Clinically-translatable High-fidelity Photoacoustic Tomography Enhanced by Virtual Point Sources

Photoacoustic tomography (PAT), a hybrid imaging modality that acoustically detects the optical absorption contrast, is a promising technology for imaging hemodynamic functions in deep tissues. Particularly, PAT is capable of measuring the blood oxygenation level using hemoglobin as the endogenous contrast. However, the most clinically compatible PAT configuration usually employs a linear ultrasound transducer array and often suffers from the poor image fidelity, mostly due to the limited detection view of the transducer array. PAT can be improved by employing highly-absorbing contrast agents such as droplets and nanoparticles, which, however, have low clinical translation potential due to safety concerns and regulatory hurdles. Moreover, unlike hemoglobin, these exogenous contrast agents cannot report the functional hemodynamic information. In this work, we have developed a new methodology that can improve PAT’s image fidelity without hampering its functional capability or clinical translation potential. By using clinically-approved microbubbles as virtual point sources that strongly scatter the local pressure waves generated by surrounding hemoglobin, we can overcome the limited-detection-view problem and achieve high-fidelity functional PAT in deep tissues, a technology referred to as virtual-point-source PAT (VPS-PAT). We have thoroughly investigated the working principle of VPS-PAT by numerical simulations and phantom validations, showing the acoustic origin of signal enhancement and the superiority over traditional PAT. We have also demonstrated proof-of-concept applications of functional VPT-PAT for in vivo small-animal studies with physiological challenges. We expect that VPS-PAT can find broad applications in biomedical research and accelerated translation to clinical impact.

Speckle reduction in ultrasound endoscopy using refraction based elevational angular compounding

Endoscopic ultrasonography (EUS) is a safe, real-time diagnostic and therapeutic tool. Speckle noise, inherent to ultrasonography, degrades the diagnostic precision of EUS. Elevational angular compounding (EAC) can provide real-time speckle noise reduction; however, EAC has never been applied to EUS because current implementations require costly and bulky arrays and are incompatible with the tight spatial constraints of hollow organs. Here we develop a radial implementation of a refraction-based elevational angular compounding technique (REACT) for EUS and demonstrate for the first time spatial compounding in a radial endoscopy. The proposed implementation was investigated in cylindrical phantoms and demonstrated superior suppression of ultrasound speckle noise and up to a two-fold improvement in signal- and contrast- ratios, compared to standard image processing techniques and averaging. The effect of elevational angular deflection on image fidelity was further investigated in a phantom with lymph node-like structures to determine the optimum elevational angular width for high speckle reduction efficiency while maintaining image fidelity. This study introduces REACT as a potential compact and low-cost solution to impart current radial echo-endoscopes with spatial compounding, which could enable accurate identification and precise sizing of lymph nodes in staging of gastrointestinal tract cancers.

Dynamic Temperature Management of Near-Sensor Processing for Energy-Efficient High-Fidelity Imaging

Vision processing on traditional architectures is inefficient due to energy-expensive off-chip data movement. Many researchers advocate pushing processing close to the sensor to substantially reduce data movement. However, continuous near-sensor processing raises sensor temperature, impairing imaging/vision fidelity. We characterize the thermal implications of using 3D stacked image sensors with near-sensor vision processing units. Our characterization reveals that near-sensor processing reduces system power but degrades image quality. For reasonable image fidelity, the sensor temperature needs to stay below a threshold, situationally determined by application needs. Fortunately, our characterization also identifies opportunities—unique to the needs of near-sensor processing—to regulate temperature based on dynamic visual task requirements and rapidly increase capture quality on demand. Based on our characterization, we propose and investigate two thermal management strategies—stop-capture-go and seasonal migration—for imaging-aware thermal management. For our evaluated tasks, our policies save up to 53% of system power with negligible performance impact and sustained image fidelity.

Image steganography using least significant bit and secret map techniques

In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity.