Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior

Area of Science:

• Medical Imaging
• Radiology
• Cardiovascular Imaging

Background:

• Dynamic myocardial perfusion computed tomography (DM-PCT) is crucial for diagnosing and assessing coronary artery disease risk.
• High radiation doses from repeated scanning limit DM-PCT's clinical utility.
• Developing low-dose protocols is essential for patient safety and wider application.

Purpose of the Study:

• To develop a novel statistical iterative reconstruction algorithm for low-dose DM-PCT.
• To incorporate a motion adaptive sparsity prior (MASP) model into a penalized weighted least-squares (PWLS) framework.
• To achieve high-quality DM-PCT imaging using low tube current data acquisition.

Main Methods:

• Developed the PWLS-MASP algorithm, modeling spatial and temporal sparsity in DM-PCT images.
• Assumed sparse differences between adjacent frames post-motion correction in the gradient domain.
• Validated the algorithm using a modified XCAT phantom and preclinical porcine DM-PCT datasets.

Main Results:

• PWLS-MASP produced high-quality DM-PCT images in both phantom and porcine studies.
• Outperformed filtered back-projection, PWLS-TV, and PWLS-RPCA in noise reduction and artifact mitigation.
• Achieved superior time density curve estimation and more accurate hemodynamic parametric maps.

Conclusions:

• The PWLS-MASP algorithm offers significant advantages for low-dose DM-PCT imaging.
• This approach holds potential for improving dynamic tomography in other medical fields.
• Enables enhanced diagnostic capabilities with reduced radiation exposure.