4.6 Article

A deep unsupervised clustering-based post-processing framework for high-fidelity Cerenkov luminescence tomography

期刊

JOURNAL OF APPLIED PHYSICS
卷 128, 期 19, 页码 -

出版社

AIP Publishing
DOI: 10.1063/5.0025877

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资金

  1. National Key R&D Program of China [2018YFC0910602]
  2. National Natural Science Foundation of China (NNSFC) [61701403]
  3. China Post-doctoral Science Foundation [2018M643719]
  4. Young Talent Support Program of the Shaanxi Association for Science and Technology [20190107]
  5. Shaanxi Provincial Education Department [18JK0767]

向作者/读者索取更多资源

Cerenkov Luminescence Tomography (CLT) is a promising optical molecular imaging technology. It involves the three-dimensional reconstruction of the distribution of radionuclide probes inside a single object to indicate a tumor's localization and distribution. However, reconstruction using CLT suffers from severe ill-posedness, resulting in numerous artifacts within the reconstructed images. These artifacts influence the visual effect and may misguide the medical professional (diagnostician), resulting in a wrong diagnosis. Here, we proposed a deep unsupervised clustering-based post-processing framework to eliminate artifacts and facilitate high-fidelity CLT. First, an initial reconstructed image was obtained by a specific reconstruction method. Second, voxel data were generated based on the initial reconstructed result. Third, these voxels were divided into three groups, and only the group with the highest mean intensity was chosen as the final reconstructed result. A group of numerical simulation and in vivo mouse-based experiments were conducted to assess the presented framework's feasibility and potential. The results indicated that the proposed framework could reduce the number of artifacts effectively. The reconstructed image's shape and distribution were more similar to the actual light source than those obtained without the proposed framework.

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