Current Issue : July - September Volume : 2016 Issue Number : 3 Articles : 5 Articles
Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit\nfrom less waiting time. Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were\nproposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement\nof clinical use. Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage thresholding\nalgorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i)\nexponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift. Results. Experimental results\nvalidated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared\nerror, and the highest peak signal-to-noise ratio. Conclusion. EWISTARS is superior to state-of-the-art approaches....
Purpose. To develop a method for identifying abnormalmyocardial function based on studying the normalized wallmotion pattern\nduring the cardiac cycle. Methods. The temporal pattern of the normalized myocardial wall thickness is used as a feature vector to\nassess the cardiac wall motion abnormality. Principal component analysis is used to reduce the feature dimensionality and the\nmaximum likelihood method is used to differentiate between normal and abnormal features. The proposed method was applied on\na dataset of 27 cases from normal subjects and patients. Results.The developed method achieved 81.5%, 85%, and 88.5% accuracy\nfor identifying abnormal contractility in the basal, midventricular, and apical slices, respectively. Conclusions. A novel feature\nvector, namely, the normalized wall thickness, has been introduced for detecting myocardial regional wall motion abnormality.\nThe proposed method provides assessment of the regional myocardial contractility for each cardiac segment and slice; therefore,\nit could be a valuable tool for automatic and fast determination of regional wall motion abnormality from conventional cine MRI\nimages....
An integrate fabrication framework is presented to build heterogeneous objects (HEO) using digital microdroplets injecting\ntechnology and rapid prototyping. The heterogeneous materials part design and manufacturing method in structure and material\nwas used to change the traditional process. The net node method was used for digital modeling that can configure multi materials in\ntime.The relationship of material, color, and jetting nozzle was built.The main important contributions are to combine the structure,\nmaterial, and visualization in one process and give the digital model for manufacture. From the given model, it is concluded that\nthe method is effective for HEO. Using microdroplet rapid prototyping and the model given in the paper HEO could be gotten\nbasically. The model could be used in 3D biomanufacturing....
With the advancement of technology in communication network, it facilitated digital medical images transmitted to healthcare\nprofessionals via internal network or public network (e.g., Internet), but it also exposes the transmitted digital medical images\nto the security threats, such as images tampering or inserting false data in the images, which may cause an inaccurate diagnosis\nand treatment. Medical image distortion is not to be tolerated for diagnosis purposes; thus a digital watermarking on medical\nimage is introduced. So far most of the watermarking research has been done on single frame medical image which is impractical\nin the real environment. In this paper, a digital watermarking on multiframes medical images is proposed. In order to speed\nup multiframes watermarking processing time, a parallel watermarking processing on medical images processing by utilizing\nmulticores technology is introduced. An experiment result has shown that elapsed time on parallel watermarking processing is\nmuch shorter than sequential watermarking processing....
This work proposes a dedicated statistical algorithm to perform a direct reconstruction of material-decomposed images from data\nacquired with photon-counting detectors (PCDs) in computed tomography. It is based on local approximations (surrogates) of the\nnegative logarithmic Poisson probability function. Exploiting the convexity of this function allows for parallel updates of all image\npixels. Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms.We investigate the\naccuracy of the algorithm for ideal photon-counting detectors. Complementarily, we apply the algorithm to simulation data of a\nrealistic PCD with its spectral resolution limited by K-escape, charge sharing, and pulse-pileup. For data from both an ideal and\nrealistic PCD, the proposed algorithm is able to correct beam-hardening artifacts and quantitatively determine the material fractions\nof the chosen basis materials. Via regularization we were able to achieve a reduction of image noise for the realistic PCD that is up\nto 90% lower compared to material images form a linear, image-based material decomposition using FBP images. Additionally, we\nfind a dependence of the algorithms convergence speed on the threshold selection within the PCD....
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