Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans

In the clinical photoacoustic (PA) imaging, ultrasound (US) array transducers are typically used to provide B-mode images in real-time. To form a B-mode image, delay-and-sum (DAS) beamforming algorithm is the most commonly used algorithm because of its ease of implementation. However, this algorithm suffers from low image resolution and low contrast drawbacks. To address this issue, delay-multiply-and-sum (DMAS) beamforming algorithm has been developed to provide enhanced image quality with higher contrast, and narrower main lobe compared but has limitations on the imaging speed for clinical applications. In this paper, we present an enhanced real-time DMAS algorithm with modified coherence factor (CF) for clinical PA imaging of humans in vivo. Our algorithm improves the lateral resolution and signal-to-noise ratio (SNR) of original DMAS beam-former by suppressing the background noise and side lobes using the coherence of received signals. We optimized the computations of the proposed DMAS with CF (DMAS-CF) to achieve real-time frame rate imaging on a graphics processing unit (GPU). To evaluate the proposed algorithm, we implemented DAS and DMAS with/without CF on a clinical US/PA imaging system and quantitatively assessed their processing speed and image quality. The processing time to reconstruct one B-mode image using DAS, DAS with CF (DAS-CF), DMAS, and DMAS-CF algorithms was 7.5, 7.6, 11.1, and 11.3 ms, respectively, all achieving the real-time imaging frame rate. In terms of the image quality, the proposed DMAS-CF algorithm improved the lateral resolution and SNR by 55.4% and 93.6 dB, respectively, compared to the DAS algorithm in the phantom imaging experiments. We believe the proposed DMAS-CF algorithm and its real-time implementation contributes significantly to the improvement of imaging quality of clinical US/PA imaging system.11Ysciescopu

Delay-multiply-and-sum based synthetic aperture focusing in photoacoustic microscopy

We propose an improved version of a synthetic aperture focusing technique (SAFT) based on a delay-multiply-and-sum algorithm for acoustic-resolution photoacoustic microscopy (AR-PAM). In this method, the photoacoustic (PA) signals from multiple scan-lines are combinatorially coupled, multiplied, and then summed. This process can be considered a correlation operation of the PA signals in each scan-line, so the spatial coherent information between the PA signals can be efficiently extracted. By applying this method in conventional AR-PAM, lateral resolution and signal-to-noise ratio in out-of-focus regions are much improved compared with those estimated from the previously developed SAFT, respectively, thereby achieving the extension of the imaging focal region. Our phantom and in vivo imaging experiments prove the validity of our proposed method. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)open11413Nsciescopu

Deep learning-based speed of sound aberration correction in photoacoustic images

Beamforming algorithms are widely used for photoacoustic (PA) imaging to reconstruct the initial pressure map. In the reconstruction process, they typically assumed that the imaged biological tissue was a homogeneous medium. However, as biological tissue is generally heterogeneous, the misassumption causes suboptimal image reconstruction. Because it is difficult to predict the heterogeneity of a medium, it was still common to reconstruct images assuming a uniform medium. To solve this problem, we introduce a deep learning-based algorithm that can correct the speed of sound (SoS) aberration in the PA image. We trained a neural network with the multiple simulation datasets and successfully corrected SoS aberrations in a PA in vivo image of the human forearm. We observed that the proposed algorithm effectively suppressed side lobes and noise in the PA image and greatly improves image quality.1

Two-Dimensional Directional Synthetic Aperture Focusing Technique using Acoustic-Resolution Photoacoustic Microscopy.

Photoacoustic microscopy (PAM) is a hybrid imaging technology using optical illumination and acoustic detection. PAM is divided into two types: optical-resolution PAM (OR-PAM) and acoustic-resolution photoacoustic microscopy (AR-PAM). Among them, AR-PAM has a great advantage in the penetration depth compared to OR-PAM because AR-PAM relies on the acoustic focus, which is much less scattered in biological tissue than optical focus. However, because the acoustic focus is not as tight as the optical focus with a same numerical aperture (NA), the AR-PAM requires acoustic NA higher than optical NA. The high NA of the acoustic focus produces good image quality in the focal zone, but significantly degrades spatial resolution and signal-to-noise ratio (SNR) in the out-of-focal zone. To overcome the problem, synthetic aperture focusing technique (SAFT) has been introduced. SAFT improves the degraded image quality in terms of both SNR and spatial resolution in the out-of-focus zone by calculating the time delay of the corresponding signals and combining them. To extend the dimension of correction effect, several 2D SAFTs have been introduced, but there was a problem that the conventional 2D SAFTs cannot improve the degraded SNR and resolution as 1D SAFT can do. In this study, we proposed a new 2D SAFT that can compensate the distorted signals in x and y directions while maintaining the correction performance as the 1D SAFT.1

Multiscale Photoacoustic Microscopy Imaging with Image Improvement and Quantification Technique

