Registration uncertainty quantification via low-dimensional characterization of geometric deformations

Area of Science:

• Medical image analysis
• Computational anatomy
• Scientific computing

Background:

• Quantifying uncertainty in image registration is crucial for reliable medical image analysis.
• Existing methods often involve high computational complexity and lengthy processing times.
• Diffeomorphic registration is a powerful tool for analyzing anatomical changes.

Purpose of the Study:

• To develop an efficient Bayesian framework for quantifying image registration uncertainty.
• To reduce the computational complexity of uncertainty quantification in diffeomorphic registration.
• To enable faster and more accessible analysis of medical imaging data.

Main Methods:

• Developed a Bayesian diffeomorphic registration framework in a bandlimited space.
• Utilized a low-dimensional representation of geometric deformations.
• Approximated marginal posterior distributions using Laplace's method to avoid sampling algorithms.

Main Results:

• Demonstrated that a dense posterior distribution can be characterized by significantly fewer parameters.
• Achieved dramatic reductions in computational complexity for model inferences.
• Experimental results showed the method is significantly faster than state-of-the-art algorithms on synthetic and real 3D brain MRI data.
• Produced comparable results to existing methods in terms of accuracy.

Conclusions:

• The proposed method offers an efficient and computationally tractable approach to image registration uncertainty quantification.
• This framework has the potential to accelerate research and clinical applications in medical image analysis.
• The bandlimited approach effectively reduces computational load without compromising accuracy.