2D synthetic radiography image can be computed from quasi-3D volume image produced by digital tomosynthesis (DTS) module negating additional radiation exposure for a separate 2D X-ray imaging. In our earlier work, we have developed a prototype DTS system that is equipped with an array of carbon-nanotube (CNT) X-ray sources. In this work, we develop an algorithm for synthesizing 2D image from the DTS-reconstructed volume image in the source array-based DTS system. Since the system uses a 2D array type source, the image artifacts due to the out-of-plane structures manifest relatively uniformly in all directions in the image slice unlike typical tomosynthesis systems. We have developed a smooth-manifold-extraction (SME) based method, which has been used in the field of confocal microscopy, for 2D image synthesis. Unlike microscopy, high-density structures exist at varying depths in a human body. Therefore, the SME algorithm was modified to apply to our DTS system.
In the production of display screen modules, multi-faceted quality control is performed. One of the processes is detection of defects on and between module components such as particles, scratches and air bubbles using a 3D optical microscope. Technicians view a stack of images of potential defect areas and make a qualitative assessment of the sample. However, this is made difficult by the artifacts in the unfocused image layers. Moreover, there is a large discrepancy in the detection tendencies of the technicians. In order to standardize and automate the classification of major and minor defects in products, we propose a convolutional neural network based binary classification that makes use of the normal angle and oblique angle images. The decision factors affecting the classification of the sample include defect position, size, and shape. In order to reflect these factors, the microscopic images of the sample are taken in varying focal depths from normal and oblique angles. Then, the maximum intensity projection (MaxIP) and minimum intensity projection (MinIP) in the xy, yz, xz plane are created. The set of MaxIP and MinIP are used to train a modified VGG-network. Each plane differs in size, so MaxIP and MinIP of every plane was independently added as input to the network and were concatenated in the fully connected layer. Being that the dataset used for this work composed of 185 major defect samples and 2036 minor defect samples, augmentation was essential. In order to even out the major and minor defect sample ratio, random affine transformation was performed on the major defect sample images. The proposed method of binary classification performs with a total accuracy of 98.6%.
KEYWORDS: X-ray sources, X-rays, X-ray imaging, Chest, 3D image reconstruction, Carbon nanotubes, Sensors, Medical research, 3D image processing, Digital imaging
Digital chest tomosynthesis that provides a reconstructed 3D chest image is a superior technique to detect chest diseases. As it is difficult to detect diseases like lung cancer with conventional 2D digital chest X-ray technology (CXT), digital chest tomosynthesis improves upon the many of the limitations inherent in the 2D digital CXT. In this study, we report a digital chest tomosynthesis system (D-CTS) that can generate multi X-ray information for the reconstruction of a 3D Xray chest image. The D-CTS reported herein employs an array of carbon nanotube (CNT) emitter-based cold cathode electron-guns that are triggered in sequence to provide a gantry-less system (Figure 1). The CNTs are achieved by direct growth on a metal substrate and have a spaghetti-like structure (Figure 2) with fast response to electrical bias under vacuum conditions. Unlike conventional rotating type systems with gantries, our CTS has the advantage of less motion blur in image acquisition, given its stationary position. Additionally, the switching from one electron-gun (e-gun) to the next is much faster than the speed of conventional gantries, allowing faster acquisition time t required for digital operation. This system shows outstanding field emission property for taking X-ray images. The design, fabrication process and imaging processing of the multi-beam CNT X-ray system will be discussed during the presentation.
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