Motor Cortex Quantitative Susceptibility Mapping
| Purpose | Quantification of QSM of motor cortex and segmentation |
| Tag(s) |
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| Panel | Neuroradiology |
| Define-AI ID | 18030002 |
| Originator | John Tsiouris |
| Panel Chair | Alex Norbash |
| Panel Reviewers | Neuroradiology Panel |
| License | |
| 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 | |
| Definition | Standard deviation of QSM MR units of the left and right motor cortex |
| Data Type | Numeric |
| Value Set | [−100,100] |
| Units | MR Units |