Dynamic coded x-ray tomosynthesis (CXT) uses a set of encoded x-ray sources to interrogate objects lying on a moving conveyor mechanism. The object is reconstructed from the encoded measurements received by the uniform linear array detectors. We propose a multi-objective optimization (MO) method for structured illuminations to balance the reconstruction quality and radiation dose in a dynamic CXT system. The MO framework is established based on a dynamic sensing geometry with binary coding masks. The Strength Pareto Evolutionary Algorithm 2 is used to solve the MO problem by jointly optimizing the coding masks, locations of x-ray sources, and exposure moments. Computational experiments are implemented to assess the proposed MO method. They show that the proposed strategy can obtain a set of Pareto optimal solutions with different levels of radiation dose and better reconstruction quality than the initial setting.
Compressive X-ray tomosynthesis uses a few two-dimensional projection measurements modulated by coding masks to reconstruct the three-dimensional object that can be sparsely represented on a predefined basis. However, the coding mask optimization and object reconstruction require significant computing resources. In addition, existing methods fall short to exploits the synergy between the encoding and reconstruction stages to approach the global optimum. This paper proposes a model-driven deep learning (MDL) approach to significantly improve the computational efficiency and accuracy of tomosynthesis reconstruction. A unified framework is developed to jointly optimize the coding masks and the neural network parameters, which effectively increase the degrees of optimization freedom. It shows that the computational efficiency of coding mask optimization and image reconstruction can be improved by more than one order of magnitude. Furthermore, the performance of reconstruction results is significantly improved.
more » « less- PAR ID:
- 10276165
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 29
- Issue:
- 15
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 24576
- Size(s):
- Article No. 24576
- Sponsoring Org:
- National Science Foundation
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Compressive x-ray tomosynthesis (CXT) uses a set of encoded projection measurements from different incident angles to reconstruct the object under inspection. We consider the variable motion of objects on a conveyor mechanism and establish an imaging model based on the sensing geometry of a dynamic CXT system. Then, a numerical algorithm is proposed to optimize the structured illumination series to improve reconstruction accuracy with reduced radiation dose. Compared with the state-of-the-art method, the proposed strategy increases the degrees of optimization freedom by jointly optimizing the coding mask patterns, locations of x-ray sources, and exposure moments in the CXT system, thus obtaining better reconstruction performance. A genetic algorithm is applied to achieve the optimization results. It shows that the proposed method outperforms the traditional CXT approach by further improving reconstruction performance under comparable radiation dose.
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