Calibration Method for ML Estimation of 3D Interaction Position in a Thick Gamma-Ray Detector
By William C. J. Hunter, H. H. Barrett, Lars R. Furenlid. 2009
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High-energy (> 100 keV) photon detectors are often made thick relative to their lateral resolution in order to improve their photon-detection efficiency. To avoid issues of parallax and increased signal variance that result from random interaction depth, we must determine the 3D interaction position in the imaging detector. With this goal in mind, we examine a method of calibrating response statistics of a thick-detector gamma camera to produce a maximum-likelihood estimate of 3D interaction position. We parameterize the mean detector response as a function of 3D position, and we estimate these parameters by maximizing their likelihood given prior knowledge of the pathlength distribution and a complete list of camera signals for an ensemble of gamma-ray interactions. Furthermore, we describe an iterative method for removing multiple-interaction events from our calibration data and for refining our calibration of the mean detector response to single interactions. We demonstrate this calibration method with simulated gamma-camera data. We then show that the resulting calibration is accurate and can be used to produce unbiased estimates of 3D interaction position.
Subfields: Biomedical Engineering, Radiation, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Maximum-Likelihood Estimation With a Contracting-Grid Search Algorithm (2010, methodology): “The contracting-grid algorithm is extendable to multiple dimensions and was previously implemented for a ML search task [28].”
- Maximum-Likelihood Methods for Processing Signals From Gamma-Ray Detectors (2009, methodology): “The data used to perform the parameterestimationcanincludespectraobtainedwithmultiple gamma-rayenergiesorwithscannedbeamsatotherthannormal incidence. For more details, see [ 10 ], [37], [38].”