Dynamic Computed Tomography Angiography for capturing vessel wall motion: A phantom study for optimal image...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Cardiovascular Imaging

Background:

• Dynamic Computed Tomography Angiography (4D CTA) offers potential for assessing sub-millimeter vessel wall motion.
• Understanding vessel biomechanical properties through 4D CTA can enhance diagnosis, prognosis, and treatment of vascular pathologies.

Purpose of the Study:

• To identify the optimal image reconstruction method for 4D CTA in accurately measuring vessel diameter changes.
• Evaluating reconstruction techniques for precise assessment of dynamic vascular changes.

Main Methods:

• An elastic tube model with simulated pulsatile flow was imaged using ECG-gated 4D CTA.
• Images were reconstructed using hybrid iterative (Hybrid-IR), model-based iterative (MBIR), and deep-learning-based (DLR) methods with half and full raw data modes.
• Diameter changes measured by 4D CTA were compared against ultrasound measurements using Sum of Relative Absolute Differences (SRAD).

Main Results:

• MBIR and DLR reconstruction methods demonstrated lower SRAD and standard deviation than Hybrid-IR.
• Full reconstruction mode yielded improved accuracy (lower SRAD and standard deviation) compared to half reconstruction mode.
• Deep-learning-based reconstruction was over three times faster than model-based iterative reconstruction.

Conclusions:

• 4D CTA effectively captures vessel diameter changes, comparable to ultrasound.
• DLR and MBIR algorithms provide more accurate results for assessing vessel diameter dynamics than Hybrid-IR.
• Full reconstruction mode enhances accuracy, while DLR offers a significant speed advantage over MBIR.