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Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
Iterative image reconstruction algorithms for optoacoustic tomography (OAT),
also known as photoacoustic tomography, have the ability to improve image
quality over analytic algorithms due to their ability to incorporate accurate
models of the imaging physics, instrument response, and measurement noise.
However, to date, there have been few reported attempts to employ advanced
iterative image reconstruction algorithms for improving image quality in
three-dimensional (3D) OAT. In this work, we implement and investigate two
iterative image reconstruction methods for use with a 3D OAT small animal
imager: namely, a penalized least-squares (PLS) method employing a quadratic
smoothness penalty and a PLS method employing a total variation norm penalty.
The reconstruction algorithms employ accurate models of the ultrasonic
transducer impulse responses. Experimental data sets are employed to compare
the performances of the iterative reconstruction algorithms to that of a 3D
filtered backprojection (FBP) algorithm. By use of quantitative measures of
image quality, we demonstrate that the iterative reconstruction algorithms can
mitigate image artifacts and preserve spatial resolution more effectively than
FBP algorithms. These features suggest that the use of advanced image
reconstruction algorithms can improve the effectiveness of 3D OAT while
reducing the amount of data required for biomedical applications
LOFAR Sparse Image Reconstruction
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital
phased array interferometer with multiple antennas distributed in Europe. It
provides discrete sets of Fourier components of the sky brightness. Recovering
the original brightness distribution with aperture synthesis forms an inverse
problem that can be solved by various deconvolution and minimization methods
Aims. Recent papers have established a clear link between the discrete nature
of radio interferometry measurement and the "compressed sensing" (CS) theory,
which supports sparse reconstruction methods to form an image from the measured
visibilities. Empowered by proximal theory, CS offers a sound framework for
efficient global minimization and sparse data representation using fast
algorithms. Combined with instrumental direction-dependent effects (DDE) in the
scope of a real instrument, we developed and validated a new method based on
this framework Methods. We implemented a sparse reconstruction method in the
standard LOFAR imaging tool and compared the photometric and resolution
performance of this new imager with that of CLEAN-based methods (CLEAN and
MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse
reconstruction performs as well as CLEAN in recovering the flux of point
sources; ii) performs much better on extended objects (the root mean square
error is reduced by a factor of up to 10); and iii) provides a solution with an
effective angular resolution 2-3 times better than the CLEAN images.
Conclusions. Sparse recovery gives a correct photometry on high dynamic and
wide-field images and improved realistic structures of extended sources (of
simulated and real LOFAR datasets). This sparse reconstruction method is
compatible with modern interferometric imagers that handle DDE corrections (A-
and W-projections) required for current and future instruments such as LOFAR
and SKAComment: Published in A&A, 19 pages, 9 figure
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