Mr. Cheng-Ting Shih Profile

Cheng-Ting Shih

at National Tsing-Hua Univ

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Publications (2)

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Proceedings Article | 29 March 2013 Paper

Rapid deployment of a Monte Carlo simulation system using diskless remote boot in Linux in a PACS environment

Yuan-Jen Chang, Yan-Lin Liu, Cheng-Ting Shih, Shu-Jun Chang, Jay Wu

Proceedings Volume 8674, 86740T (2013) https://doi.org/10.1117/12.2007731

KEYWORDS: Monte Carlo methods, Computer simulations, Switches, Picture Archiving and Communication System, Particles, Medicine, Atrial fibrillation, Operating systems, Parallel computing, Computing systems

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The Monte Carlo (MC) technique has been widely used as the gold standard for interaction of radiation with matter in the fields of medical physics, radiation therapy, and nuclear medicine. However, MC simulation is time consuming and requires a lot of computational resources. Generally, a dedicated high performance computing cluster is use to improve efficiency, but it is costly and lacks of the ability to run routine errands in healthcare facilities. In this study, we proposed a method for rapid deployment of computing platform for MC simulation in the PACS environment using review workstations as computing nodes. The workstations were booted from the network and initialed a RAM disk as the boot sector. The simplified Linux operating system and the Monte Carlo N-Particle Transport Code Version 5 (MCNP5) were transferred from the DRBL (Diskless Remote Boot in Linux) server to each node automatically. The cluster computing environment can be established within four minutes. We compared a commercially available dedicated cluster with the DRBL cluster. The results showed that the commercial cluster had a slightly higher acceleration factor than the DRBL cluster. The simulation time of the commercial and the DRBL clusters for 2×10⁸ particle histories was 37,151 and 40,021 sec, respectively. When the number of rendezvous increased to 20, the maximum time differences between both clusters were 95 and 85 sec for the megabit and the gigabit switches. We conclude that the DRBL cluster can be quickly deployed to the non-workloaded review workstations in the PACS. Thus, the MC technique could be broadly used to enhance the research capability of radiological sciences in healthcare facilities.

Proceedings Article | 6 March 2013 Paper

Noise reduction of low-dose computed tomography using the multi-resolution total variation minimization algorithm

Cheng-Ting Shih, Shu-Jun Chang, Yan-lin Liu, Jay Wu

Proceedings Volume 8668, 86682H (2013) https://doi.org/10.1117/12.2007543

KEYWORDS: Signal to noise ratio, Digital filtering, Gaussian filters, Computed tomography, Denoising, Image filtering, Wavelets, X-ray computed tomography, Head, Wavelet transforms

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Computed tomography (CT) has become a popular tool in radiologic diagnosis due to the ability of obtaining highresolution anatomical images. However, radiation doses to patients are substantial and can increase the risk of cancer incidence. Although lowering the tube current is a direct way to reduce absorbed doses, insufficient photon numbers can cause severe quantum mottle and subsequently degrade the diagnostic value of CT images. In this study, we proposed an algorithm for noise reduction of low-dose computed tomography (LDCT) based on the multiresolution total variation minimization (MRTV) method. The discrete wavelet transform was used to decompose the CT image into high- and lowfrequency wavelet coefficients. The total variation minimization with suitable tuning parameters was then applied to reduce the variance among the wavelet coefficients. The noise-reduced image was reconstructed by the inverse wavelet transform. The results of the Shepp-Logan phantom added with Gaussian white noise showed that the noise was eliminated effectively and the SNR in the three compartments was increased from 2.04, 20.69 and 0.09 to 19.45, 187.77 and 0.27, respectively. In the CT image of the water phantom acquired with 50-mAs tube currents, the MRTV improved the smoothness of the water compartment. The average SNR was increased from 0.14 to 0.98, which is even better than the CT image acquired by 200 mAs. In the clinical head CT image with a tube current of 9.12 mAs, the MRTV successfully removed the severe noise in the parenchyma, and SNR was increased from 0.982 to 3.452 in average. In addition, the details of the septal structure of the sinus cavity were maintained. We conclude that the MRTV approach can effectively reduce the image noise caused by the tube current insufficiency, and thereby could improve the diagnostic value of LDCT images.

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