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Systems and methods for processing MRI data

A data and preprocessing technology, applied in image data processing, application, image enhancement, etc., can solve the problem of long waiting time for MRI data acquisition and analysis

Pending Publication Date: 2022-01-28
BLACKTHORN THERAPEUTICS INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This can lead to excessive latency between MRI data acquisition and its analysis, especially in large datasets with hundreds of subjects, and especially when calculations are performed using traditional computer infrastructure such as high-performance workstation units

Method used

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  • Systems and methods for processing MRI data
  • Systems and methods for processing MRI data
  • Systems and methods for processing MRI data

Examples

Experimental program
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example 1

[0094] Example 1: Parameter selection

[0095] In response to the limitations of conventional systems and methods for processing and / or preprocessing MRI data, the present disclosure provides an automated search method for selecting optimal fMRI preprocessing pipeline parameters. Embodiments of the disclosed systems and methods have been validated on two independent datasets.

[0096] For example, from two publicly available MRI datasets CNP LA5c1 (N = 251) and EMBARC2 (N = 330), MRI data were preprocessed using 72 different parameter sets. This is due to the disclosed technique's ability to perform parallel fMRI preprocessing at scale and through an AFNI-based cloud-enabled pipeline. These 72 parameter sets were created by varying four different parameters that would normally require manual optimization - two from the structure-function alignment step and two from the cranial stripping step.

[0097] For each of the 72 pipeline outputs per subject, a whole-brain functional ...

example 2

[0099] Example 2: Parallel processing for QC for parameter selection

[0100] In some embodiments of the present disclosure, preprocessing the received MRI data may include parallel processing. Preprocessing of structural and functional MRI scans is a computationally intensive operation, typically requiring several hours per subject. This leads to excessive latency between MRI data acquisition and analysis, especially in large datasets with hundreds of subjects, and especially when calculations are performed using traditional computer infrastructure such as high-performance workstation units.

[0101] The present disclosure provides for cloud computing and / or massively parallel MRI preprocessing pipelines. Parallel preprocessing may include any suitable parallel processing technique. In some embodiments, the method provides preprocessing averaging over 150 scans per day. For example, in certain embodiments, a preprocessing pipeline can be constructed using FreeSurfer and th...

example 3

[0105] Example 3: Machine Learning with Automated QC

[0106] Over the past twenty-five (25) years, advances in the collection and analysis of functional magnetic resonance imaging (fMRI) data have enabled new insights into the brain basis of human health and disease. Individual behavioral changes can now be visualized at the neural level as patterns of connectivity between brain regions. Functional brain imaging thus enhances our understanding of clinical psychiatric disorders by revealing links between regional and network abnormalities and psychiatric symptoms.

[0107] Recent initial successes in the field have prompted the collection of larger datasets that require the use of fMRI to generate brain-based biomarkers to support the development of precision medicines. Despite methodological advances and enhancements in computing power, assessing fMRI scan quality remains a critical step in the analytical framework. Prior to analysis, expert reviewers visually inspected ind...

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Abstract

The present disclosure provides systems and methods for automating the QC of MRI scans. Particularly, the inventors trained machine learning classifiers using features derived from brain MR images and associated processing to predict the quality of those images, which is based on the ground truth of an expert's opinion. In one example, classifiers that utilized features derived from preprocessing log files (textual files output during MRI preprocessing) were particularly accurate and demonstrated an ability to be generalized to new datasets, which allows the disclosed technology to be scalable to new datasets and MRI preprocessing pipelines.

Description

[0001] Cross References to Related Applications [0002] This application claims priority and benefit to U.S. Provisional Patent Application Serial No. 62 / 841,420, filed May 1, 2019, and U.S. Provisional Patent Application Serial No. 62 / 923,238, filed October 18, 2019, each of which is hereby incorporated The entire content of is hereby incorporated by reference. technical field [0003] The present invention relates to the processing of MTI data. Background technique [0004] MRI data requires extensive preprocessing of scanned images to construct usable output datasets. Quality control (QC) of MRI data processing is a significant obstacle in the analysis of large-scale datasets, and especially affects preprocessing features for fMRI data. Traditional data processing requires human intervention (eg, "human in the loop (or human-machine loop)"). This human-involved data processing requires experts to manually identify correctly preprocessed output images. Typically, expe...

Claims

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Application Information

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IPC IPC(8): G06T7/00A61B5/055G06T5/40G06T7/41G16H30/40
CPCG16H30/40A61B5/055G06T2207/10088G06T2207/30168G01R33/5608G01R33/4806A61B5/7267G06T7/0002G06T2207/20081G06T2207/30016G06N20/00G16H30/20
Inventor 马修·科拉达亨贝托·安德烈斯·冈萨雷斯·卡贝萨斯刘岳陆莫妮卡·夏玛·梅勒姆帕维兹·阿哈玛德高庆柱
Owner BLACKTHORN THERAPEUTICS INC
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