Myocardial Perfusion Quantification For CT
| Purpose | Automatic quantification and classification of myocardial perfusion |
| Tag(s) | |
| Panel | Cardiac |
| Define-AI ID | 18040014 |
| Originator | Marly Van Assen |
| Panel Chair | Carlo De Cecco |
| Panel Reviewers | Cardiac Panel |
| License | |
| Status | Published |
Clinical Implementation
Value Proposition
Myocardial perfusion CT imaging allows absolute quantification of myocardial blood flow (MBF) and could play a crucial role in the diagnosis and management of coronary artery disease. Analysis of perfusion acquisitions are currently performed visually by physicians based on the American Heart Association's (AHA) standard 17-segment model. This qualitative approach is time consuming and subjective, and suffers from inter- and intraobserver variability. Accurate quantification could provide information about perfusion defects related to specific vessel territories and could be used for diagnostic and prognostic purposes and to guide treatment. Global quantification is able to detect subclinical changes in myocardial perfusion and indicate microvascular disease.Narrative(s)
All patients undergoing myocardial perfusion imaging should have automated quantification of perfusion and detection of perfusion deficits. The results, including the segments with perfusion deficits, the size of these deficits, and the probability of the defects being caused by decreased blood flow or artefacts, should then be automatically populated into the radiology report or a report supplement. This Use Case Template will focus specifically on CT because this modality offers the best potential for automatic quantification of MBF. However, this approach could be interesting for other modalities too.Workflow Description
A patient receives dynamic stress myocardial perfusion CT protocols. An algorithm retrieves input from an imaging data set, relevant clinical data (including age, sex, and body surface area [mL/m2]), and other factors influencing myocardial perfusion, such as hypertension and diabetes. Using the algorithm, motion correction is performed to align the images made at all time points and automatically segments the borders of the left ventricular myocardium. The algorithm creates a segmentation based on the AHA’s 17-segment model on the polar plot for both rest and stress. MBF is calculated based on the time and attenuation curves on a per-pixel basis using the aorta for the arterial reference. Myocardial blood volume (MBV) is calculated for each of the segments on a per-pixel basis. Based on the location and size of the perfusion defect, the algorithms provide a probability of the defect being a real defect or an artefact. The algorithm returns measurements visualized in a 17-segment image as a color-coded map and then detects whether there are abnormal MBF values, either locally (indicating ischemia or infarction) or globally (potentially indicating microvascular disease). Based on the segmentation, the algorithm calculates local values for myocardial wall thickness and calculates left ventricle (LV) mass. The algorithm flags abnormal values and provides the location and size of each of the defects, which will be visualized on the color-coded polar plots.Considerations for Dataset Development
Procedures(s): Dynamic Stress CT perfusion
Sex at Birth: {Male, Female}
Age: [0,90]
Body Surface Area: Varied
Risk Factors: {Hypertension, diabetes}
Cardiac Abnormalities: {Congenital, Intervention}
Technical Specifications
Inputs
DICOM Study
| Procedure | Dynamic Stress CT Perfusion |
| Data Type | DICOM |
| Modality | CT |
| Body Region | Chest |
| Anatomic Focus | Heart |
Primary Outputs
MBF Quantification
| RadElement ID | |
| Definition | MBF quantification per segment |
| Data Type | Numeric / Polar Plot |
| Value Set |
|
| Units | mL/100 mL/min |
MBV Quantification
| RadElement ID | |
| Definition | MBV quantification per segment |
| Data Type | Numeric / Polar Plot |
| Value Set |
|
| Units | mL/100 mL |
Global MBF Quantification
| RadElement ID | |
| Definition | Global MBF Quantification |
| Data Type | Numeric / Polar Plot |
| Value Set |
|
| Units | mL/100 mL / min |
Global MBV Quantification
| RadElement ID | |
| Definition | Global MBV Quantification |
| Data Type | Numeric / Polar Plot |
| Value Set |
|
| Units | mL/100 mL |
MBF Index
| RadElement ID | RDE278 |
| Definition | MBF index comparing MBF from each segment to the global LV-MBF value |
| Data Type | Numeric |
| Value Set |
|
| Units |
|
Defect Detection
| RadElement ID | |
| Definition | Size, location (AHA segment), absolute values, and nature (ischemic/infarct) of defects |
| Data Type | Numeric/Polar Plot |
| Value Set |
|
| Units |
|
Secondary Outputs
LV Wall Thickness
| RadElement ID | RDES36 |
| Definition | Wall thickness measurements for each segment |
| Data Type | Numeric |
| Value Set |
|
| Units | mm |
LV Mass
| RadElement ID | RDES64 |
| Definition | Based on the segmentation, the LV mass is calculated |
| Data Type | Numeric |
| Value Set |
|
| Units | g |
Future Development Ideas
Consider algorithm adaptions to handle these quantifications beyond CT.Related Datasets
No known related public datasets at this time, please alert us if you know of any.