研究目的
Developing an image reconstruction method for a Compton camera that improves image quality by accurately modeling the error of the Compton scattering angle, considering the detector material's specific error distribution function and the variation of its function parameters depending on each measurement event.
研究成果
The developed error distribution model of the Compton cone half-apex angle, optimized for the detector material of the semiconductor Compton camera GREI, improves the spatial resolution of the reconstructed image and reduces artifacts. The method's effectiveness is demonstrated through application to simulated data and imaging data of a tumor-bearing live mouse.
研究不足
The study acknowledges challenges in improving the quantitative accuracy of the reconstructed images and the need for further improvement in the detector-position-resolution model. The simplistic sensitivity correction and the fixed effect of position resolution on the angular error are noted as limitations.
1：Experimental Design and Method Selection:
The study focuses on improving the image quality of a Compton camera by accurately modeling the error distribution of the Compton cone half-apex angle. The methodology includes the development of an error distribution model that incorporates Doppler broadening and detector energy resolution.
2：Sample Selection and Data Sources:
The method is applied to simulated data assumed to be measured by a Ge-semiconductor Compton camera GREI and imaging data of a tumor-bearing live mouse obtained using GREI.
3：List of Experimental Equipment and Materials:
The GREI-II system, composed of two double-sided orthogonal-strip Ge detectors with a three-dimensional position sensing capability, is used.
4：Experimental Procedures and Operational Workflow:
The image reconstruction method involves the backprojection of the list-mode maximum-likelihood expectation-maximization (LM-ML-EM) method, incorporating the developed error model as the system response function.
5：Data Analysis Methods:
The spatial resolution and image roughness are evaluated by comparing images reconstructed with variable and fixed angular error models.
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