Co-registration method for photoacoustic imaging and laser speckle imaging

Abstract

Combining photoacoustic (PA) imaging with laser speckle (LS) imaging (LSI) can simultaneously determine total hemoglobin concentration (HbT), hemoglobin oxygen saturation (SO2), and blood flow rates. Thus, the co-registration of PA and LS images is important in physiological studies and pathological diagnosis. This letter presents a co-registration algorithm combining mutual information with the maximum betweenclass variance segmentation method (Otsu method). The mutual information and Otsu method are used to provide the registration measure criterion and image feature recognition, respectively. The evaluation results show that the registration function possesses a single maximum peak and high smoothness across the global co-registration district, indicating a robust behavior. Moreover, this method has good registration accuracy, and the fusion result simultaneously visualizes the separate functional information of two kinds of images.OCIS codes: 110.6150, 110.5120, 100.2000codes: 110.6150, 110.5120, 100. , 170.2655 Photoacoustic (PA) imaging (PAI) is a rapidly emerging, noninvasive imaging method. It visualizes the structural and functional characteristics of biological tissues by ultrasonically detecting their optical absorption contrast of these tissues in vivo, breaking highly optical scattering limitations and achieving super-depth high-resolution optical imaging [1−3] . Thus, the method has been widely applied in monitoring angiogenesis, melanoma, hemoglobin oxygen saturation (SO2) [4] , and total hemoglobin concentration (HbT) [1,2] . Laser speckle (LS) imaging (LSI), a no-scanning, noninvasive imaging tool, could achieve highly spatial (tens of microns) and temporal (millisecond) resolutions for imaging biological tissues in vivo [5,6] . The temporal and spatial statistics of speckle pattern intensity fluctuations contain the movement information of the object observed. Local velocity distributions should be measured properly by analyzing local speckle contrast variations [6] . Thus, LSI is always used for monitoring capillary blood flow rates in the skin and cerebral blood flow rates [6−8] . Combining PA and LS images could simultaneously provide HbT, SO2, and capillary blood flow information. The multimodality image coregistration technology is the foundation of fusion, which is significant for understanding the normal and pathophysiological conditions of neurovascular, metabolic, and hemodynamic interactions [9,10] . These multimodality coregistration methods can be classified as either extrinsic, intrinsic (including landmarks, segmentation-and mutual-information-based registration), or non-imagebased registration methods [11,12] . The mutual information method does not need assumptions and limiting constraints for the image content. Thus, it is a very general and powerful criterion for multimodality image coregistration. However, mutual information is only sensitive to gray-level distribution and does not include spatial information. Therefore, it fails in some multimodality co-registration method...

High total variation-based method for sparse-view photoacoustic reconstruction

“…Photoacoustic imaging (PAI) is a noninvasive medical imaging technique that has a great potential for clinic applications such as early tumor detection [1,2] , vessel imaging [3,4] , and brain imaging [5] . PAI technique can provide higher contrast [6] and resolution than ultrasound imaging and is more effective for imaging deeper structure compared with pure optical imaging. It combines the strengths of optical and ultrasound imaging [7] .…”