Motor Cortex Quantitative Susceptibility Mapping

Purpose	Quantification of QSM of motor cortex and segmentation
Tag(s)
Panel	Neuroradiology
Define-AI ID	18030002
Originator	John Tsiouris
Panel Chair	Alex Norbash
Panel Reviewers	Neuroradiology Panel
License	Creative Commons 4.0
Status	Published
Related RadElement Set	RDES67

                

Clinical Implementation

Value Proposition

The diagnosis of primary upper motor neuron diseases such as amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS) is a clinical challenge. Currently, the role of imaging is to exclude structural lesions that may mimic these diseases. Diagnosis is commonly delayed, and false positive diagnoses can occur. Therefore, there has been recent research interest in improved imaging biomarkers for ALS/PLS. Susceptibility weighted imaging (SWI) with quantitative susceptibility mapping (QSM) has become an intriguing imaging biomarker. Numerous recent publications indicate promise in differentiating patients with a primary upper motor neuron disease from normals (Schweitzer et al, AJR 2015; Adachi et al, Journal of Neuroimaging 2014) and mimics (Lee et al, Neuroradiology 2017). Currently, manual segmentation and QSM assessments of the motor cortex are necessary, difficult, and time consuming. Automating this procedure with machine learning would facilitate research and assist in the development of a promising imaging biomarker.

Narrative(s)

A 56-year-old man is having new difficulty walking and occasionally trips and falls. He is also having progressive difficulty writing and holding his eating utensils. Occasionally, he sees the muscles in his legs twitching involuntarily. These symptoms are concerning, and he sees a neurologist.

Workflow Description

The patient is seen by a neurologist, who suspects a motor neuron disease. He orders MRI scans of the brain and spine to assess for any structural lesions or demyelination.

An algorithm receives a post-processed SWI/QSM data set. If the algorithm can determine a result, return the following: representative images of motor cortex segmentation, quantitative measures of the entire motor cortex (left and right, separately), QSM measurements of the face/hand/leg portions of the motor cortex homunculus, and the odds/risk ratios for ALS/PLS given these results.

Additional considerations are as follows: The algorithm executes after the exam is verified on PACS. The algorithm optimally integrates on PACS and dictation/reporting software. The user is able to automatically populate the report or manually input the results. An indicator image may save to PACS as part of the medical record.

Considerations for Dataset Development

Procedures(s): MRI, Brain, SWI with QSM

Sex at Birth: {Male, Female}

 

Age (years): [21,90]

Technical Specifications

Inputs

DICOM Study

Procedure	MRI, Brain, SWI with QSM
Data Type	DICOM
Modality	MRI
Body Region	Head
Anatomic Focus	Brain

Primary Outputs

Motor Cortex QSM Mean

RadElement ID	RDE308
Definition	Mean calculation of QSM MR units of the left and right motor cortex
Data Type	Numeric
Value Set	[−100,100]
Units	MR Units

 

Motor Cortex QSM Max

RadElement ID	RDE309
Definition	Max calculation of QSM MR units of the left and right motor cortex
Data Type	Numeric
Value Set	[−100,100]
Units	MR Units

 

Motor Cortex QSM Standard Deviation

RadElement ID	RDE310
Definition	Standard deviation of QSM MR units of the left and right motor cortex
Data Type	Numeric
Value Set	[−100,100]
Units	MR Units

Future Development Ideas

Display segmentation map of motor cortex into left/right and face/hand/leg components.

Related Datasets

No known related public datasets at this time, please alert us if you know of any.