Lossy raw data compression in computed tomography with noise shaping to control image effects

Author(s):  
Yao Xie ◽  
Adam S. Wang ◽  
Norbert J. Pelc
1995 ◽  
Author(s):  
Y. Le Roy ◽  
Jean-Guy Planes ◽  
F. Cazaban

2018 ◽  
Vol 58 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Dominic Gascho ◽  
Michael J. Thali ◽  
Tilo Niemann

Post-mortem computed tomography (PMCT) has become a standard procedure in many forensic institutes worldwide. However, the standard scan protocols offered by vendors are optimised for clinical radiology and its main considerations regarding computed tomography (CT), namely, radiation exposure and motion artefacts. Thus, these protocols aim at low-dose imaging and fast imaging techniques. However, these considerations are negligible in post-mortem imaging, which allows for significantly increased image quality. Therefore, the parameters have to be adjusted to achieve the best image quality. Several parameters affect the image quality differently and have to be weighed against each other to achieve the best image quality for different diagnostic interests. There are two main groups of parameters that are adjustable by the user: acquisition parameters and reconstruction parameters. Acquisition parameters have to be selected prior to scanning and affect the raw data composition. In contrast, reconstruction parameters affect the calculation of the slice stacks from the raw data. This article describes the CT principles from acquiring image data to post-processing and provides an overview of the significant parameters for increasing the image quality in PMCT. Based on the CT principles, the effects of these parameters on the contrast, noise, resolution and frequently occurring artefacts are described. This article provides a guide for the performance of PMCT in morgues, clinical facilities or private practices.


2013 ◽  
Vol 380-384 ◽  
pp. 1495-1498
Author(s):  
Shang Chun Zeng ◽  
Yun Xia Xie ◽  
Yi Xian Chen ◽  
Zhao Da Zhu

t is difficult to directly compress the raw data of synthetic aperture radar for its low relativity. In this paper, a new algorithm is put forward. Firstly range focusing is imposed to SAR raw data, which makes it have comparative high relativity, secondly a linear prediction is performed along the azimuth, lastly block adaptive quantization is used to the prediction difference series. The experiments manifest that with same bit rate, SQNR and SDNR of the algorithm proposed in this paper surpass that of BAQ algorithm. The calculation in this paper is far less than that of compression method after range focusing advised in corresponding reference. The algorithm proposed in this paper has a certain practical value.


Author(s):  
Zeng Shangchun ◽  
Xie Yunxia ◽  
Chen Yixian ◽  
Zhu Zhaoda
Keyword(s):  

2013 ◽  
Author(s):  
Shangchun Zeng ◽  
Yunxia Xie ◽  
Yixian Chen ◽  
Huazhang Wang

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