Photoacoustic speckle tracking for motion estimation and flow analysis

An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences

Deep learning is heavily being borrowed to solve problems in medical imaging applications, and Siamese neural networks are the front runners of motion tracking. In this article, we propose to upgrade one such Siamese architecture-based neural network for robust and accurate landmark tracking in ultrasound images to improve the quality of image-guided radiation therapy. Although several researchers have improved the Siamese architecture-based networks with sophisticated detection modules and by incorporating transfer learning, the inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We employ a reference template update to resolve the constant position model and a linear Kalman filter (LKF) to address the missing motion model. Moreover, we demonstrate that the proposed architecture provides promising results without transfer learning. The proposed model was submitted to an open challenge organized by MICCAI and was evaluated exhaustively on the Liver US Tracking (CLUST) 2D dataset. Experimental results proved that the proposed model tracked the landmarks with promising accuracy. Furthermore, we also induced synthetic occlusions to perform a qualitative analysis of the proposed approach. The evaluations were performed on the training set of the CLUST 2D dataset. The proposed method outperformed the original Siamese architecture by a significant margin.

Event-based laser speckle correlation for micro motion estimation

Micro motion estimation has important applications in various fields such as microfluidic particle detection and biomedical cell imaging. Conventional methods analyze the motion from intensity images captured using frame-based imaging sensors such as the complementary metal-oxide semiconductor (CMOS) and the charge-coupled device (CCD). Recently, event-based sensors have evolved with the special capability to record asynchronous light changes with high dynamic range, high temporal resolution, low latency, and no motion blur. In this Letter, we explore the potential of using the event sensor to estimate the micro motion based on the laser speckle correlation technique.

Dynamic laser speckle analysis using the event sensor

Dynamic laser speckle analysis (DLSA) can obtain useful information about the scene dynamics. Traditional implementations use intensity-based imaging sensors such as a complementary metal oxide semiconductor and charge-coupled device to capture time-varying intensity frames. We use an event sensor that measures pixel-wise asynchronous brightness changes to record speckle pattern sequences. Our approach takes advantage of the low latency and high contrast sensitivity of the event sensor to implement DLSA with high temporal resolution. We also propose two evaluation metrics designed especially for event data. Comparison experiments are conducted in identical conditions to demonstrate the feasibility of our proposed approach.

Analysis of low-scattering regions in optical coherence tomography: applications to neurography and lymphangiography

Analysis of semi-transparent low scattering biological structures in optical coherence tomography (OCT) has been actively pursued in the context of lymphatic imaging, with most approaches relying on the relative absence of signal as a means of detection. Here we present an alternate methodology based on spatial speckle statistics, utilizing the similarity of a distribution of given voxel intensities to the power distribution function of pure noise, to visualize the low-scattering biological structures of interest. In a human tumor xenograft murine model, we show that these correspond to lymphatic vessels and nerves; extensive histopathologic validation studies are reported to unequivocally establish this correspondence. The emerging possibility of OCT lymphangiography and neurography is novel and potentially impactful (especially the latter), although further methodology refinement is needed to distinguish between the visualized lymphatics and nerves.