System, method and computer-accessible medium for detecting functional disorder(s) or aging progression in magnetic resonance imaging

Patent metadata

Field	Value
Publication number	US-12182969-B2
Application number	US-202117347917-A
Country	US
Kind code	B2
Filing date	Jun 15, 2021
Priority date	Aug 23, 2019
Publication date	Dec 31, 2024
Grant date	Dec 31, 2024

Abstract

An exemplary system, method, and computer-accessible medium for detection of functional disorder(s) or aging progression of patient(s) can be provided which can include, for example, receiving magnetic resonance imaging (MRI) information of the portion(s), generating gadolinium (“Gd”) enhanced map(s) based on the MRI information using a machine learning procedure(s), and detecting the functional disorder(s) or aging progression of the patient(s) based on the Gd enhanced map(s). The Gd enhanced map(s) can be a full dosage Gd enhanced map which can be a full dosage Gd enhanced cerebral blood volume map(s). The machine learning procedure can be a convolutional neural network. The MRI information can include (i) a low-dosage Gd MRI scan(s), and/or (ii) a Gd-free MRI scan(s). Functional disorder(s) or age progression can include a neurodegenerative disease, a neuropsychiatric disease, a neurodevelopment disorder or aging.

First claim

What is claimed is:

1. A non-transitory computer-accessible medium having stored thereon computer executable instructions for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising:
receiving magnetic resonance imaging (MRI) information of:
a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or
b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient;
generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit; and
detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map.

2. The computer-accessible medium of claim 1 , wherein the at least one Gd enhanced cerebral blood volume map is a full dosage Gd enhanced cerebral blood volume map.

3. The computer-accessible medium of claim 1 , wherein the at least one machine learning procedure is a convolutional neural network.

4. The computer-accessible medium of claim 1 , wherein the at least one functional neurological disorder or cognitive aging progression includes at least one of (i) a neurodegenerative disorder, (ii) a neuropsychiatric disease, (iii) cognitive aging.

5. The computer-accessible medium of claim 4 , wherein the neurodegenerative disorder includes Alzheimer's disease.

6. The computer-accessible medium of claim 4 , wherein the neuropsychiatric disease includes Schizophrenia.

7. The computer-accessible medium of claim 1 , wherein the at least one machine learning procedure includes at least one residual unit.

8. The computer-accessible medium of claim 7 , wherein the at least one machine learning procedure includes at least five layers.

9. The computer-accessible medium of claim 8 , wherein the at least one machine learning procedure includes at least one contraction path configured to encode at least one high resolution image into at least one low resolution representation.

10. The computer-accessible medium of claim 9 , wherein the at least one machine learning procedure includes at least one expansion path configured to decode the at least one low resolution representation into at least one further high-resolution image.

11. The computer-accessible medium of claim 1 , wherein the at least one machine learning procedure includes max-pooling and upsampling.

12. The computer-accessible medium of claim 11 , wherein the computer arrangement is further configured to perform the max-pooling and the upsampling using a factor of 2.

13. The computer-accessible medium of claim 12 , wherein the at least one machine learning procedure includes at least one batch normalization layer and at least one rectified linear unit layer.

14. The computer-accessible medium of claim 1 , wherein the at least one portion is at least one section of a brain of the at least one patient.

15. The computer-accessible medium of claim 1 , wherein the MRI information excludes data for a contrast-agent scan.

16. The computer-accessible medium of claim 1 , wherein the at least one Gd enhanced map is generated without information associated with data for a contrast-agent scan.

17. A non-transitory computer-accessible medium having stored thereon computer executable instructions for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, wherein, when a computing arrangement executes the instructions, the computing arrangement is configured to perform procedures comprising:
receiving magnetic resonance imaging (MRI) information of:
a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or
b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient;
generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure; and
detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map, wherein the at least one machine learning procedure includes at least five encoding layers and at least five decoding layers.

18. The computer-accessible medium of claim 17 , wherein each of the at least five encoding layers and each of the at least five decoding layers includes a residual connection.

19. The computer-accessible medium of claim 17 , wherein each of the at least five encoding layers and each of the at least five decoding layers include two series of 3×3 two dimensional convolutions.

20. The computer-accessible medium of claim 17 , wherein (i) each of the at least five encoding layers is followed by a 2×2 max-pooling layer, and (ii) each of the at least five decoding layers is followed by at least one 2×2 upsampling layers.

21. A method for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, comprising:
receiving magnetic resonance imaging (MRI) information of:
a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or
b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient;
using a computer hardware arrangement, generating at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit; and
using the computer hardware arrangement, detecting the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map.

22. A system for detecting at least one functional neurological disorder or cognitive aging progression of at least one patient, comprising:
a computer hardware arrangement configured to:
receive magnetic resonance imaging (MRI) information of:
a. only one gadolinium (Gd)-free pre-contrast T1W structural MRI scan of at least one portion of the at least one patient, or
b. only gadolinium (Gd)-free pre-contrast T1W structural MRI scans of at least one portion of the at least one patient,
generate at least one Gd enhanced cerebral blood volume map of the at least one portion based on the MRI information using at least one machine learning procedure, wherein the at least one machine learning procedure includes at least one attention unit, and
detect the at least one functional neurological disorder or cognitive aging progression of the at least one patient based on the at least one Gd enhanced cerebral blood volume map.

Assignees

• Univ Columbia

Inventors

• Guo Jia
• Small Scott A

Classifications

• G06T5/60

  using machine learning, e.g. neural networks · CPC title

• G16H30/40 Primary

  for processing medical images, e.g. editing · CPC title

• G16H50/50

  for simulation or modelling of medical disorders · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G06T2207/30016

  Brain · CPC title