Photoacoustic (PA) imaging is one of the fastest growing imaging technologies nowadays in both research and clinical applications, especially due to its unique capability to visualize blood vessels. The PA microscopy (PAM) is classified into two types: optical-resolution PAM (OR-PAM) and acoustic-resolution PAM (AR-PAM). OR-PAM image has a point spread function (PSF) much smaller than AR-PAM because it uses a tightly focused optical beam and the PSF is determined by the optical focus. In contrast, AR-PAM uses an unfocused optical illumination to excite a relatively large area and detects the PA signal from a small area determined by its acoustic focus. Because ultrasound is less scattered than light in biological tissue, AR-PAM can achieve deeper imaging depth than OR-PAM at the expense of image resolution. Due to the limited resolution and imaging depth scale of each PAM type, it is challenging to image vessels in various area of small animals. In this study, we demonstrated in vivo OR-/AR-PAM imaging of blood vessels in various areas such as eye, ear, and hind limb by using a single commercial PAM system. Additionally, we quantified micro-vessel density (MVD) of the mouse eye and ear images, and applied a synthetic aperture focusing technique (SAFT) to correct the distorted PA signal at the out-of-focus in AR-PAM image. As a result, we have demonstrated multiscale PAM imaging of small animal vasculature in various areas with vessel quantification and resolution enhancement, so we believe that this multiscale PAM imaging technique would be helpful in biology research such as ischemia and neovascularization.1

A Deep Learning-Based Model That Reduces Speed of Sound Aberrations for Improved In Vivo Photoacoustic Imaging

Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images in terms of image resolution, imaging depth, or processing speed. Here, we demonstrate a novel deep learning PAI that uses multiple speed of sound (SoS) inputs. With this novel method, we achieved SoS aberration mitigation, streak artifact removal, and temporal resolution improvement all at once in structural and functional in vivo PA images of healthy human limbs and melanoma patients. The presented method produces high-contrast PA images in vivo with reduced distortion, even in adverse conditions where the medium is heterogeneous and/or the data sampling is sparse. Thus, we believe that this new method can achieve high image quality with fast data acquisition and can contribute to the advance of clinical PAI.11Nsciescopu

Photothermal strain imaging

Vulnerable plaques are the major cause of cardiovascular disease, but they are difficult to detect with conventional intravascular imaging techniques. Techniques are needed to identify plaque vulnerability based on the presence of lipids in plaque. Thermal strain imaging (TSI) is an imaging technique based on ultrasound (US) wave propagation speed, which varies with the medium temperature. In TSI, the strain that occurs during tissue temperature change can be used for lipid detection because it has a different tendency depending on the type of tissue. Here, we demonstrate photothermal strain imaging (pTSI) using an intravascular ultrasound catheter. pTSI is performed by slightly and selectively heating lipid using a relatively inexpensive continuous laser source. We applied a speckle-tracking algorithm to US B-mode images for strain calculations. As a result, the strain produced in porcine fat was different from the strain produced in water-bearing gelatin phantom, which made it possible to distinguish the two. This suggests that pTSI could potentially be a way of differentiating lipids in coronary artery. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)113sciescopu

A study of battery operational optimization with data-driven clustering

Environmental problems have led to continuing efforts to reduce fossil fuel consumption around the world. As a result, interest in battery-based hybrid systems is increasing in the shipbuilding and offshore industries. In particular, battery applications are more efficient for offshore vessels with frequent load variations and high peak power consumption. Propulsion systems are gen-erally classified as direct or electric propulsion. For some vessels, both direct and electric propulsion are used. The electrical power system of a vessel consists of one or multiple grids depending on the status (open/closed) of the bus tie. Owing to the complexity of propulsion and electrical power systems, designing the operation method and specifications of the battery onboard the vessel remains a challenge. Therefore, this paper categorizes and analyzes the data according to the condition of the bus tie. Principal component analysis clustering is applied to define the ship operation mode. The entire profile of a hybrid vessel with the hybrid propulsion sys-tem from a data point of view is analyzed, and an optimized battery operation method is proposed

Non-destructive photoacoustic imaging of metal surface defects

The detection of metal surface defects is important in achieving the goals of product quality enhancement and manufacturing cost reduction. Identifying the defects with visual inspection is difficult, inaccurate, and time-consuming. Thus, several inspection methods using line cameras, magnetic field, and ultrasound have been proposed. However, identifying small defects on metal surfaces remains a challenge. To deal with this problem, we propose the use of photoacoustic imaging (PAI) as a new non-destructive imaging tool to detect metal surface defects. We successfully visualized two types of cracks (i.e., unclassified and seam cracks) in metal plate samples using PAI. In addition, we successfully extracted cracked edges from height-encoded photoacoustic maximum amplitude projection images using the Laplacian of Gaussian filtering method, and then, quantified the detected edges for a statistical analysis. We concluded that PAI can be useful in detecting metal surface defects reducing the defect rate and manufacturing cost during metal production.11Nsciescopu