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

Creative Commons 4.0

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

RDE275 

Definition

MBF quantification per segment

Data Type

Numeric / Polar Plot

Value Set

 

Units

mL/100 mL/min

 

MBV Quantification

RadElement ID

RDE276 

Definition

MBV quantification per segment

Data Type

Numeric / Polar Plot

Value Set

 

Units

mL/100 mL

 

Global MBF Quantification

RadElement ID

RDE277 

Definition

Global MBF Quantification

Data Type

Numeric / Polar Plot

Value Set

 

Units

mL/100 mL / min

 

Global MBV Quantification

RadElement ID

RDE278 

Definition

Global MBV Quantification

Data Type

Numeric / Polar Plot

Value Set

 

Units

mL/100 mL

 

MBF Index

RadElement ID

RDE279 

Definition

MBF index comparing MBF from each segment to the global LV-MBF value

Data Type

Numeric

Value Set

 

Units

 

 

Defect Detection

RadElement ID

RDE280 

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.