nurturing learning environment can be created. A randomized two-group pretest-posttest design was used to investigate effects of an informal peer group experience on baccalaureate nursing students' emotional well-being and professional socialization as caring practitioners. Groups did not differ significantly on the outcomes measured in this study.