Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing
نویسندگان
چکیده
منابع مشابه
Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.
In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processin...
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Weiwen Wu1,2, Yanbo Zhang2, Qian Wang2, Fenglin Liu1,3,*, Peijun Chen1 and Hengyong Yu2,* 1Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China 2Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA 3Engineering Research Center of Industrial Computed Tomography Nondestructive...
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PURPOSE Acquiring data for CT at low radiation doses has become a pressing goal. Unfortunately, the reduced data quality adversely affects the quality of the reconstructions, impeding their readability. In previous work, the authors showed how a prior regular-dose scan of the same patient can efficiently be used to mitigate low-dose artifacts. However, since a prior is not always available, the...
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Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computati...
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BACKGROUND In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimiz...
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ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2014
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2014.2336860