Model-Based Optoacoustic Tomography Image Reconstruction With Non-local and Sparsity Regularizations

Optoacoustic tomography (OAT) is an emerging imaging modality with ultrasonic imaging depth and optical contrast. The reconstruction of optoacoustic image is to recover the initial acoustic pressure distribution of the object from a set of ultrasound signals. The model-based optoacoustic tomography image reconstruction is an ill-conditioned inverse problem affected by factors such as limited detection angle, imperfect imaging model, and noise. Accounting for this, appropriate penalties should be incorporated into the reconstruction process to improve image quality. In this paper, we present a new dual-constraint OAT imaging model involving a combination of non-local means filtering and sparse coding, with the former to preserve image details by self-similarity and the latter to enforce sparsity. A two-step optimization algorithm and an iterative parameter tuning method were proposed to ensure accurate solution. By comparing to other existing regularization approaches in both numerical simulation and in vivo animal imaging studies, the new method showed improved image quality in terms of signal to noise ratio and contrast enhancement.