Image Deconvolution with Hybrid Reweighted Adaptive Total Variation (HRATV) for Optoacoustic Tomography

Abstract

Optoacoustic tomography (OAT) is a hybrid biomedical imaging modality that usually employs a transducer array to detect laser-generated ultrasonic signals. The reconstructed image suffers low contrast and degraded resolution due to the limited bandwidth and the spatial directivity of the transducer element. Here, we introduce a modified image deconvolution method with a hybrid reweighted adaptive total variation tailored to improve the image quality of OAT. The effectiveness and the parameter dependency of the proposed method are verified on standard test images. The performance of the proposed method in OAT is then characterized on both simulated phantoms and in vivo mice experiments, which demonstrates that the modified deconvolution algorithm is able to restore the sharp edges and fine details in OAT simultaneously. The signal-to-noise ratios (SNRs) of the target structures in mouse liver and brain were improved by 4.90 and 12.69 dB, respectively. We also investigated the feasibility of using Fourier ring correlation (FRC) as an indicator of the image quality to monitor the deconvolution progress in OAT. Based on the experimental results, a practical guide for image deconvolution in OAT was summarized. We anticipate that the proposed method will be a promising post-processing tool to enhance the visualization of micro-structures in OAT.