No abstract

Photoacoustic imaging (PAI) is an evolving real-time imaging modality that combines the higher contrast of optical imaging with the higher spatial resolution of ultrasound imaging. We utilized dual-wavelength PAI for the diagnosis and monitoring of myocardial ischemia by assessing variations in blood oxygen saturation estimated in a murine model. The use of high-frequency ultrasound in conjunction with PAI enabled imaging of anatomic and functional changes associated with ischemia. Myocardial ischemia was established in eight mice by ligating the left anterior descending artery (LAD). Longitudinal results reveal that PAI is sensitive to acute myocardial ischemia, with a rapid decline in blood oxygen saturation (p ˂ 0.001) observed after LAD ligation (30 min: 33.05 ± 6.80%, 80 min: 36.59 ± 5.22%, 120 min: 36.70 ± 9.46%, 24 h: 40.55 ± 13.04%) compared with baseline (87.83 ± 5.73%). Variation in blood oxygen saturation was found to be linearly correlated with ejection fraction (%), fractional shortening (%) and stroke volume (µL), with Pearson's correlation coefficient values of 0.66, 0.67 and 0.77, respectively (p ˂ 0.001). Our results indicate that PAI has the potential for real-time diagnosis and monitoring of acute myocardial ischemia.

Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p < 0.001). Contrast-to-noise (CNR e ) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNR e of −0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p < 0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

No abstract

No abstract

No abstract

. Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity. Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI. Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations. Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance. Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

No abstract