A practical guide for model-based reconstruction in optoacoustic imaging

Abstract

Optoacoustic (OA, photoacoustic) imaging capitalizes on the low scattering of ultrasound within biological tissues to provide optical absorption-based contrast with high resolution at depths not reachable with optical microscopy. For deep tissue imaging applications, OA image formation commonly relies on acoustic inversion of time-resolved tomographic data. The excitation of OA responses and subsequent propagation of ultrasound waves can be mathematically described as a forward model enabling image reconstruction via algebraic inversion. These model-based reconstruction methods have been shown to outperform alternative inversion approaches and can further render OA images from incomplete datasets, strongly distorted signals or other suboptimally recorded data. Herein, we provide a general perspective on model-based OA reconstruction methods, review recent progress, and discuss the performance of the different algorithms under practical imaging scenarios.

Full-view LED-based optoacoustic tomography

“…4 b), the model-based reconstruction schemes offer an enhanced image quality and higher degree of image reconstruction accuracy ( Fig. 4 c) [46] , [47] , [48] . The presence of circular ring artifacts in cross-sectional finger images, caused by inherent electrical noise in the FLOAT system, can be effectively reduced by implementing a notch filtering in the signal domain ( Fig.…”

Photoacoustic imaging on its way toward clinical utility: a tutorial review focusing on practical application in medicine

“…68 This method can deliver very high-quality images but is computationally inefficient and which limits its utility for real-time imaging applications. Model-based methods formulate image formation as an optimization problem in which the chromophore distribution is optimized until its simulated PA response matches the recorded pressure p. 69 Model-based methods can be applied to regularize image reconstruction in limited-view geometries, producing high-resolution images with a reduced artifact level from incomplete PA data. [70][71][72] Adaptive beamforming methods substantially outperform conventional beamforming methods in terms of resolution and CNR by locally minimizing the noise in the image.…”

“…Model-based methods formulate image formation as an optimization problem in which the chromophore distribution is optimized until its simulated PA response matches the recorded pressure . 69 Model-based methods can be applied to regularize image reconstruction in limited-view geometries, producing high-resolution images with a reduced artifact level from incomplete PA data. 70 – 72 …”

Recent advances in deep-learning-enhanced photoacoustic imaging

“…Model-based iterative optimization methods have been developed to address this issue and provide more accurate solutions 117 . But these methods are time-consuming and sensitive to quantification errors 118 . A new approach called eigenspectral multispectral optoacoustic tomography (eMSOT) has been proposed to improve qPAI accuracy 116 .…”