Abstract
Analytical (closed-form) inversion schemes have been the standard approach for image reconstruction in optoacoustic tomography due to their fast reconstruction abilities and low memory requirements. Yet, the need for quantitative imaging and artifact reduction has led to the development of more accurate inversion approaches, which rely on accurate forward modeling of the optoacoustic wave generation and propagation. In this way, multiple experimental factors can be incorporated, such as the exact detection geometry, spatio-temporal response of the transducers, and acoustic heterogeneities. The model-based inversion commonly results in very large sparse matrix formulations that require computationally extensive and memory demanding regularization schemes for image reconstruction, hindering their effective implementation in real-time imaging applications. Herein, we introduce a new discretization procedure for efficient model-based reconstructions in two-dimensional optoacoustic tomography that allows for parallel implementation on a graphics processing unit (GPU) with a relatively low numerical complexity. By on-the-fly calculation of the model matrix in each iteration of the inversion procedure, the new approach results in imaging frame rates exceeding 10 Hz, thus enabling real-time image rendering using the model-based approach.
Highlights
Much like other tomographic imaging modalities, optoacoustic tomography (OAT) relies upon a mathematical reconstruction procedure to render images of biological samples
The suggested method allows for parallel implementation on a graphics processing unit (GPU) with relatively low complexity, which is achieved by on-the-fly calculation of the model matrix in each iteration of the inversion procedure
Applicability of our methodology does not depend on the size of the model matrix as it only requires the storage of a small look-up table on the GPU memory
Summary
Much like other tomographic imaging modalities, optoacoustic tomography (OAT) relies upon a mathematical reconstruction procedure to render images of biological samples. Analytical (closed-form) inversion algorithms, such as filtered back-projection [2], may generally result in fast and memoryefficient reconstructions, model-based approaches based on numerical (or semi-analytical) inversion of an optoacoustic forward model provide extra flexibility in terms of their applicability to different types of imaging systems and samples [10], [11] In this way, one could for instance account for specific experimental and modeling imperfections, such as spatially-dependent response of the ultrasound transducers [9], [16], [17] or acoustic heterogeneities and attenuation in the sample and the surrounding medium [18]–[20]
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