Singular Value Decomposition Processing for In vivo Cardiac Photoacoustic Imaging

Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS is prone to image quality degradation when applied to murine cardiac PAI resulting in low signal specificity in the myocardium. To address this, we propose a spatiotemporal singular value decomposition (SVD) processing method using electrocardiogram (ECG) and respiratory gated in vivo cardiac murine PAI data. SVD was applied on a two-dimensional spatiotemporal matrix generated using a three-dimensional volume of DAS beamformed complex PAI data over a cardiac cycle. The singular value spectrum (SVS) was then filtered to remove contributions from static clutter and random noise. Finally, SVD processing of beamformed images were derived using filtered SVS and inverse SVD computations. In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data using two healthy mice. Qualitative comparison with DAS shows that SVD processed images had better signal specificity and contrast. DAS and SVD processed PAI were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR) and signal-to-noise ratio (SNR). SVD processed PAI had higher CR, gCNR and SNR values compared to DAS results. For example, at the end-systolic phase for mouse 1, the SVD processed image had 100.48% higher gCNR compared to the DAS image. These results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.