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In this study, we present finite element analysis models to calculate the increase in intrathoracic volume of pectus excavatum patients after the Nuss procedure. One virtue of our approach is that the measurement of the intrathoracic volume has no time difference and is not affected by postoperative pain, which cannot be achieved with a 2-year difference between pre- and postoperative pulmonary function testing or any other clinical method. The calculations show that the intrathoracic volume of pectus excavatum patients increased by approximately 2.72-8.88% after the Nuss procedure. The increment curve was patient-dependent, although the increment behavior was similar among the six patients examined. The curve of the increase became flat when the elevating force exceeded 80 N or the displacement of the lower sternal end exceeded 2.6 cm in half of our cases. [ABSTRACT FROM AUTHOR]

Synchrotron radiation can be used as a light source in X-ray microscopy to acquire a high-resolution image of a microscale object for tomography. However, numerous projections must be captured for a high-quality tomographic image to be reconstructed; thus, image acquisition is time consuming. Such dense imaging is not only expensive and time consuming but also results in the target receiving a large dose of radiation. To resolve these problems, sparse acquisition techniques have been proposed; however, the generated images often have many artefacts and are noisy. In this study, a deep-learning-based approach is proposed for the tomographic reconstruction of sparse-view projections that are acquired with a synchrotron light source; this approach proceeds as follows. A convolutional neural network (CNN) is used to first interpolate sparse X-ray projections and then synthesize a sufficiently large set of images to produce a sinogram. After the sinogram is constructed, a second CNN is used for error correction. In experiments, this method successfully produced high-quality tomography images from sparse-view projections for two data sets comprising Drosophila and mouse tomography images. However, the initial results for the smaller mouse data set were poor; therefore, transfer learning was used to apply the Drosophila model to the mouse data set, greatly improving the quality of the reconstructed sinogram. The method could be used to achieve high-quality tomography while reducing the radiation dose to imaging subjects and the imaging time and cost., (open access.)

An error in tomographic reconstruction parameters can result considerable artifacts in the reconstructed image, particularly in micro-computed tomography and nano-computed tomography. This study involved designing an automatic method for efficiently correcting errors resulting from incorrectly determined rotational axes and projection angles. In this method, errors are corrected by minimizing the "total variation" of a reconstructed image, and minimization is accomplished by using the gradient descent method. Compared with two previous methods, the proposed method achieved the best reconstruction results.

Computing and analyzing the neuronal structure is essential to studying connectome. Two important tasks for such analysis are finding the soma and constructing the neuronal structure. Finding the soma is considered more important because it is required for some neuron tracing algorithms. We describe a robust automatic soma detection method developed based on the machine learning technique. Images of neurons were three-dimensional confocal microscopic images in the FlyCircuit database. The testing data were randomly selected raw images that contained noises and partial neuronal structures. The number of somas in the images was not known in advance. Our method tries to identify all the somas in the images. Experimental results showed that the method is efficient and robust.

A synchrotron X-ray microscope is a powerful imaging apparatus for taking high-resolution and high-contrast X-ray images of nanoscale objects. A sufficient number of X-ray projection images from different angles is required for constructing 3D volume images of an object. Because a synchrotron light source is immobile, a rotational object holder is required for tomography. At a resolution of 10 nm per pixel, the vibration of the holder caused by rotating the object cannot be disregarded if tomographic images are to be reconstructed accurately. This paper presents a computer method to compensate for the vibration of the rotational holder by aligning neighboring X-ray images. This alignment process involves two steps. The first step is to match the "projected feature points" in the sequence of images. The matched projected feature points in the x-θ plane should form a set of sine-shaped loci. The second step is to fit the loci to a set of sine waves to compute the parameters required for alignment. The experimental results show that the proposed method outperforms two previously proposed methods, Xradia and SPIDER. The developed software system can be downloaded from the URL, http://www.cs.nctu.edu.tw/~chengchc/SCTA or http://goo.gl/s4AMx.

