DR. GARTH MERVID BEACHE, M.D.
Radiology at Jackson St, Louisville, KY
License number
Kentucky D0051294
Category
Radiology
Type
Vascular & Interventional Radiology
License number
Kentucky 40968
Category
Radiology
Type
Diagnostic Radiology
License number
Kentucky 40968
Category
Radiology
Type
Vascular & Interventional Radiology
License number
Kentucky D0051294
Category
Radiology
Type
Diagnostic Radiology
Address
Address
530 S Jackson St SUITE C07, Louisville, KY 40202
Phone
(502) 852-5875
(502) 852-1754 (Fax)
(502) 581-1500
(502) 540-4959 (Fax)
(502) 852-1754 (Fax)
(502) 581-1500
(502) 540-4959 (Fax)
Personal information
See more information about GARTH MERVID BEACHE at radaris.comName
Address
Phone
1014 Anchorage Woods Cir, Louisville, KY 40223
1014 Anchorage Woods Cir, Louisville, KY 40223
Professional information
Garth M Beache, Louisville KY
Specialties:
Radiology, Diagnostic Radiology, Body Imaging, Vascular & Interventional Radiology
Work:
University of Louisville Hospital
530 S Jackson St, Louisville, KY 40202 Veterans Administration Medical Center
800 Zorn Ave, Louisville, KY 40206
530 S Jackson St, Louisville, KY 40202 Veterans Administration Medical Center
800 Zorn Ave, Louisville, KY 40206
Education:
Howard University(1987)
Garth Mervid Beache, Louisville KY
Specialties:
Radiologist
Address:
530 S Jackson St, Louisville, KY 40202
Education:
Doctor of Medicine
Board certifications:
American Board of Radiology Certification in Diagnostic Radiology (Radiology)
Spatial-Spectral Analysis By Augmented Modeling Of 3D Image Appearance Characteristics With Application To Radio Frequency Tagged Cardiovascular Magnetic Resonance
US Patent:
2013029, Nov 7, 2013
Filed:
Mar 15, 2013
Appl. No.:
13/834304
Inventors:
Matthew Nitzken - Louisville KY, US
Garth M. Beache - Louisville KY, US
Garth M. Beache - Louisville KY, US
Assignee:
UNIVERSITY OF LOUISVILLE RESEARCH FOUNDATION, INC. - Louisville KY
International Classification:
G06T 7/00
US Classification:
382131
Abstract:
A computer aided image processing system and automated method to improve tagged magnetic resonance image data through modeling and analyzing the magnetic resonance image data using a linear combination of discrete Gaussians model and using a Markov-Gibbs random field model. The processed magnetic resonance images include reduced noise associated with the tags, augmented gradients across a tag profile and an amplified tag to background contrast as compared to the original tagged magnetic resonance images.