Design of short wave infrared imaging spectrometer system based on CDP.

A compact static infrared snapshot imaging spectrometer (ISIS) is designed in order to satisfy the application requirements of real-time spectral imaging for the moving targets. It consists of a CDP (crossed dispersion prism), an imaging lens, and a detector. Here we describe the spectral imaging principle, and design a short wave infrared imaging spectrometer with 4.8° field of view, the measured spectrum is from 0.9µm to 2.5µm and is sampled by 40 spectral channels. This instrument has a large potential for detecting, locating and identifying unknown energetic events in real-time.

Automatic Side Branch Ostium Detection and Main Vascular Segmentation in Intravascular Optical Coherence Tomography Images.

Intravascular optical coherence tomography is the state-of-the-art imaging modality in percutaneous coronary intervention planning and evaluation, in which side branch ostium and main vascular measurements play critical roles. However, manual measurement is time consuming and labor intensive. In this paper, we propose a fully automatic method for side branch ostium detection and main vascular segmentation to make up manual deficiency. In our method, side branch ostium points are first detected and subsequently used to divide the lumen contour into side branch and main vascular regions. Based on the division, main vascular contour is then smoothly fitted for segmentation. In side branch ostium detection, our algorithm creatively converts the definition of curvature into the calculation of the signed included angles in global view, and originally applies a differential filter to highlight the feature of side branch ostium points. A total of 4618 images from 22 pullback runs were used to evaluate the performance of the presented method. The validation results of side branch detection were TPR = 82.8 $\%$, TNR = 98.7$\%$ , PPV = 86.8$\%$, NPV = 98.7$\%$. The average ostial distance error (ODE) was 0.22 mm, and the DSC of main vascular segmentation was 0.96. In conclusion, the qualitative and quantitative evaluation indicated that the presented method is effective and accurate.

Automatic analysis of bioresorbable vascular scaffolds in intravascular optical coherence tomography images.

The bioresorbable vascular scaffold (BVS) is a new generation of bioresorbable scaffold (BRS) for the treatment of coronary artery disease. A potential challenge of BVS is malapposition, which may possibly lead to late stent thrombosis. It is therefore important to conduct malapposition analysis right after stenting. Since an intravascular optical coherence tomography (IVOCT) image sequence contains thousands of BVS struts, manual analysis is labor intensive and time consuming. Computer-based automatic analysis is an alternative, but faces some difficulties due to the interference of blood artifacts and the uncertainty of the struts number, position and size. In this paper, we propose a novel framework for a struts malapposition analysis that breaks down the problem into two steps. Firstly, struts are detected by a cascade classifier trained by AdaBoost and a region of interest (ROI) is determined for each strut to completely contain it. Then, strut boundaries are segmented within ROIs through dynamic programming. Based on the segmentation result, malapposition analysis is conducted automatically. Tested on 7 pullbacks labeled by an expert, our method correctly detected 91.5% of 5821 BVS struts with 12.1% false positives. The average segmentation Dice coefficient for correctly detected struts was 0.81. The time consumption for a pullback is 15 sec on average. We conclude that our method is accurate and efficient for BVS strut detection and segmentation, and enables automatic BVS malapposition analysis in IVOCT images.

An improved schlieren method for measurement and automatic reconstruction of the far-field focal spot.

The schlieren method of measuring far-field focal spots offers many advantages at the Shenguang III laser facility such as low cost and automatic laser-path collimation. However, current methods of far-field focal spot measurement often suffer from low precision and efficiency when the final focal spot is merged manually, thereby reducing the accuracy of reconstruction. In this paper, we introduce an improved schlieren method to construct the high dynamic-range image of far-field focal spots and improve the reconstruction accuracy and efficiency. First, a detection method based on weak light beam sampling and magnification imaging was designed; images of the main and side lobes of the focused laser irradiance in the far field were obtained using two scientific CCD cameras. Second, using a self-correlation template matching algorithm, a circle the same size as the schlieren ball was dug from the main lobe cutting image and used to change the relative region of the main lobe cutting image within a 100×100 pixel region. The position that had the largest correlation coefficient between the side lobe cutting image and the main lobe cutting image when a circle was dug was identified as the best matching point. Finally, the least squares method was used to fit the center of the side lobe schlieren small ball, and the error was less than 1 pixel. The experimental results show that this method enables the accurate, high-dynamic-range measurement of a far-field focal spot and automatic image reconstruction. Because the best matching point is obtained through image processing rather than traditional reconstruction methods based on manual splicing, this method is less sensitive to the efficiency of focal-spot reconstruction and thus offers better experimental precision.

Automatic Lumen Segmentation in Intravascular Optical Coherence Tomography Images Using Level Set.

Automatic lumen segmentation from intravascular optical coherence tomography (IVOCT) images is an important and fundamental work for diagnosis and treatment of coronary artery disease. However, it is a very challenging task due to irregular lumen caused by unstable plaque and bifurcation vessel, guide wire shadow, and blood artifacts. To address these problems, this paper presents a novel automatic level set based segmentation algorithm which is very competent for irregular lumen challenge. Before applying the level set model, a narrow image smooth filter is proposed to reduce the effect of artifacts and prevent the leakage of level set meanwhile. Moreover, a divide-and-conquer strategy is proposed to deal with the guide wire shadow. With our proposed method, the influence of irregular lumen, guide wire shadow, and blood artifacts can be appreciably reduced. Finally, the experimental results showed that the proposed method is robust and accurate by evaluating 880 images from 5 different patients and the average DSC value was 98.1% ± 1.1%.