PyTomography: A Python Library for Quantitative Medical Image Reconstruction

Abstract

Background: There is a scarcity of open-source libraries in medical imaging dedicated to both (i) the development and deployment of novel reconstruction algorithms and (ii) support for clinical data. Purpose: To create and evaluate a GPU-accelerated, open-source, and user-friendly image reconstruction library, designed to serve as a central platform for the development, validation, and deployment of novel tomographic reconstruction algorithms. Methods: PyTomography was developed using Python and inherits the GPU-accelerated functionality of PyTorch for fast computations. The software uses a modular design that decouples the system matrix from reconstruction algorithms, simplifying the process of integrating new imaging modalities or developing novel reconstruction techniques. As example developments, SPECT reconstruction in PyTomography is validated against both vendor-specific software and alternative open-source libraries. Bayesian reconstruction algorithms are implemented and validated. Results: PyTomography is consistent with both vendor-software and alternative open source libraries for standard SPECT clinical reconstruction, while providing significant computational advantages. As example applications, Bayesian reconstruction algorithms incorporating anatomical information are shown to outperform the traditional ordered subset expectation maximum (OSEM) algorithm in quantitative image analysis. PSF modeling in PET imaging is shown to reduce blurring artifacts. Conclusions: We have developed and publicly shared PyTomography, a highly optimized and user-friendly software for quantitative image reconstruction of medical images, with a class hierarchy that fosters the development of novel imaging applications.Comment: 26 pages, 7 figure