Real-Time in Vivo Photoacoustic Imaging in the Assessment of Myocardial Dynamics in Murine Model of Myocardial Ischemia

Mukaddim, Rashid Al; Rodgers, Allison C.; Hacker, Timothy A.; Heinmiller, Andrew; Varghese, Tomy

doi:10.1016/j.ultrasmedbio.2018.05.021

Cited by 25 publications

(23 citation statements)

References 43 publications

(64 reference statements)

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“…The proposed SVD processing is a data-driven approach where spatiotemporal characteristics of cardiac PAI are utilized to enhance signal contribution from myocardial tissue under the following assumptions based on literature findings and experimental observations. First, highly absorbing blood inside the coronary artery (murine arterial oxygen saturation

to 95% 11 , 16 ) having low blood flow velocity (diastolic coronary flow velocity

46 ) should contribute to the PA signals from myocardial tissue at 850 nm. Second, highly scattering mice skin and muscle due to the presence of connective tissues and anisotropic layers of collagen, 47 having lower optical absorption coefficients at 850 nm (for example, male BALB/CJ mice skin optical absorption coefficient at

47 ) compared to oxygenated blood, should result in low amplitude PA signals compared to myocardial tissue.…”

Section: Discussionmentioning

confidence: 99%

“…The skin surface of the mice was placed at an approximate depth of 8 mm whenever possible to avoid reverberation artifacts from the skin. 11 , 37 A cine loop of US B-mode was collected to confirm normal cardiac function for each mouse. Then, 1000 frames of co-registered beamformed US and pre-beamformed PA channel data were acquired using an optical wavelength of 850 nm where oxygenated hemoglobin has dominant absorption 38 with simultaneous acquisition of ECG and respiratory signals.…”

Section: Methodsmentioning

confidence: 99%

“… 1 Application of PAI has been demonstrated in both clinical (e.g., carotid atherosclerosis, 2 breast mass differentiation, 3 melanoma detection, 4 and cardiac catheter intervention 5 ) and pre-clinical settings (e.g., oncology research, 6 surgical guidance, 7 – 9 and prostate brachytherapy 10 ). PAI can also be utilized non-invasively to evaluate blood oxygenation in myocardial tissue, 11 , 12 which can potentially complement existing preclinical (e.g., murine models of ischemia-reperfusion 13 ) cardiac imaging methods such US echocardiography, speckle tracking echocardiography, 14 and cardiac elastography 15 by providing unique molecular information.…”

Section: Introductionmentioning

confidence: 99%

“…PAI has been used to describe myocardial blood oxygenation information utilizing high persistence (multiple frame averaging) to increase the signal-to-noise ratio (SNR) of myocardial wall PA signals. 11 , 16 This PAI technique leads to reduced sensitivity and resolution (both spatial and temporal) in cardiac photoacoustic (PA) images because of the potential for averaging PA signals from multiple sources [i.e., myocardial tissue, blood in left-ventricular (LV) chamber, and surrounding static muscle tissue] due to the presence of natural cardiac deformation, thus corrupting blood oxygenation quantification in the myocardium. Therefore, avoiding frame averaging is desirable to improve spatial and temporal resolution, and sensitivity to small variations in PA signals from the myocardial wall.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

et al. 2021

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. 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.

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to 95% 11 , 16 ) having low blood flow velocity (diastolic coronary flow velocity

47 ) compared to oxygenated blood, should result in low amplitude PA signals compared to myocardial tissue.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

et al. 2021

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“…[115][116][117][118][119][120] This noninvasive and label-free vasculature imaging approach has been utilized to study existing disease conditions, monitor therapeutic progress, and diagnose clinical conditions. Vascular imaging with PA has been successfully used in diagnosis and assessment of cancer, [121][122][123] cardiovascular diseases, 124 microvascular abnormalities, 120,125,126 and superficial soft tissue damages. 127 Vascularization is crucial for engineering tissues of sufficient volume for clinical impact.…”

Section: Vascular Imagingmentioning

confidence: 99%

Photoacoustic Imaging in Tissue Engineering and Regenerative Medicine

Shrestha

DeLuna

Anastasio

et al. 2020

Tissue Engineering Part B: Reviews

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Several imaging modalities are available for investigation of the morphological, functional, and molecular features of engineered tissues in small animal models. While research in tissue engineering and regenerative medicine (TERM) would benefit from a comprehensive longitudinal analysis of new strategies, researchers have not always applied the most advanced methods. Photoacoustic imaging (PAI) is a rapidly emerging modality that has received significant attention due to its ability to exploit the strong endogenous contrast of optical methods with the high spatial resolution of ultrasound methods. Exogenous contrast agents can also be used in PAI for targeted imaging. Applications of PAI relevant to TERM include stem cell tracking, longitudinal monitoring of scaffolds in vivo, and evaluation of vascularization. In addition, the emerging capabilities of PAI applied to the detection and monitoring of cancer and other inflammatory diseases could be exploited by tissue engineers. This article provides an overview of the operating principles of PAI and its broad potential for application in TERM.

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In‐vivo hemodynamic imaging of acute prenatal ethanol exposure in fetal brain by photoacoustic tomography

Shan

Zhao

Jiang

et al. 2020

Journal of Biophotonics

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Prenatal ethanol exposure (PEE) can lead to structural and functional abnormalities in fetal brain. Although neural developmental deficits due to PEE have been recognized, the immediate effects of PEE on fetal brain vasculature and hemodynamics remain poorly understood. One of the major obstacles that preclude the rapid advancement of studies on fetal vascular dynamics is the limitation of the imaging techniques. Thus, a technique for noninvasive in-vivo imaging of fetal vasculature and hemodynamics is desirable. In this study, we explored the dynamic changes of the vessel dimeter, density and oxygen saturation in fetal brain after acute maternal ethanol exposure in the second-trimester equivalent murine model using a real-time photoacoustic tomography system we developed for imaging embryo of small animals. The results indicate a significant decrease in fetal brain vessel diameter, perfusion and oxygen saturation. This work demonstrated that PAT can provide highresolution noninvasive imaging ability to monitor fetal vascular dynamics. K E Y W O R D Sacute prenatal ethanol exposure, fetal alcohol spectrum disorder, fetal brain vasculature, hemodynamics, real-time photoacoustic tomography

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Real-Time in Vivo Photoacoustic Imaging in the Assessment of Myocardial Dynamics in Murine Model of Myocardial Ischemia

Cited by 25 publications

References 43 publications

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

Photoacoustic Imaging in Tissue Engineering and Regenerative Medicine

In‐vivo hemodynamic imaging of acute prenatal ethanol exposure in fetal brain by photoacoustic tomography

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