Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation.

ABSTRACT: Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-crafted parameters to achieve good performance. Many iterations are usually required to reconstruct a high-quality image, which is computationally expensive due to repeated evaluations of the physical model. While learned iterative reconstruction approaches such as model-based learning (MBLr) can reduce the number of iterations through convolutional neural networks, it still requires repeated evaluations of the physical models at each iteration. Therefore, the goal of this study is to develop a Fast Iterative Reconstruction (FIRe) algorithm that incorporates

SUBMITTER: Hsu KT 

PROVIDER: S-EPMC9867977 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature