Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model

Ye C, Schneider LS, Sun Y, Thies M, Maier A (2025)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2025

Journal

Publisher: SPIE

Series: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Book Volume: 13405

Pages Range: 134052L

Conference Proceedings Title: Medical Imaging 2025: Physics of Medical Imaging

Event location: San Diego, CA

ISBN: 9781510685888

DOI: 10.1117/12.3047414

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.

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How to cite

APA:

Ye, C., Schneider, L.-S., Sun, Y., Thies, M., & Maier, A. (2025). Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model. In John M. Sabol, Ke Li, Shiva Abbaszadeh (Eds.), Medical Imaging 2025: Physics of Medical Imaging (pp. 134052L). San Diego, CA, US: SPIE.

MLA:

Ye, Chengze, et al. "Compressibility Analysis for the differentiable shift-variant Filtered Backprojection Model." Proceedings of the Medical Imaging 2025: Physics of Medical Imaging, San Diego, CA Ed. John M. Sabol, Ke Li, Shiva Abbaszadeh, SPIE, 2025. 134052L.

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