Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model
Journal:
arXiv
Published Date:
Jan 20, 2025
Abstract
The differentiable shift-variant filtered backprojection (FBP) model enables
the reconstruction of cone-beam computed tomography (CBCT) data for any
non-circular trajectories. This method employs deep learning technique to
estimate the redundancy weights required for reconstruction, given knowledge of
the specific trajectory at optimization time. However, computing the redundancy
weight for each projection remains computationally intensive. This paper
presents a novel approach to compress and optimize the differentiable
shift-variant FBP model based on Principal Component Analysis (PCA). We apply
PCA to the redundancy weights learned from sinusoidal trajectory projection
data, revealing significant parameter redundancy in the original model. By
integrating PCA directly into the differentiable shift-variant FBP
reconstruction pipeline, we develop a method that decomposes the redundancy
weight layer parameters into a trainable eigenvector matrix, compressed
weights, and a mean vector. This innovative technique achieves a remarkable
97.25% reduction in trainable parameters without compromising reconstruction
accuracy. As a result, our algorithm significantly decreases the complexity of
the differentiable shift-variant FBP model and greatly improves training speed.
These improvements make the model substantially more practical for real-world
applications.
