The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 15, 2025

Filed:

Jan. 25, 2023
Applicants:

Trustees of Tufts College, Medford, MA (US);

Lahey Clinic, Inc., Burlington, MA (US);

Inventors:

Irene Georgakoudi, Winchester, MA (US);

Einstein Gnanatheepam, Medford, MA (US);

Robert Michael Trout, Medford, MA (US);

Thomas Schnelldorfer, Arlington, MA (US);

Assignees:

Trustees of Tufts College, Medford, MA (US);

Lahey Clinic, Inc., Burlington, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01J 3/44 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
A61B 5/0075 (2013.01); A61B 5/0084 (2013.01); A61B 5/7264 (2013.01);
Abstract

Methods for improved imaging of internal tissue structures, such as lesions in the peritoneum, are disclosed employing color-weighted Polarization Enhanced Light (mPEL) imaging. A target tissue can be Illuminated with light of defined polarization at a plurality of wavelengths or wavelength bands. Scattered light from the tissue is collected and its polarization states analyzed and detected either via a polarization sensitive camera or a combination or polarizing filters and a standard camera. Light detected at distinct polarization states and colors is weighted by a factor and combined to yield an image that results in optimized visualization of lesions and/or discrimination of malignant from benign lesions. The factors may be identified based on a combination of Monte Carlo simulations and regression analysis to yield enhanced sensitivity to the tissue scattering power. Alternatively, the factors may be identified through machine learning based optimization algorithms to optimize tissue classification.


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