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An Efficient Preconditioner for Stochastic Gradient Descent Optimization of Image Registration.

Yuchuan Qiao, Boudewijn P F Lelieveldt, Marius Staring

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Area of Science:

Medical image analysis
Computational imaging
Optimization algorithms

Background:

Stochastic gradient descent (SGD) is widely used for parametric image registration.
Poorly scaled problems lead to sublinear convergence rates for standard SGD.
Improving SGD efficiency is crucial for clinical applications.

Purpose of the Study:

To develop an efficient preconditioner estimation method for accelerating SGD in image registration.
To enhance the convergence properties of SGD for badly scaled registration problems.
To maintain registration accuracy while improving computational speed.

Main Methods:

Estimating diagonal entries of a preconditioning matrix based on voxel displacement distribution.
Rescaling the optimization cost function using the estimated preconditioner.
Applying the method to mono-modal and multi-modal cost functions with rigid, affine, and B-spline transformations.

Main Results:

The proposed preconditioner significantly improves SGD convergence rates.
Observed speedups range from 2x to 5x across various clinical datasets and settings.
Registration accuracy was maintained compared to standard SGD.

Conclusions:

The developed preconditioner estimation method is efficient and effective for accelerating SGD in image registration.
This approach offers a practical solution for improving the performance of image registration algorithms.
The method is versatile, applicable to different imaging modalities and transformation models.