Fast Large-Scale Model-Based Iterative Tomography via Exploiting Mathematical Structure, Hierarchical Optimization, Smart Initialization, and Distributed GPU Computing
This work makes high-quality MBIR practical for large-scale tomographic imaging, enabling near-real-time reconstruction for applications like in situ and time-evolving experiments, though it is incremental as it builds on prior Fourier-domain acceleration methods.
The paper tackled the high computational cost of Model-Based Iterative Reconstruction (MBIR) in tomography by introducing strategies like exploiting mathematical structure, hierarchical optimization, smart initialization, and distributed GPU computing, resulting in significantly reduced iteration counts and improved parallel efficiency for large-scale applications.
Model-Based Iterative Reconstruction (MBIR) is important because direct methods, such as Filtered Back-Projection (FBP) can introduce significant noise and artifacts in sparse-angle tomography, especially for time-evolving samples. Although MBIR produces high-quality reconstructions through prior-informed optimization, its computational cost has traditionally limited its broader adoption. In previous work, we addressed this limitation by expressing the Radon transform and its adjoint using non-uniform fast Fourier transforms (NUFFTs), reducing computational complexity relative to conventional projection-based methods. We further accelerated computation by employing a multi-GPU system for parallel processing. In this work, we further accelerate our Fourier-domain framework, by introducing four main strategies: (1) a reformulation of the MBIR forward and adjoint operators that exploits their multi-level Toeplitz structure for efficient Fourier-domain computation; (2) an improved initialization strategy that uses back-projected data filtered with a standard ramp filter as the starting estimate; (3) a hierarchical multi-resolution reconstruction approach that first solves the problem on coarse grids and progressively transitions to finer grids using Lanczos interpolation; and (4) a distributed-memory implementation using MPI that enables near-linear scaling on large high-performance computing (HPC) systems. Together, these innovations significantly reduce iteration counts, improve parallel efficiency, and make high-quality MBIR reconstruction practical for large-scale tomographic imaging. These advances open the door to near-real-time MBIR for applications such as in situ, in operando, and time-evolving experiments.