Quantitative myocardial perfusion from static cardiac and dynamic arterial CT

Area of Science:

• Cardiovascular imaging
• Medical physics
• Radiology

Background:

• Dynamic contrast-enhanced cardiac CT for myocardial blood flow (MBF) estimation requires frequent sampling, leading to motion artifacts and high radiation dose.
• Current methods necessitate multi-frame acquisitions for arterial input and myocardial tissue response functions, posing limitations for clinical application.

Purpose of the Study:

• To introduce and evaluate a novel static cardiac and dynamic arterial (SCDA) quantitative MBF approach.
• To reduce radiation dose and computational complexity in cardiac CT perfusion imaging.

Main Methods:

• The SCDA approach utilizes a well-sampled arterial input function (predicted or measured) and a single contrast-enhanced CT acquisition for myocardial data.
• A perfusion model generates myocardial contrast enhancement curves linked to MBF values, creating a lookup table for quantitative MBF estimation.
• The method was tested in 28 patients undergoing rest and vasodilator stress CT protocols.

Main Results:

• Using a measured input function with single or double myocardial acquisitions, MBF estimates achieved root mean square (RMS) errors of 1.2 and 0.35 ml/min/g, respectively.
• Radiation dose was reduced by 90% with single acquisition and 83% with double acquisition.
• Predicting the input function resulted in over threefold higher MBF estimation errors compared to measured input functions.

Conclusions:

• The static cardiac and dynamic arterial (SCDA) approach offers a simplified and effective method for quantitative myocardial perfusion imaging.
• SCDA significantly reduces radiation dose and computational demands compared to traditional dynamic CT perfusion protocols.
• Further validation is needed for SCDA methods relying on predicted input functions.