The rapid development of microscopic imaging techniques has greatly facilitated time-lapse imaging of neuronal morphology. However, analysis of structural dynamics in the vast amount of 4-Dimensional data generated by in vivo or ex vivo time-lapse imaging still relies heavily on manual comparison, which is not only laborious, but also introduces errors and discrepancies between individual researchers and greatly limits the research pace. Here we present a supervised 4D Structural Plasticity Analysis (4D SPA) computer method to align and match 3-Dimensional neuronal structures across different time points on a semi-automated basis. We demonstrate 2 applications of the method to analyze time-lapse data showing gross morphological changes in dendritic arbor morphology and to identify the distribution and types of branch dynamics seen in a series of time-lapse images. Analysis of the dynamic changes of neuronal structure can be done much faster and with greatly improved consistency and reliability with the 4D SPA supervised computer program. Users can format the neuronal reconstruction data to be used for this analysis. We provide file converters for Neurolucida and Imaris users. The program and user manual are publically accessible and operate through a graphical user interface on Windows and Mac OSX.

We demonstrate that, with appropriate staining, high-resolution X-ray microscopy can image complicated tissue structures--cerebellum and liver--and resolve large or small amounts of Au nanoparticles in these tissues. Specifically, images of tumor tissue reveal high concentrations of accumulated Au nanoparticles. PEG (poly(ethylene glycol)) coating is quite effective in enhancing this accumulation and significantly modifies the mechanism of uptake by reticuloendothelial system (RES) organs.

Drosophila melanogaster is a well-studied model organism, especially in the field of neurophysiology and neural circuits. The brain of the Drosophila is small but complex, and the image of a single neuron in the brain can be acquired using confocal microscopy. Analyzing the Drosophila brain is an ideal start to understanding the neural structure. The most fundamental task in studying the neural network of Drosophila is to reconstruct neuronal structures from image stacks. Although the fruit fly brain is small, it contains approximately 100,000 neurons. It is impossible to trace all the neurons manually. This study presents a high-throughput algorithm for reconstructing the neuronal structures from 3D image stacks collected by a laser scanning confocal microscope. The proposed method reconstructs the neuronal structure by applying the shortest path graph algorithm. The vertices in the graph are certain points on the 2D skeletons of the neuron in the slices. These points are close to the 3D centerlines of the neuron branches. The accuracy of the algorithm was verified using the DIADEM data set. This method has been adopted as part of the protocol of the FlyCircuit Database, and was successfully applied to process more than 16,000 neurons. This study also shows that further analysis based on the reconstruction results can be performed to gather more information on the neural network.

Refractive-index (phase-contrast) radiology was able to detect lung tumors less than 1 mm in live mice. Significant micromorphology differences were observed in the microradiographs between normal, inflamed, and lung cancer tissues. This was made possible by the high phase contrast and by the fast image taking that reduces the motion blur. The detection of cancer and inflammation areas by phase contrast microradiology and microtomography was validated by bioluminescence and histopathological analysis. The smallest tumor detected is less than 1 mm(3) with accuracy better than 1 × 10(-3) mm(3). This level of performance is currently suitable for animal studies, while further developments are required for clinical application.

