The Application of Compressed Sensing for Photo-Acoustic Tomography

Abstract

Photo-acoustic (PA) imaging has been developed for different purposes, but recently, the modality has gained interest with applications to small animal imaging. As a technique it is sensitive to endogenous optical contrast present in tissues and, contrary to diffuse optical imaging, it promises to bring high resolution imaging for in vivo studies at midrange depths (3-10 mm). Because of the limited amount of radiation tissues can be exposed to, existing reconstruction algorithms for circular tomography require a great number of measurements and averaging, implying long acquisition times. Time-resolved PA imaging is therefore possible only at the cost of complex and expensive electronics. This paper suggests a new reconstruction strategy using the compressed sensing formalism which states that a small number of linear projections of a compressible image contain enough information for reconstruction. By directly sampling the image to recover in a sparse representation, it is possible to dramatically reduce the number of measurements needed for a given quality of reconstruction.

Bayesian compressed sensing in ultrasound imaging

“…Hyperparameters α0 and α1 are fixed to constant values ensuring the resulting inverse-gamma distribution is noninformative. However, it is important to mention here that we have observed in our experiments that the choice of these hyperparameters is less critical than the direct choice of λ in (2).…”

“…Note that the positions of the 1 can be completely random or adapted to UI [3]. Many algorithms are available to solve the problem (2). The optimization routine used in this paper is a non linear conjugate gradient descent algorithm, adapted to large scale problems [3].…”

“…Compressed sensing (CS) is based on the idea of sampling a signal directly into its compressed form [1]. CS has been widely used in various medical imaging domains [2]. Its applicability in UI has been recently shown in [3].…”

In vivo optical-resolution photoacoustic computed tomography with compressed sensing

“…It has been successfully applied to imaging modalities such as magnetic resonance imaging [5], computed tomography [6], and diffuse optical tomography [7]. Recently, with numerical simulation and tissue phantoms, Provost and Lesage demonstrated the feasibility to use CS for PACT [8]. Guo et al then developed CS-based PACT in vivo in the time domain [9].…”

Recovery of localised structure from signals with non-sparse components

“…These include medical imaging (for example magnetic resonance imaging [11] and computed tomography [14]), astronomical imaging [13], speech and audio signal reconstruction [9], and photo-acoustic imaging [12]. In many of these applications data acquisition time is limited so the aim is to reconstruct these signals or images from a reduced data set.…”