Improvement of LED‐based photoacoustic imaging using lag‐coherence factor (LCF) beamforming

Abstract

AbstractBackgroundOwing to its portability, affordability, and energy‐efficiency, LED‐based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser‐based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED‐based PA imaging is not optimal, especially in sub‐surface vascular imaging applications.PurposeCombination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED‐based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay‐and‐sum (DAS) is the most common beamforming algorithm in linear array‐based PA detection. Side‐lobes and reconstruction‐related artifacts make the DAS performance unsatisfactory and poor for a clinical‐implementation. In this work, we explored a new weighting‐based image processing technique for LED‐based PAs to yield improved image quality when compared to the traditional methods.MethodsWe are proposing a lag‐coherence factor (LCF), which is fundamentally based on the combination of the spatial auto‐correlation of the detected PA signals. In LCF, the numerator contains lag‐delay‐multiply‐and‐sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto‐correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue‐mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED‐based PA imaging system.ResultsOur novel correlation‐based weighting technique showed LED‐based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side‐lobes and artifacts when compared to the DAS and coherence‐factor (CF) approaches. Signal‐to‐noise ratio, generalized contrast‐to‐noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal‐to‐noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DAS algorithm.ConclusionsOur results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal‐to‐noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED‐based PA imaging and thus accelerate its clinical translation.