Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading\nthe image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a\nlimited number of projections.However, conventional CS-based algorithms are computationally intensive and time-consuming.We\npropose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform\nand rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior\napproach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on\nthe statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning\ninterpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and\ninterpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 Ã?â?? 512 Shepp-Logan phantom\nwhen reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed\nmethod the reconstruction error was less than 1%.Moreover, computation times of less than 30 sec were obtained using a standard\ndesktop computer without numerical optimization....
We report on the inverse problem for the truncated Fourier series representation of f(x) ? BV(?l, l) in a form with a quadratic\ndegeneracy, revealing the existence of the Gibbs-Wilbraham phenomenon. A new distribution-theoretic proof is proposed for\nthis phenomenon. The paper studies moreover the iterative numerical solvability and solution of this inverse problem near\ndiscontinuities of f(x)....
Recently, there has been a problem of shortage of sleep laboratories that can accommodate the patients in a timelymanner. Delayed\ndiagnosis and treatment may lead to worse outcomes particularly in patients with severe obstructive sleep apnea (OSA). For\nthis reason, the prioritization in polysomnography (PSG) queueing should be endorsed based on disease severity. To date, there\nhave been conflicting data whether clinical information can predict OSA severity. The 1,042 suspected OSA patients underwent\ndiagnostic PSG study at Siriraj Sleep Center during 2010-2011. A total of 113 variables were obtained from sleep questionnaires and\nanthropometric measurements. The 19 groups of clinical risk factors consisting of 42 variables were categorized into each OSA\nseverity. This study aimed to array these factors by employing Fuzzy Analytic Hierarchy Process approach based on normalized\nweight vector.The results revealed that the first rank of clinical risk factors in Severe, Moderate, Mild, and No OSA was nighttime\nsymptoms. The overall sensitivity/specificity of the approach to these groups was 92.32%/91.76%, 89.52%/88.18%, 91.08%/84.58%,\nand 96.49%/81.23%, respectively. We propose that the urgent PSG appointment should include clinical risk factors of Severe OSA\ngroup. In addition, the screening for Mild from No OSA patients in sleep center setting using symptoms during sleep is also\nrecommended (sensitivity = 87.12% and specificity = 72.22%)....
An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat\nkidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore\nrenal microarchitecture.The purpose of the current research is to reduce the time and effort required to manually trace nephrons\nby creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely\npacked nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image\ndistortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a\ncustom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection\nof automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the\ncortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention\nis introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse\nnephron and 58 manual corrections per rat nephron....
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in\nrecent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction\nmethods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of\nfurther improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven\ntight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data driven\ntight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two level\nBregman iteration algorithm has been developed to solve the proposed model.The proposed method has been compared to\ntwo state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI....
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