a-high-performance-compact-photoacoustic-tomography-system-for-vivo

A High-performance Compact Photoacoustic Tomography System for <em>In Vivo</em> Small-animal Brain Imaging

In vivo small-animal imaging has an important role to play in preclinical studies. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality ...

pulsed-laser-diode-based-desktop-photoacoustic-tomography-for

Pulsed Laser Diode-Based Desktop Photoacoustic Tomography for Monitoring Wash-In and Wash-Out of Dye in Rat Cortical Vasculature

Photoacoustic (PA) tomography (PAT) imaging is an emerging biomedical imaging modality useful in various preclinical and clinical applications. ...

Nanyang Technological University

Development of simple and computationally efficient extrapolated Tikhonov filtering reconstruction methods for photoacoustic tomography.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography.

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

Photoacoustic tomography tends to be an ill-conditioned problem with noisy limited data requiring imposition of regularization constraints, such as standard Tikhonov (ST) or total variation (TV), to reconstruct meaningful initial pressure rise distribution from the tomographic acoustic measurements acquired at the boundary of the tissue. However, these regularization schemes do not account for nonuniform sensitivity arising due to limited detector placement at the boundary of tissue as well as other system parameters. For the first time, two regularization schemes were developed within the Tikhonov framework to address these issues in photoacoustic imaging. The model resolution, based on spatially varying regularization, and fidelity-embedded regularization, based on orthogonality between the columns of system matrix, were introduced. These were systematically evaluated with the help of numerical and in-vivo mice data. It was shown that the performance of the proposed spatially varying regularization schemes were superior (with at least 2 dB or 1.58 times improvement in the signal-to-noise ratio) compared to ST-/TV-based regularization schemes.

ArticleTotal : 2	Year
Publication title Cited by 11	2017
Publication title Cited by 9	2019

Article	Year
Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography. Medical physics\| PubMed ID: 29856489	2018
Image-guided filtering for improving photoacoustic tomographic image reconstruction. Journal of biomedical optics\| PubMed ID: 29943527	2018
Spatially variant regularization based on model resolution and fidelity embedding characteristics improves photoacoustic tomography. Journal of biomedical optics\| PubMed ID: 30362308	2018

Sandeep Kumar Kalva

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