Today we have two papers on the utilisation of compressive sensing in hardware and sensors. The first one is initially mathematical in nature but does address some of the noise issue of EM and even takes a look at tomography. The second paper is behind paywall so I added two other papers on the same subject that go deeper in the explaining the technique.
Compressed Sensing and Electron Microscopy by Peter Binev, Wolfgang Dahmen, Ronald DeVore, Philipp Lamby, Daniel Savu, and Robert Sharpley. The abstract reads:
Compressed Sensing (CS) is a relatively new approach to signal acquisition which has as its goal to minimize the number of measurements needed of the signal in order to guarantee that it is captured to a prescribed accuracy. It is natural to inquire whether this new subject has a role to play in Electron Microscopy (EM). In this paper, we shall describe the foundations of Compressed Sensing and then examine which parts of this new theory may be useful in EM.
In vivo optical-resolution photoacoustic computed tomography with compressed sensing by Jing Meng, Lihong V. Wang, Dong Liang, and Liang Song. The abstract reads:
Optical-resolution photoacoustic microscopy is becoming a powerful research tool for studying microcirculation in vivo. Moreover, ultrasonic-array-based optical-resolution photoacoustic computed tomography (OR-PACT), providing comparable resolution at an improved speed, has opened up new opportunities for studying microvascular dynamics. In this Letter, we have developed a compressed sensing with partially known support (CS-PKS) photoacoustic reconstruction strategy for OR-PACT. Compared with conventional backprojection reconstruction, the CS-PKS strategy was shown to produce high-quality in vivo OR-PACT images with threefold less measurement data, which can be leveraged to improve the data acquisition speed and costs of OR-PACT systems.
Multiscale photoacoustic microscopy and computed tomography by Lihong V. Wang. The abstract reads:
Photoacoustic tomography (PAT) is probably the fastest-growing area of biomedical imaging technology, owing to its capacity for high-resolution sensing of rich optical contrast in vivo at depths beyond the optical transport mean free path (~1 mm in human skin). Existing high-resolution optical imaging technologies, such as confocal microscopy and two-photon microscopy, have had a fundamental impact on biomedicine but cannot reach the penetration depths of PAT. By utilizing low ultrasonic scattering, PAT indirectly improves tissue transparency up to 1000-fold and consequently enables deeply penetrating functional and molecular imaging at high spatial resolution. Furthermore, PAT promises in vivo imaging at multiple length-scales; it can image subcellular organelles to organs with the same contrast origin — an important application in multiscale systems biology research.
Compressed sensing in photoacoustic tomography in vivo by Zijian Guo, Changhui Li, Liang Song, Lihong V. Wang. The abstract reads:
The data acquisition speed in photoacoustic computed tomography PACT is limited by the laser repetition rate and the number of parallel ultrasound detecting channels. Reconstructing an image with fewer measurements can effectively accelerate the data acquisition and reduce the system cost. We adapt compressed sensing CS for the reconstruction in PACT. CS-based PACT is implemented as a nonlinear conjugate gradient descent algorithm and tested with both phantom and in vivo experiments.
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