Systems, devices, and methods for harmonization of imaging datasets including biomarkers

Patent metadata

Field	Value
Publication number	US-12079960-B2
Application number	US-202217693166-A
Country	US
Kind code	B2
Filing date	Mar 11, 2022
Priority date	Mar 11, 2021
Publication date	Sep 3, 2024
Grant date	Sep 3, 2024

Abstract

An exemplary system, method and computer-accessible medium for harmonizing neuromelanin (NM) data using combat directly on a NM database or using combat generated coefficients to harmonize future data can include, for example, receiving imaging information of a brain of the patient(s), from one MRI scanner, receiving imaging information of a brain of the patient(s), from a second MRI scanner and using combat to harmonize the data between scanners against a reference dataset. The Neuromelanin (NM) concentration of the patient(s) can then be determined based on the harmonized data. The NM concentration can be determined using a voxel-wise analysis procedure. The voxel-wise analysis procedure can be used to determine a topographical pattern(s) within a substantia nigra (SN) of the brain of the patient(s).

First claim

What is claimed is:

1. A method of harmonizing a neuromelanin dataset using a vendor specific combat coefficient, comprising:
measuring a first level of neuromelanin associated with a first dataset obtained using a set of MRI machines manufactured by a first vendor;
measuring a second level of neuromelanin associated with second dataset obtained using a reference MRI machine manufactured by a second vendor different from the first vendor;
implementing a machine learning (ComBat) algorithm to generate a vendor specific coefficient associated with the set of MRI machines, the vendor specific coefficient being based on at least one of (1) the first level of neuromelanin associated with a set of MRI machines or (2) the second level of neuromelanin associated with reference MRI machine;
obtaining a third dataset associated with a first MRI machine manufactured by the first vendor; and
applying the vendor specific coefficient directly on the third dataset from the first MRI machine manufactured by the first vendor to measure an adjusted third level of neuromelanin associated with first MRI machine compared to at least one of (1) the second level of neuromelanin associated with the reference MRI machine or (2) the second dataset.

2. A method of assessing the neuromelanin concentration in a region of interest of the brain of a subject, comprising:
performing a Neuromelanin-Magnetic Resonance Imaging (NM-MRI) scan on a subject;
acquiring a neuromelanin dataset from the NM-MRI scan;
optionally encrypting the neuromelanin dataset;
uploading the neuromelanin dataset to a remote server;
optionally decrypting the dataset;
harmonizing the dataset by (i) running a combat algorithm on the entire dataset to generate and apply a coefficient, or (ii) applying a scanner specific coefficient to the dataset, or (iii) applying a vendor specific coefficient to the dataset;
performing an analysis of the neuromelanin dataset, wherein the analysis comprises one or more of:
(i) comparing the neuromelanin dataset with one or more previously acquired neuromelanin datasets from the said subject;
(ii) comparing the neuromelanin dataset with a control dataset;
(iii) comparing the neuromelanin dataset with one or more previously acquired neuromelanin datasets from different subjects;
generating a report comprising the neuromelanin analysis; and
uploading the report to remote server.

3. The method of claim 2 , wherein the NM dataset prior to adjustment with combat includes Contrast Ratio-Neuromelanin (CNR-NM) voxels.

4. The method of claim 2 , wherein the NM dataset after adjustment with combat comprises ComBat Harmonized Contrast Ratio-Neuromelanin (CH-CNR-NM) voxels.

5. The method of claim 2 , wherein the NM dataset includes measures indicating presence of Neuromelanin in a midbrain region including dopaminergic pathways of a subject.

6. The method of claim 2 , where the voxel-wise analysis procedure is performed on CH-CNR-NM voxels.

7. An apparatus, comprising:
a memory; and
a processor operatively coupled to the memory, the processor configured to:
receive information associated with an imaging source;
receive a first dataset including a first plurality of images associated with the imaging source, each image in the plurality of images includes a set of voxels;
apply a mask on the first plurality of images to generate a voxel map, the voxel map being based in an average of values associated with a subset of voxels from the set of voxels;
calculate a first set of measures based on the voxel map, the first set of measures being based on indications of a presence of a biomarker in the plurality of images based on which the voxel map was generated;
provide the first set of measures to a data harmonization algorithm, the data harmonization algorithm configured to calculate a set of coefficients, the set of coefficients when applied to the first set of measures configured to transform the first set of measures into a harmonized second set of measures, the second set of measures having a reduced variability associated with one or more identified sources of variability, compared to the first set of measures;
receive the second set of measures and the set of coefficients associated with the transformation of the first set of measures into the second set of measures; and
link and store the second set of measures and the set of coefficients with the information associated with the imaging source.

8. The apparatus of claim 7 , wherein the processor is configured to calculate the first set of measures by determining contrast to noise ratio (CNR) associated with each voxel from the subset of voxels, the CNR associated with each voxel being based on a difference between a first signal intensity associated with that voxel and a second signal intensity associated with a reference voxel, the reference voxel known to include minimal content of the biomarker.

9. The apparatus of claim 7 , wherein the biomarker is Neuromelanin.

10. The apparatus of claim 7 , wherein the imaging source includes at least one of an MRI scanner type, an MRI scanner, or a subject of an MRI scan.

11. The apparatus of claim 7 , wherein the set of coefficients is such that second set of measures has a reduced variability associated with one or more identified sources of variability, including identified sources of additive and multiplicative variability.

12. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the instructions comprising code to cause the processor to:
receive a dataset including a plurality of images;
receive information associated with an imaging source associated with the dataset;
compute a first set of measures based on the plurality of images;
search a database to determine a set of coefficients associated with the imaging source, the set of coefficients configured to transform the first set of measures into a harmonized second set of measures, the second set of measures having a reduced variability associated with one or more identified sources of variability, compared to the first set of measures;
obtain the set of coefficients associated with the imaging source;
apply the set of coefficients to the first set of measures to generate the second set of measures having the reduced variability associated with the one or more identified sources of variability; and
store the second set of measures in association with the set of coefficients and the information associated with an imaging source.

13. The non-transitory processor-readable medium of claim 12 , wherein the imaging source includes at least one of a brand of scanning equipment used to acquire the plurality of images, an identity of scanning equipment used to acquire the plurality of images, or a subject being scanned to acquire the plurality of images.

14. The non-transitory processor-readable medium of claim 12 , wherein the instructions include code to cause the processor to:
determine, in response to the search, an absence of the set of coefficients associated with the imaging source;
provide the first set of measures to a data harmonization algorithm, the data harmonization algorithm configured to generate the set of coefficients to transform the first set of measures into a harmonized second set of measures.

15. The non-transitory processor-readable medium of claim 12 , wherein the instructions include code to cause the processor to:
apply a mask to the plurality of images, the mask being based on a region of interest; and
compute, based on the application of the mask, an average voxelmap associated with the

Assignees

• Terran Biosciences Inc
• Univ Columbia
• The Res Foundation For Mental Hygiene Inc

Inventors

• Clark Samuel
• Wengler Kenneth
• Horga Hernandez Guillermo

Classifications

• G06T5/90

  Dynamic range modification of images or parts thereof · CPC title

• A61B5/055

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

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/10088

  Magnetic resonance imaging [MRI] · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title