Study on Large-Scale 3-Dimensional Image Reconstruction of Biological Tissues Using the Quantum Mechanics

Project Details


The objective of this research proposal is to develop a method of large-scale 3-dimensional image reconstruction of biological tissues using the quantum mechanics. Based on our previous investigations (Scientific Reports, accepted, 2017), a connection between the density functional theory, a sophisticated and pragmatic method in quantum mechanics, and the methods of machine learning was successfully constructed by mapping the information from the physical space to the data space. An unsupervised searching algorithm of the cluster number as well as the corresponding cluster boundaries was proposed based on the principle of energy conservation. Morphologies of Lagrangian density functional reveal significant data boundaries between clusters, while that of Hamiltonian density functional connect the components having the most similar local information. Several interdisciplinary problems of pattern recognition from physical to biological systems were enumerated to elucidate the feasibility and the accuracy of the proposed algorithm. The study is the pioneering attempt to propose such a methodology to solve these issues. Therefore, in this proposal we would like to extend this fundamental study to a pragmatic application on the large-scale 3-dimensional image reconstruction of biological tissues. Two contemporary techniques, the Lucas-Kanade optic flow and the pixel connectivity will be employed to resolve the issues from image film alignment and labeling of pixels. Meanwhile, these employed methods will be combined with our previous work and the mathematical framework of quantum mechanics. The mathematical connection between the employed methods and the quantum mechanics will be carefully derived and published. Then the relevant algorithms will also be constructed on the platforms of MATLAB and TensorFlow for specific applications and problem solutions. Eventually, we hope the proposed mathematical connection and the algorithm can make contribution to the clinical investigation and translational science.
Effective start/end date1/08/1831/07/19

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 14 - Life Below Water
  • SDG 15 - Life on Land
  • SDG 17 - Partnerships for the Goals


  • Biological sciences
  • Computational biology
  • bioinformatics
  • Image processing
  • Statistical methods
  • MRI
  • Brainbow
  • unsupervised learning
  • Lucas-Kanade optic flow
  • pixel connectivity
  • density functional theory


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