Literature DB >> 30238563

Quantitative characterization and diagnosis via hard X-ray phase-contrast microtomography.

Huiqiang Liu1, Xuewen Ji2, Yan Ma1, Guohao Du3, Yanan Fu3, Yibanu Abudureheman4, Wenya Liu4.   

Abstract

Nondestructive three-dimensional (3D) micromorphological imaging technique is essential for hepatic alveolar echinococcosis (HAE) disease to determine its damage level and early diagnosis, assess relative drug therapy and optimize treatment strategies. However, the existing morphological researches of HAE mainly depend on the conventional CT, MRI, or ultrasound in hospitals, unfortunately confronting with the common limit of imaging resolution and sensitivity, especially for tiny or early HAE lesions. Now we presented a phase-retrieval-based synchrotron X-ray phase computed tomography (PR-XPCT) with striking contrast-to-noise ratio and high-density resolution to visualize the HAE nondestructive 3D structures and quantitatively segment different pathological characteristics of HAE lesions without staining process at the micrometer scale. Our experimental results of the HAE rat models at early and developed pathological stages and albendazole liposome (L-ABZ) therapeutic feeding models successfully exhibited the different HAE pathological 3D morphological and microstructural characteristics with excellent contrast and high resolution, demonstrating its availability and superiority. Moreover, we achieved the quantitative statistics and analysis of the pathological changes of HAE lesions at different stages and L-ABZ therapeutic evaluation, helpful to understanding the development and drug treatment of HAE disease. The PR-XPCT-based quantitative segmentation and characterization has a great potential in detection and analysis of soft tissue pathological changes, such as different tumors.
© 2018 Wiley Periodicals, Inc.

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Keywords:  X-ray phase microtomography; hepatic alveolar echinococcosis (HAE); micromorphological diagnosis and assessment; quantitative segmentation; synchrotron radiation

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Year:  2018        PMID: 30238563     DOI: 10.1002/jemt.23114

Source DB:  PubMed          Journal:  Microsc Res Tech        ISSN: 1059-910X            Impact factor:   2.769


  1 in total

1.  Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.

Authors:  Gong-Xiang Wei; Yun-Yan Liu; Xue-Wen Ji; Qiao-Xin Li; Yan Xing; Yan-Ling Xue; Hui-Qiang Liu
Journal:  Cancer Med       Date:  2021-03-07       Impact factor: 4.452

  1 in total

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