TY - GEN
T1 - Calibration and Processing of a Waveform MDRF for a Clinical Gamma Camera
AU - Momsen, Neil
AU - Furenlid, Lars R.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - A classic clinical gamma camera was reverse engineered and retrofitted for the use of time dependent waveform maximum-likelihood techniques for the task of gamma-ray event parameter estimation. The original detector electronics were outfitted with tap-in circuit boards that intercept the photomultiplier tube (PMT) signals. These amplifiers send the temporal PMT voltage signal to off-camera 12-bit waveform digitizers. To include the temporal waveform data into the maximum-likelihood estimation of gamma-ray event parameters, waveform Mean Detector Response Functions (MDRF) are necessary. The collection of this MDRF differs substantially from conventional MDRFs because additional high-speed acquisition electronics must be used. The MDRF calibration requires 3 main processing steps which are done with a conventional MDRF: a spectral filter; a smoothing/fitting step; and a position-estimation filter. The waveform MDRF information can then be extracted from this result by tracking which events are filtered out from the dataset.
AB - A classic clinical gamma camera was reverse engineered and retrofitted for the use of time dependent waveform maximum-likelihood techniques for the task of gamma-ray event parameter estimation. The original detector electronics were outfitted with tap-in circuit boards that intercept the photomultiplier tube (PMT) signals. These amplifiers send the temporal PMT voltage signal to off-camera 12-bit waveform digitizers. To include the temporal waveform data into the maximum-likelihood estimation of gamma-ray event parameters, waveform Mean Detector Response Functions (MDRF) are necessary. The collection of this MDRF differs substantially from conventional MDRFs because additional high-speed acquisition electronics must be used. The MDRF calibration requires 3 main processing steps which are done with a conventional MDRF: a spectral filter; a smoothing/fitting step; and a position-estimation filter. The waveform MDRF information can then be extracted from this result by tracking which events are filtered out from the dataset.
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U2 - 10.1109/NSS/MIC42101.2019.9059790
DO - 10.1109/NSS/MIC42101.2019.9059790
M3 - Conference contribution
AN - SCOPUS:85083566232
T3 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
BT - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019
Y2 - 26 October 2019 through 2 November 2019
ER -