Automatic Channel Fault Detection and Diagnosis System for a Small   Animal APD-Based Digital PET Scanner
  Fault detection and diagnosis is critical to many applications in order to ensure proper operation and performance over time. Positron emission tomography (PET) systems that require regular calibrations by qualified scanner operators are good candidates for such continuous improvements. Furthermore, for scanners employing one-to-one coupling of crystals to photodetectors to achieve enhanced spatial resolution and contrast, the calibration task is even more daunting because of the large number of independent channels involved. To cope with the additional complexity of the calibration and quality control procedures of these scanners, an intelligent system (IS) was designed to perform fault detection and diagnosis (FDD) of malfunctioning channels. The IS can be broken down into four hierarchical modules: parameter extraction, channel fault detection, fault prioritization and diagnosis. Of these modules, the first two have previously been reported and this paper focuses on fault prioritization and diagnosis. The purpose of the fault prioritization module is to help the operator to zero in on the faults that need immediate attention. The fault diagnosis module will then identify the causes of the malfunction and propose an explanation of the reasons that lead to the diagnosis. The FDD system was implemented on a LabPET avalanche photodiode (APD)-based digital PET scanner. Experiments demonstrated a FDD Sensitivity of 99.3 % (with a 95% confidence interval (CI) of: [98.7, 99.9]) for major faults. Globally, the Balanced Accuracy of the diagnosis for varying fault severities is 92 %. This suggests the IS can greatly benefit the operators in their maintenance task. 
Jonathan Charest, Jean-Fran\c{c}ois Beaudoin, Jules Cadorette, Roger   Lecomte, Charles-Antoine Brunet, R\'ejean Fontaine
cs.AI nucl-ex