Fast gamma-ray interaction-position estimation using k-d tree search.

Area of Science:

• Physics
• Nuclear Science
• Computational Methods

Background:

• Accurate gamma-ray interaction position estimation is crucial for various applications, including medical imaging and nuclear physics.
• Traditional methods often face limitations in speed, accuracy, or adaptability to complex detector responses.
• Existing search strategies like contracting-grid-search (CG) and vector-search (VS) may yield suboptimal results.

Purpose of the Study:

• To develop a fast and accurate method for estimating gamma-ray interaction positions.
• To introduce a k-d tree search algorithm applicable to diverse closeness metrics and detector response functions.
• To demonstrate the robustness and efficiency of the k-d tree method compared to conventional approaches.

Main Methods:

• Implementation of a k-d tree search algorithm for gamma-ray interaction position estimation.
• Integration with various closeness metrics, including Euclidean distance and maximum-likelihood estimation.
• Evaluation of the method's time complexity and accuracy against exhaustive search and other popular methods.

Main Results:

• The k-d tree search method achieves high accuracy comparable to exhaustive search.
• It offers significant speed advantages, enabling efficient processing of large reference datasets (time complexity O(log N)).
• The method demonstrates robustness, working effectively with complex mean-detector-response functions (MDRFs) and various data structures.

Conclusions:

• The developed k-d tree search method provides a fast, accurate, and robust solution for gamma-ray interaction position estimation.
• It overcomes limitations of traditional methods, offering superior performance with large datasets and complex detector responses.
• This technique is highly adaptable and suitable for demanding applications requiring precise event localization.