Title:
METHODS FOR USING MACHINE LEARNING AND MECHANISTIC MODELS FOR BIOLOGICAL FEATURE MAPPING WITH MULTIPARAMETRIC MRI
Document Type and Number:
WIPO Patent Application WO/2019/100032
Kind Code:
A3
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.
Inventors:
HU LELAND (US)
LI JING (US)
SWANSON KRISTIN (US)
WU TERESA (US)
GAW NATHAN (US)
YOON HYUNSOO (US)
HAWKINS-DAARUD ANDREA (US)
LI JING (US)
SWANSON KRISTIN (US)
WU TERESA (US)
GAW NATHAN (US)
YOON HYUNSOO (US)
HAWKINS-DAARUD ANDREA (US)
Application Number:
PCT/US2018/061887
Publication Date:
June 27, 2019
Filing Date:
November 19, 2018
Export Citation:
Assignee:
MAYO FOUND MEDICAL EDUCATION & RES (US)
International Classes:
A61B5/026
Domestic Patent References:
| WO2016138041A9 | 2016-11-17 |
Foreign References:
| US20160106321A1 | 2016-04-21 | |||
| US20140071125A1 | 2014-03-13 |
Other References:
KICKINGEREDER ET AL., RADIOGENOMICS OF GLIOBLASTOMA: MACHINE LEARNING-BASED CLASSIFICATION OF MOLECULAR CHARACTERISTICS BY USING MULTIPARAMETRIC AND MULTIREGIONAL MR IMAGING FEATURES, vol. 281, no. 3, 2016, pages 907 - 918, XP055621167, Retrieved from the Internet [retrieved on 20190325]
Attorney, Agent or Firm:
STONE, Jonathan, D. (US)
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