Separating single- and multiple-scattering components in laser speckle contrast imaging of tissue blood flow

Dual-Channel in Spatial-Frequency Domain CycleGAN for perceptual enhancement of transcranial cortical vascular structure and function

Observing cortical vascular structures and functions using laser speckle contrast imaging (LSCI) at high resolution plays a crucial role in understanding cerebral pathologies. Usually, open-skull window techniques have been applied to reduce scattering of skull and enhance image quality. However, craniotomy surgeries inevitably induce inflammation, which may obstruct observations in certain scenarios. In contrast, image enhancement algorithms provide popular tools for improving the signal-to-noise ratio (SNR) of LSCI. The current methods were less than satisfactory through intact skulls because the transcranial cortical images were of poor quality. Moreover, existing algorithms do not guarantee the accuracy of dynamic blood flow mappings. In this study, we develop an unsupervised deep learning method, named Dual-Channel in Spatial-Frequency Domain CycleGAN (SF-CycleGAN), to enhance the perceptual quality of cortical blood flow imaging by LSCI. SF-CycleGAN enabled convenient, non-invasive, and effective cortical vascular structure observation and accurate dynamic blood flow mappings without craniotomy surgeries to visualize biodynamics in an undisturbed biological environment. Our experimental results showed that SF-CycleGAN achieved a SNR at least 4.13 dB higher than that of other unsupervised methods, imaged the complete vascular morphology, and enabled the functional observation of small cortical vessels. Additionally, the proposed method showed remarkable robustness and could be generalized to various imaging configurations and image modalities, including fluorescence images, without retraining.

Improvements and validation of spatiotemporal speckle correlation model for rolling shutter speckle imaging

Rolling shutter speckle imaging (RSSI) is a single-shot imaging technique that directly measures the temporal dynamics of the scattering media using a low-cost rolling shutter image sensor and vertically elongated speckles. In this paper, we derive and validate a complete spatiotemporal intensity correlation (STIC) model for RSSI, which describes the row-by-row correlation of the dynamic speckles measured with a rolling shutter in the presence of static scattering. Our new model accounts for the finite exposure time of the detector, which can be longer than the sampling interval in RSSI. We derive a comprehensive model that works for all correlation times of rolling shutter measurements. As a result, we can correctly utilize all data points in RSSI, which improves the measurement accuracy and ranges of speckle decorrelation time and dynamic scattering fraction, as demonstrated by phantom experiments. With simulations and experiments, we provide an understanding of the design parameters of RSSI and the measurement range of the speckle dynamics.

Laser speckle contrast imaging based on spatial frequency domain filtering

We proposed a novel method to separate static and dynamic speckles based on spatial frequency domain filtering. First, the raw speckle image sequence is processed frame by frame through 2D Fourier transform, low-pass and high-pass filtering in the spatial frequency domain, and inverse Fourier transform. Then, we can obtain low- and high-frequency image sequences in the spatial domain. Second, we averaged both sequences in the time domain. After the above processing, we obtain the mean intensities of the dynamic and static speckle components in the spatial domain. Finally, we calculated the time-averaged modulation depth to map the 2-D blood flow distribution. Both phantom and vivo experiments demonstrated that the proposed method could effectively suppress the background non-uniformity and has the advantage of high computational efficiency. It also can effectively improve image contrast, contrast-to-noise ratio, and imaging dynamic range.

Random matrix-based laser speckle contrast imaging enables quasi-3D blood flow imaging in laparoscopic surgery

Laser speckle contrast imaging (LSCI) provides full-field and label-free imaging of blood flow and tissue perfusion. It has emerged in the clinical environment, including the surgical microscope and endoscope. Although traditional LSCI has been improved in resolution and SNR, there are still challenges in clinical translations. In this study, we applied a random matrix description for the statistical separation of single and multiple scattering components in LSCI using a dual-sensor laparoscopy. Both in-vitro tissue phantom and in-vivo rat experiments were performed to test the new laparoscopy in the laboratory environment. This random matrix-based LSCI (rmLSCI) provides the blood flow and tissue perfusion in superficial and deeper tissue respectively, which is particularly useful in intraoperative laparoscopic surgery. The new laparoscopy provides the rmLSCI contrast images and white light video monitoring simultaneously. Pre-clinical swine experiment was also performed to demonstrate the quasi-3D reconstruction of the rmLSCI method. The quasi-3D ability of the rmLSCI method shows more potential in other clinical diagnostics and therapies using gastroscopy, colonoscopy, surgical microscope, etc.