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Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm.

Jacob Y Hesterman¹, Luca Caucci, Matthew A Kupinski

¹Bioscan, Inc., Washington, DC 20007 USA.

IEEE Transactions on Nuclear Science

|September 9, 2010

Summary

This summary is machine-generated.

A novel fast search algorithm enhances multi-dimensional data processing. This algorithm accelerates maximum-likelihood position estimation in gamma cameras, achieving significant speedups on specialized hardware for real-time imaging.

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Area of Science:

Computer Science
Medical Imaging
Signal Processing

Background:

Accurate interaction localization is crucial for compact gamma cameras.
Processing photomultiplier tube (PMT) signals for position estimation presents computational challenges.
Existing methods may lack the speed required for real-time applications.

Purpose of the Study:

Introduce a fast, multi-dimensional search algorithm.
Demonstrate its application in 2D and 3D maximum-likelihood position estimation for gamma cameras.
Evaluate the algorithm's performance and potential for real-time imaging.

Main Methods:

Developed a general-purpose fast search algorithm for multi-dimensional spaces.
Implemented the algorithm for 2D maximum-likelihood position estimation using Cell/BE processors.
Implemented the algorithm for 3D maximum-likelihood position estimation using Graphics Processing Units (GPUs).
Explored parallel pipeline implementation for specialized hardware like Field-Programmable Gate Arrays (FPGAs).

Main Results:

Achieved processing speeds exceeding one million events per second for 2D position estimation on Cell/BE processors (20x speedup).
Achieved processing speeds of nearly 250,000 events per second for 3D position estimation on GPUs (250x speedup).
Demonstrated efficient parallelization capabilities suitable for hardware implementation.

Conclusions:

The introduced fast search algorithm significantly accelerates position estimation in gamma camera signal processing.
The algorithm's parallelizability and high performance on specialized hardware (FPGAs, Cell/BE, GPUs) make it viable for real-time imaging.
This advancement holds promise for improving the efficiency and capabilities of medical imaging systems.