A unified material decomposition framework for quantitative dual- and triple-energy CT imaging

Area of Science:

• Medical Imaging
• Materials Science
• Computational Imaging

Background:

• Accurate material differentiation in patient anatomy is crucial for clinical applications.
• Current CT techniques face limitations in precise material classification.

Purpose of the Study:

• Introduce a unified framework for nonlinear material decomposition.
• Apply this framework to triple-energy CT (TECT) and dual-energy CT (DECT) for enhanced material differentiation.
• Improve material classification in CT imaging.

Main Methods:

• Formulated polychromatic projection as a linear combination of material-selective images.
• Treated material decomposition as an iterative least-squares optimization problem.
• Evaluated the technique using numerical phantoms and realistic clinical CT configurations (micro-CT, DECT).

Main Results:

• Achieved quantitative material- and energy-selective images for both DECT and TECT.
• TECT demonstrated superior performance over DECT for multicontrast imaging with high accuracy (e.g., <8 mg/mL difference for gadodiamide concentration).
• Virtual monochromatic images showed improved quality compared to polychromatic kV CT images.

Conclusions:

• Established a unified framework for DECT and TECT material decomposition using commercial systems.
• The technique shows promise for CT-based diagnostics, particularly in cardiovascular and abdominal imaging requiring multicontrast analysis.
• Offers a potential solution for enhancing diagnostic and therapeutic applications in CT.