Document Type

Article

Date of Original Version

2013

DOI

10.1145/2506583.2506622

Abstract

Coronary heart disease is the single largest killer of Americans so improved means of detecting risk factors before arterial obstructions appear are expected to lead to a improvement in quality of life with a reduced cost. This paper introduces a new approach to 3-D reconstruction of individual particles based on statistical modeling from a sparse set of 2-D projection images. This paper introduces a new approach to 3-D reconstruction of individual particles based on statistical modeling from a sparse set of 2-D projection images. The method is in contrast to the current state of practice where reconstruction is performed via signal processing or Bayesian methods that use averaged images acquired from an ensemble of particles. As such, this new approach has its impetus in use for novel diagnostic tests such as LDL and HDL particle shape characterization. The approach is also expected to have uses in areas such as quality assurance for drug delivery nano-technologies and for general proteomic studies.

The individual particle reconstruction algorithm is based on a hidden Markov model. Higher order Markov chain statistics, which are generated from the a priori model of the target of interest, can be derived from traditional methods such as single particle reconstruction and/or the underlying physical properties of the particle. By placing the reconstruction voxel space at a 45° angle to the projection image, 4-passes of the HMM processing can be performed from a single image. Reconstruction results from a simple model and a single projection image resulted in better than 98% reconstruction accuracy as compared to the original target.

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