Distributed Tomographic Reconstruction with Quantization
Journal:
arXiv
Published Date:
Oct 8, 2024
Abstract
Conventional tomographic reconstruction typically depends on centralized
servers for both data storage and computation, leading to concerns about memory
limitations and data privacy. Distributed reconstruction algorithms mitigate
these issues by partitioning data across multiple nodes, reducing server load
and enhancing privacy. However, these algorithms often encounter challenges
related to memory constraints and communication overhead between nodes. In this
paper, we introduce a decentralized Alternating Directions Method of
Multipliers (ADMM) with configurable quantization. By distributing local
objectives across nodes, our approach is highly scalable and can efficiently
reconstruct images while adapting to available resources. To overcome
communication bottlenecks, we propose two quantization techniques based on
K-means clustering and JPEG compression. Numerical experiments with benchmark
images illustrate the tradeoffs between communication efficiency, memory use,
and reconstruction accuracy.
