Dr. Alessandro Perelli

University of Edinburgh - Institute for Digital Communications (IDCOM)

Compressive Computed Tomography Image Reconstruction with Denoising Message Passing Algorithms

Abstract:

X-ray Computed Tomography (CT) is widely used as 3-D imaging technique in materials science or for medical applications such as the reconstruction of inner organs of patients.
In this talk we address the compressive reconstruction of images from a limited number of projections in order to reduce the X-ray radiation dose in CT while achieving high diagnostic performances. The objective is to study the feasibility of applying message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction extending the algorithm from its theoretical domain of i.i.d. random matrices. An extension of the Approximate Message Passing (AMP) method for linear observation systems with deterministic measurement matrix is proposed; AMP refers to an iterative signal reconstruction algorithm based on the Gibbs free energy optimization that performs scalar denoising within each iteration. Exploiting the intuition of employing a generic denoiser in a CS reconstruction algorithm, we develop a denoising-based Turbo CS algorithm (D-Turbo) and we extend the application of the denoising approximate message passing (D-AMP) algorithm to partial Radon Projection data. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser yielding an improvement in the reconstruction quality compared to existing direct and iterative methods.

Biography:

Alessandro Perelli received the Bachelor of Science and Master of
Science in Electronic Engineering respectively in 2007 and 2010 from
the Universita' Politecnica delle Marche, Italy and the Ph.D. in
Electronic Engineering from the University of Bologna, Italy, in 2014.
He has been a visiting research scholar at the Ultrasound Group of
University of Leeds, UK, from 2012 to 2013. Since July 2014, he is a
Research Associate at the Institute of Digital Communications
(IDCOM) University of Edinburgh.
His current activity focuses on image processing, compressive
sensing, optimization techniques for computed tomography. Another
area of interest concerns the development of signal processing
algorithms for guided wave-based nondestructive and monitoring
applications.