A Simple, Dynamic Geometric Phantom for MRI and CT Reconstruction Pipelines: Beyond Shepp-Logan.

Area of Science:

• Medical Imaging
• Computational Modeling
• Geometric Phantoms

Background:

• Existing medical imaging phantoms are either static or overly complex 4D models.
• There is a need for intermediate solutions for testing dynamic imaging techniques.

Purpose of the Study:

• To develop a customizable, dynamic geometric phantom for medical imaging research.
• To provide a tool for quantitative evaluation of dynamic acquisition and reconstruction methods.
• To bridge the gap between static phantoms and complex 4D solutions.

Main Methods:

• Developed a dynamic geometric phantom using constructive solid geometry and superellipsoids.
• Modeled respiratory and cardiac motion with decoupled signal generators.
• Created a high-performance, open-source reference implementation in Julia.

Main Results:

• Quantified lung volume differences with a maximum relative error of 5.5%.
• Measured cardiac chamber volume errors between 1.76% and 3.67%.
• Demonstrated phantom utility in MRI simulations for motion blur and artifact quantification.

Conclusions:

• The dynamic geometric phantom offers a customizable, user-friendly, open-source alternative.
• It serves as a valuable tool for researchers developing new reconstruction algorithms.
• The phantom can be readily integrated into existing imaging research pipelines.