Physics-Based Computational Imaging in the Era of Big Data: An X-Ray Perspective
Physics-Based Computational Imaging is the process of reconstructing a physical object represented by a digital image, which is often high-dimensional (e.g., 3D, hyperspectral, movie of 3D images), by computational inversion of measured data that is not directly related to the imaged object. This inverse problem is inherently ill-posed due to the non-direct and incomplete nature of the measurements, model uncertainties, and noise. In my talk I will discuss in detail two examples of computational imaging: (1) X-ray computed tomography (CT), which is used for 3D medical imaging of humans/animals, quality control in industrial manufacturing, and security inspections, to name a few; (2) Coded-aperture x-ray coherent scatter imaging, which is a novel imaging modality proposed by Prof. Brady’s group at Duke University that allows one to identify the molecular structure of materials in security and medical applications. Traditional methods used for data inversion in most actual imaging systems employ one-shot methods, e.g., Fourier-based inversion or filtered back-projection. In recent years, there has been an increased interest in statistical iterative inversion methods based on minimizing some cost function that incorporates additional physical information about the system, the measured signals, and prior knowledge about the imaged object, which makes the inverse problem less ill-posed. Despite their many advantages, iterative methods are computationally much more expensive (require more CPU time and memory) than one-shot methods.