The Johns Hopkins University

National Institutes of Health, a Component of the US Dept. of Health & Human Services

Siemens Healthcare Gmbh

The Henry M. Jackson Foundation for the Advancement of Military Medicine Inc.

Daniel H Reich

Russell T Shinohara

Elizabeth M Sweeney

Ciprian Crainiceanu

Arthur Jeffrey Goldsmith

Peter C Searson

Christopher S Chen

Jorge Otero-Millan

Chia-Ling Chien

Dale Roberts

Sunil Goraksha Patil

Michael C Schubert

Gerald Meyer

Fan Xu

Navid Shiee

Collin L Broholm

Ruogang Zhao

Alan S Liu

Colin Shea

David Zee

Tristin Metz

Yu Shi

Pascal Sati

Bryan Ward

Dzung Pham

A computer-implemented method for automatically identifying subjects at risk of Multiple Sclerosis (MS) includes acquiring a plurality of images of a subject's brain using a Magnetic Resonance Imaging (MRI) scanner. A contrast enhancement process is applied to each image to generate a plurality of contrast-enhanced images. An automated lesion detection algorithm is applied to detect one or more lesions present in the contrast-enhanced images. An automated central vein detection algorithm is applied to detect one or more central veins present in the contrast-enhanced images. An automated paramagnetic rim detection algorithm is applied to detect one or more paramagnetic rims present in the contrast-enhanced images. The patient's risk for MS may then be determined based on the one or more of the lesions, central veins, and paramagnetic rims present in the contrast-enhanced images.

Automatic identification of subjects at risk of multiple sclerosis

The present invention is directed to a magnetic vestibular stimulation (MVS) system for rehabilitation of subjects with vestibular and balance disorders. The magnetic vestibular stimulation system includes a magnet and a cooling system. In some embodiments, the subject wears video goggles in order to stimulate the subject to orient to a virtual reality. The subject can lie, sit or stand in or near a strong magnetic field generated by the system. The magnetic field generated by the system stimulates the vestibular system to counteract the inherent imbalance created by a naturally-occurring vestibular disorder. Particularly important, the direction and amplitude of vestibular stimulation can be easily manipulated by altering the orientation and distance of the head relative to the magnetic field vector. Thus, MVS can be precisely tailored to a patient's specific pattern of vestibular disturbance.

Device for magnetic stimulation of the vestibular system

A system and method for conditioning a tissue are provided. The system includes a substrate, a plurality of microwells formed in the substrate, and a microsphere associated with each of the plurality of microwells. The system also includes a pair of flexible pillars within each of the plurality of microwells. Each flexible pillar includes a first end bonded to a respective microwell and at least one flexible pillar has a second end bonded to the microsphere. The flexible pillars are configured to deflect when exposed to a magnetic field to controllably stretch microtissue spanning the flexible pillars.

System for conditioning of engineered microtissues

The present invention, referred to as Oasis is Automated Statistical Inference for Segmentation (OASIS), is a fully automated and robust statistical method for cross-sectional MS lesion segmentation. Using intensity information from multiple modalities of MRI, a logistic regression model assigns voxel-level probabilities of lesion presence. The OASIS model produces interpretable results in the form of regression coefficients that can be applied to imaging studies quickly and easily. OASIS uses intensity-normalized brain MRI volumes, enabling the model to be robust to changes in scanner and acquisition sequence. OASIS also adjusts for intensity inhomogeneities that preprocessing bias field correction procedures do not remove, using BLUR volumes. This allows for more accurate segmentation of brain areas that are highly distorted by inhomogeneities, such as the cerebellum. One of the most practical properties of OASIS is that the method is fully transparent, easy to implement, and simple to modify for new data sets.

Method of analyzing multi-sequence MRI data for analysing brain abnormalities in a subject

A method of automatically detecting tissue abnormalities in images of a region of interest of a subject includes obtaining first image data for the region of interest of the subject, normalizing the first image data based on statistical parameters derived from at least a portion of the first image data to provide first normalized image data, obtaining second image data for the region of interest of the subject, normalizing the second image data based on statistical parameters derived from at least a portion of the second image data to provide second normalized image data, processing the first and second normalized image data to provide resultant image data, and generating a probability map for the region of interest based on the resultant image data and a predefined statistical model. The probability map indicates the probability of at least a portion of an abnormality being present at locations within the region of interest.

System and method of automatically detecting tissue abnormalities

The invention provides multisegmented, multifunctional magnetic nanowires for the probing and manipulation of molecules at the cellular and subcellular level. The different segments of the nanowire may have differing properties, including a variety of magnetic, non-magnetic, and luminescent behavior. Differences in surface chemistry allow different segments of a single nanowire to be functionalized with different multiple functional groups and/or ligands, giving the wire chemical multifunctionality.

Multifunctional magnetic nanowires

Growing community of inventors

Baltimore, MD, United States of America

Daniel H Reich