Model based image reconstruction with physics based priors

Computed tomography is increasingly enabling scientists to study physical processes of materials at micron scales. The MBIR framework provides a powerful method for CT reconstruction by incorporating both a measurement model and prior model. Classically, the choice of prior has been limited to models enforcing local similarity in the image data. In some material science problems, however, much more may be known about the underlying physical process being imaged. Moreover, recent work in Plug-And-Play decoupling of the MBIR problem has enabled researchers to look beyond classical prior models, and innovations in methods of data acquisition such as interlaced view sampling have also shown promise for imaging of dynamic physical processes. In this paper, we propose an MBIR framework with a physics based prior model - namely the Cahn-Hilliard equation. The Cahn-Hilliard equation can be used to describe the spatiotemporal evolution of binary alloys. After formulating the MBIR cost with Cahn-Hilliard prior, we use Plug-And-Play algorithm with ICD optimization to minimize this cost. We apply this method to simulated data using the interlaced-view sampling method of data acquisition. Results show superior reconstruction quality compared to the Filtered Back Projection. Though we use Cahn-Hilliard equation as one instance, the method can be easily extended to use any other physics-based prior model for a different set of applications.