Evaluation of topological complexity and generation of quantitative markers in medical images

Adaptive image reconstruction techniques using inverse scattering algorithms and graph theory/algebraic topology address the challenge of quantifying topological complexity in medical images, improving diagnostic and predictive capabilities for personalized patient management.

WO2026112341A2PCT designated stage Publication Date: 2026-05-28QT ULTRASOUND
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Patent Information

Application Number
PCT/US2025/056419
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-11-21
Filing Date
2025-11-20
Publication Date
2026-05-28

AI Technical Summary

Technical Problem

Existing medical imaging techniques struggle to accurately quantify topological complexity and generate reliable diagnostic and predictive markers for personalized patient management, particularly in breast imaging, due to limitations in image reconstruction methods.

Method used

Adaptive image reconstruction techniques using inverse scattering algorithms and graph theory/algebraic topology to process medical images, generating segmented images of fibroglandular tissue and determining quantitative measures of topological complexity, which can be used as correction factors and inputs for risk assessment models.

Benefits of technology

Enhances personalized patient management through improved diagnostic accuracy and real-time response monitoring by providing precise quantitative estimates of complexity, aiding clinical decision-making and tailored therapeutic approaches.

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Abstract

A method can include receiving, at a computing system, a reconstruction image of a breast, the reconstruction image comprising image data corresponding to a plurality of transmission frequencies used by a transmitter of an imaging system; generating, by the computing system, a ductal and / or glandular image from the reconstruction image of the breast; and determining, by the computing system, a quantitative measure of topological complexity of the breast from the ductal and / or glandular image. In certain applications, a quantitative measure of topological complexity can be used as a correction factor to estimates such as a Volpara estimate. In certain applications, a quantitative measure of topological complexity can be used as an input to a risk assessment model.
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Citation Information

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