Our aim in this study was to optimize and validate an adaptive denoising algorithm based on Block-Matching 3D, for reducing image noise and improving assessment of left ventricular function from low-radiation dose coronary CTA. In this paper, we describe the denoising algorithm and its validation, with low-radiation dose coronary CTA datasets from 7 consecutive patients. We validated the algorithm using a novel method, with the myocardial mass from the low-noise cardiac phase as a reference standard, and objective measurement of image noise. After denoising, the myocardial mass were not statistically different by comparison of individual datapoints by the students' t-test (130.9±31.3g in low-noise 70% phase vs 142.1±48.8g in the denoised 40% phase, p= 0.23). Image noise improved significantly between the 40% phase and the denoised 40% phase by the students' t-test, both in the blood pool (p <0.0001) and myocardium (p <0.0001). In conclusion, we optimized and validated an adaptive BM3D denoising algorithm for coronary CTA. This new method reduces image noise and has the potential for improving assessment of left ventricular function from low-dose coronary CTA.

Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.

This paper presents a low-power always-on image sensor for mobile and wearable device applications. The sensor continuously captures images for smart sensing, such as face detection, eye tracking, and gesture recognition, and it provides high-resolution images for capturing pictures with a unified sensor. The sensor employs a switchable dual mode: always-on (AO) mode with low power consumption and photo-shooting (PS) mode with high signal-to-noise ratio. For dual-mode operation with high energy efficiency, we implemented dynamic voltage scaling that provides 0.9 V analog-digital supply voltage for the AO mode and 3.3 V analog-1.8 V digital supply voltage for the PS mode. For low-voltage operation in the AO mode, a conventional four-transistor pixel operates as a charge-shared pixel. In order to suppress power consumption and frequency in conventional single-slope analog-to-digital converters (ADC), two neighboring column-parallel ADCs are reconfigured to one successive-approximation ADC for low-voltage and low-frequency operation in the AO mode with negligible area overhead. The fabricated 640 × 480 pixel prototype sensor operates at 45.5 μW (at 15 fps, 320 × 240) in the AO mode, which significantly extends battery life when performing always-on sensing.

Noninvasive cardiac imaging is an invaluable tool for the diagnosis and treatment of cardiovascular disease (CVD). Magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), and ultrasound (US) have been used extensively for physiologic understanding and diagnostic purposes in cardiology. These imaging technologies have greatly increased our understanding of normal and diseased anatomy. Cardiac image segmentation plays a crucial role and allows for a wide range of applications, including quantification of volume, computer-aided diagnosis, localization of pathology, and image-guided interventions. However, manual delineation is tedious, time-consuming, and is limited by inter- and intraobserver variability. In addition, many segmentation algorithms are sensitive to the initialization and therefore the results are not always reproducible, which is also limited by interalgorithm variability. Furthermore, the amount and quality of imaging data that needs to be routinely acquired in one or more subjects has increased significantly. Therefore, it is crucial to develop automated, precise, and reproducible segmentation methods. Figure 1 illustrates an example of segmentation of heart on CT scan.

In this paper we present an autostereoscopic 3D display using a directional subpixel rendering algorithm in which clear left–right images are expressed in real time based on a viewer's 3D eye positions. In order to maintain the 3D image quality over a wide viewing range, we designed an optical layer that generates a uniformly distributed light field. The proposed 3D rendering method is simple, and each pixel processing can be performed independently in parallel computing environments. To prove the effectiveness of our display system, we implemented 31.5” 3D monitor and 10.1” 3D tablet prototypes in which the 3D rendering is processed in the GPU and FPGA board, respectively.

Eye pupil tracking is important for augmented reality (AR) three-dimensional (3D) head-up displays (HUDs). Accurate and fast eye tracking is still challenging due to multiple driving conditions with eye occlusions, such as wearing sunglasses. In this paper, we propose a system for commercial use that can handle practical driving conditions. Our system classifies human faces into bare faces and sunglasses faces, which are treated differently. For bare faces, our eye tracker regresses the pupil area in a coarse-to-fine manner based on a revised Supervised Descent Method based eye-nose alignment. For sunglasses faces, because the eyes are occluded, our eye tracker uses whole face alignment with a revised Practical Facial Landmark Detector for pupil center tracking. Furthermore, we propose a structural inference-based re-weight network to predict eye position from non-occluded areas, such as the nose and mouth. The proposed re-weight sub-network revises the importance of different feature map positions and predicts the occluded eye positions by non-occluded parts. The proposed eye tracker is robust via a tracker-checker and a small model size. Experiments show that our method achieves high accuracy and speed, approximately 1.5 and 6.5 mm error for bare and sunglasses faces, respectively, at less than 10 ms on a 2.0GHz CPU. The evaluation dataset was captured indoors and outdoors to reflect multiple sunlight conditions. Our proposed method, combined with AR 3D HUDs, shows promising results for commercialization with low crosstalk 3D images.