AI-powered histological fingerprinting

Patent metadata

Field	Value
Publication number	US-12584984-B2
Application number	US-202318364498-A
Country	US
Kind code	B2
Filing date	Aug 3, 2023
Priority date	Aug 3, 2023
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

Systems and methods for AI-powered histological fingerprinting in magnetic resonance imaging. MR signal data of an object is acquired using a high sensitivity scanner. Ground truth tissue microstructure data is acquired for the object. A forward model is learned using machine learning. The forward model is used to generate a dictionary or to train a model to map the signals to the histological parameters including the tissue microstructure of a scanner object. A signal-to-signal translation model is also provided to provide signals with improved sensitivity.

First claim

The invention claimed is:

1 . A system for histological fingerprinting, the system comprising:
an MRI scanner configured to generate signal evolutions for a sequence while scanning a patient; a histological forward model learned using machine learning, the histological forward model configured to input tissue microstructure properties and output signal evolutions; a histological dictionary created using the histological forward model; and an image processor configured to reconstruct an image including at least tissue microstructure properties of the patient, wherein the tissue microstructure properties of the patient are determined using the histological dictionary to map the signal evolutions to the tissue microstructure properties.

2 . The system of claim 1 , wherein the MRI scanner includes a maximum gradient amplitude of 300 mT/m or more.

3 . The system of claim 1 , wherein the histological forward model is learned using an encoder decoder network.

4 . The system of claim 1 , wherein the histological forward model is learned using machine learning with post-mortem high-resolution MRI of human tissue with corresponding microscopy images.

5 . The system of claim 1 , wherein the histological forward model is learned using machine learning with tissue microstructure information acquired using digital pathology and respective signal data.

6 . The system of claim 5 , wherein the respective signal data is simulated.

7 . The system of claim 1 , wherein the tissue microstructure properties includes at least one of cell size, cell shape, or cell wall permeability.

8 . The system of claim 1 , further comprising:
a display configured to display the image.

Assignees

• Siemens Healthineers Ag

Inventors

• Mostapha Mahmoud
• Comaniciu Dorin
• Nadar Mariappan S

Classifications

• A61B5/055

  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging · CPC title

• G01R33/5608 Primary

  Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels (image data processing or generation, in general G06T) · CPC title