Development of a new Python-based cardiac phantom for myocardial SPECT imaging

Area of Science:

• Medical Imaging
• Computational Cardiology
• Nuclear Medicine

Background:

• Gated myocardial perfusion single photon emission computed tomography (SPECT) imaging is crucial for assessing cardiac function.
• Accurate digital phantoms are needed to validate and improve cardiac analysis software.

Purpose of the Study:

• To develop a digital dynamic cardiac phantom capable of mimicking gated myocardial perfusion SPECT images.
• To create a software tool for simulating cardiac function and perfusion with adjustable parameters.

Main Methods:

• A Python-based software code was developed using an ellipsoidal mathematical model for the left ventricle.
• The model incorporated myocardial boundaries, wall thickening, motion, and realistic count density variations.
• Variable defect extents, severities, and sizes were precisely modeled, considering cardiac contraction and partial volume effects.

Main Results:

• A Python-based software successfully modeled gated myocardial perfusion SPECT images with variable left ventricular volumes and ejection fraction.
• The phantom allows manipulation of shape, functional parameters, and defect characteristics (size, extent, severity).
• Image filtration effects on image quality were simulated, and defect impact on functional parameters was consistent with clinical findings.

Conclusions:

• A Python-based gated myocardial perfusion SPECT phantom was successfully developed.
• The phantom is reliable for assessing cardiac software analysis tools regarding perfusion and functional parameters.
• The software is under further development to enhance functionalities and features.