Invention Grant
- Patent Title: Methods for using machine learning and mechanistic models for biological feature mapping with multiparametric MRI
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Application No.: US16764837Application Date: 2018-11-19
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Publication No.: US11861475B2Publication Date: 2024-01-02
- Inventor: Leland S. Hu , Jing Li , Kristin R. Swanson , Teresa Wu , Nathan Gaw , Hyunsoo Yoon , Andrea Hawkins-Daarud
- Applicant: Mayo Foundation for Medical Education and Research , Arizona Board of Regents on behalf of Arizona State University
- Applicant Address: US MN Rochester
- Assignee: Mayo Foundation for Medical Education and Research,Arizona Board of Regents on behalf of Arizona State University
- Current Assignee: Mayo Foundation for Medical Education and Research,Arizona Board of Regents on behalf of Arizona State University
- Current Assignee Address: US MN Rochester; US AZ Scottsdale
- Agency: Quarles & Brady LLP
- International Application: PCT/US2018/061887 2018.11.19
- International Announcement: WO2019/100032A 2019.05.23
- Date entered country: 2020-05-15
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06N20/10 ; G16H50/20 ; G16H30/40 ; G06T7/00 ; G06F18/214

Abstract:
Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.
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