Daniel S Reich

US Patent:

7132275, Nov 7, 2006

Filed:

May 14, 2002

Appl. No.:

10/143813

Inventors:

Daniel Reich - Baltimore MD, US
Gerald Meyer - Baltimore MD, US
Chia-Ling Chien - Lutherville MD, US
Christopher Chen - Baltimore MD, US
Peter C. Searson - Balto MD, US

Assignee:

The John Hopkins University - Baltimore MD

International Classification:

C07H 21/04
C12Q 1/68

US Classification:

4352871, 536 231, 536 243, 977704, 977728, 977769

Abstract:

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.

US Patent:

20050079591, Apr 14, 2005

Filed:

Jul 6, 2004

Appl. No.:

10/885275

Inventors:

Daniel Reich - Baltimore MD, US
Monica Tanase - New York NY, US
Christopher Chen - Princeton MD, US

International Classification:

C12N013/00

US Classification:

435173100

Abstract:

In accordance with the invention, a surface is provided with a plurality of microscale magnets (“micromagnets”) disposed on a surface in a pattern to form a desired distribution of magnetic field strength. Cells and magnetic nanowires are attached, immersed in fluid, and flowed over the pattern. The nanowires and their bound cells are attracted to and bound to regions of the pattern as controlled by the geometry and magnetic properties of the pattern, the strength and direction of the fluid flow, and the strength and direction of an applied magnetic field.

US Patent:

20220268867, Aug 25, 2022

Filed:

Apr 24, 2020

Appl. No.:

17/593365

Inventors:

Daniel Salo Reich - Bethesda MD, US
Erin Savner Beck - Bethesda MD, US
Govind Nair - Bethesda MD, US
Neville Dali Gai - Bethesda MD, US

International Classification:

G01R 33/56
G01R 33/563
G01R 33/565
A61B 5/00
A61B 5/055

Abstract:

Provided herein are methods and systems for high-resolution, cerebrospinal fluid-suppressed T2*-weighted magnetic resonance imaging of cortical lesions.

US Patent:

20200229698, Jul 23, 2020

Filed:

Jan 23, 2019

Appl. No.:

16/254710

Inventors:

- Erlangen, DE
- Bethesda MD, US
Daniel Reich - Washington DC, US

International Classification:

A61B 5/00
A61B 5/055
G06T 7/00
G01R 33/56
G01R 33/565

Abstract:

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.

US Patent:

20150302599, Oct 22, 2015

Filed:

Dec 5, 2012

Appl. No.:

14/362354

Inventors:

- Baltimore MD, US
- Rockville MD, US
Russell T. Shinohara - Baltimore MD, US
Arthur J. Goldsmith - Baltimore MD, US
Daniel Reich - Washington DC, US

International Classification:

G06T 7/00
G01T 1/16
G01R 33/48
A61B 8/00
A61B 5/00
A61B 5/055
A61B 6/03
G01R 33/50
G06F 19/00

Abstract:

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.

US Patent:

20150045651, Feb 12, 2015

Filed:

Mar 21, 2013

Appl. No.:

14/385509

Inventors:

- Baltimore MD, US
- Rockville MD, US
- Bethesda MD, US
Daniel S. Reich - Washington DC, US
Navid Shiee - North Bethesda MD, US
Russell T. Shinohara - Philadelphia PA, US
Elizabeth M. Sweeney - Baltimore MD, US

International Classification:

A61B 5/00
G01R 33/36
G06T 7/00
G01R 33/385

US Classification:

600410, 382131

Abstract:

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.