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Clinical Study of Diffusion-Weighted Imaging in the Diagnosis of Liver Focal Lesion

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response so as to determine the best therapy approach. In this paper, our goal was to determine whether the DW-MRI can be used for qualitative and quantitative liver cancer analysis, where an automated method will be proposed for improving the accuracy of liver segmentation in DW-MRI to increase the ability of diagnosis of disease. We firstly analyzed the research status of liver cancer diagnosis, especially on the issues of liver image segmentation technology in MRI. Then, the imaging mechanism and image features of the DW-MRI were analyzed, and the initial DW-MRI slice was segmented by graph-cut algorithm. Finally, our obtained result from the liver DW-MRI image is quantitatively and qualitatively analyzed. Experimental results show that DW-MRI has a great advantage in the diagnosis, the DWI images of benign lesion group was lower than that of malignant lesion, thus DW-MRI is segmented by graph-cut algorithm can provide important additional information regarding differential diagnosis of specific liver cancer to some extend.

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Authors and Affiliations

  1. Tongde hospital of Zhejiang province, Zhejiang, 310012, Hangzhou, China

    Yue Yang

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  1. Jiapeng Li
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  2. Yue Yang
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Correspondence to Yue Yang.

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We declare that we have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.

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This article is part of the Topical Collection on Image & Signal Processing

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Li, J., Yang, Y. Clinical Study of Diffusion-Weighted Imaging in the Diagnosis of Liver Focal Lesion. J Med Syst 43, 43 (2019). https://doi.org/10.1007/s10916-019-1164-1

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