Background: Animal behavior is governed by the activity of interconnected brain circuits. Comprehensive brain wiring maps are thus needed in order to formulate hypotheses about information flow and also to guide genetic manipulations aimed at understanding how genes and circuits orchestrate complex behaviors., Results: To assemble this map, we deconstructed the adult Drosophila brain into approximately 16,000 single neurons and reconstructed them into a common standardized framework to produce a virtual fly brain. We have constructed a mesoscopic map and found that it consists of 41 local processing units (LPUs), six hubs, and 58 tracts covering the whole Drosophila brain. Despite individual local variation, the architecture of the Drosophila brain shows invariance for both the aggregation of local neurons (LNs) within specific LPUs and for the connectivity of projection neurons (PNs) between the same set of LPUs. An open-access image database, named FlyCircuit, has been constructed for online data archiving, mining, analysis, and three-dimensional visualization of all single neurons, brain-wide LPUs, their wiring diagrams, and neural tracts., Conclusion: We found that the Drosophila brain is assembled from families of multiple LPUs and their interconnections. This provides an essential first step in the analysis of information processing within and between neurons in a complete brain., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Gel electrophoresis (GE) is an important tool in genomic analysis. GE results are presented using images. Each image contains several vertical lanes. Each lane consists of several horizontal bands. Two lanes are identical if the relative positions of the bands are the same. We present a computer method designed to compare the lanes and identify identical lanes. This method, developed using many image-processing techniques, is applied to segment the lanes and bands in GE images. The lanes are then converted into "position vectors" that describe the positions of the bands. Comparing lanes becomes equivalent to comparing the position vectors. This method can accurately identify identical lanes, helping biologists to identify the identical lanes from many lanes with much less effort.

Computed radiography (CR) has many advantages such as filmless operations, efficiency, and convenience. Furthermore, it is easier to integrate with the picture archiving and communication systems. Another important advantage is that CR images generally have a wider dynamic range than conventional screen film. Unfortunately, grid artifacts and moiré pattern artifacts may be present in CR images. These artifacts become a more serious problem when viewing CR images on a computer monitor when a clinic grade monitor is not available. Images produced using a grid with higher frequency or a Potter--Bucky grid (i.e., a moving grid, Bucky for short) can reduce occurrence but cannot guarantee elimination of these artifacts [CR & PACS (2000); Detrick F (2001), pp 7-8]. In this paper, the formation of the artifacts is studied. We show that the grid artifacts occur in a narrow band of frequency in the frequency domain. The frequency can be determined, accurately located, and thus removed from the frequency domain. When comparing the results obtained from the proposed method against the results obtained using previous computer methods, we show that our method can achieve better image quality.

Online teaching files are an important source of educational and referential materials in the radiology community. The commonly used Digital Imaging and Communications in Medicine (DICOM) file format of the radiology community is not natively supported by common Web browsers. The ability of the Web server to convert and parse DICOM is important when the DICOM-converting tools are not available. In this paper, we describe our approach to develop a Web-based teaching file authoring tool. Our server is built using Apache Web server running on FreeBSD operating system. The dynamic page content is produced by Hypertext Preprocessor (PHP). Digital Imaging and Communications in Medicine images are converted by ImageMagick into Joint Photographic Experts Group (JPEG) format. Digital Imaging and Communications in Medicine attributes are parsed by dicom3tools and stored in PostgreSQL database. Using free software available from the Internet, we build a Web service that allows radiologists to create their own online teaching file cases with a common Web browser.

The ERG3 gene in Candida albicans was identified as a gene whose mRNA level was higher in the cph1/cph1 efg1/efg1 double mutant than in the wild-type cells. Further study showed that Efg1, but not Cph1, negatively regulated ERG3. Mutations in EFG1 consistently increased the susceptibility of the cells to antifungal agents.

The apical four-chamber view echocardiogram collected by a transthoracic transducer can be used to evaluate the left ventricle volume. In the diastole, the left ventricle and left atrium become one chamber. In this case, the left ventricle and left atrium need to be separated using a "mitral annular line" so the volume of the left ventricle can be estimated. In this paper, a nearly automatic method for identifying the mitral annular lines from two-dimensional (2-D) + one-dimensional (1-D) precordial four-chamber view echocardiogram is presented. This method employs the optical flow technique and graph-search approach. The mitral annular line sequence is found by finding the shortest path in a weighted directed graph. The vertices in the graph are candidates for the mitral annular lines. The weights on the directed edges are determined using the optical flow technique. The proposed method requires only a physician to provide a point that is always in the left ventricular chamber. Experimental results show that the average error for the left ventricle volume obtained based on the computed mitral annular lines is 3%.