System and Method for Resolving Gamma-Ray Spectra

Abstract

A system for identifying radionuclide emissions is described. The system includes at least one processor for processing output signals from a radionuclide detecting device, at least one training algorithm run by the at least one processor for analyzing data derived from at least one set of known sample data from the output signals, at least one classification algorithm derived from the training algorithm for classifying unknown sample data, wherein the at least one training algorithm analyzes the at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by the detecting device.

1 . A system for identifying radionuclide emissions, comprising:
 at least one processor for processing output signals from a radionuclide detecting device;   at least one training algorithm run by said at least one processor for analyzing data derived from at least one set of known sample data from said output signals;   at least one classification algorithm derived from said training algorithm for classifying unknown sample data;   wherein said at least one training algorithm analyzes said at least one sample data set to derive at least one rule used by said classification algorithm for identifying at least one radionuclide emission detected by said detecting device.   
     
     
         2 . The system according to  claim 1 , wherein said at least one training algorithm is an artificial intelligence algorithm. 
     
     
         3 . The system according to  claim 2 , wherein said artificial intelligence algorithm is a support-vector machine training algorithm. 
     
     
         4 . The system according to  claim 1 , wherein said known or unknown sample data is spectral data. 
     
     
         5 . The system according to  claim 4 , wherein said at least one set of known sample spectral data is labeled with the presence or absence of a signature of said at least one radionuclide emission. 
     
     
         6 . The system according to  claim 1 , wherein said system identifies the at least one radionuclide in real-time. 
     
     
         7 . The system according to  claim 1 , wherein said radionuclide emissions are gamma rays. 
     
     
         8 . The system according to  claim 1 , further comprising a peak-fitting algorithm run in parallel with said learning and classification algorithms. 
     
     
         9 . The system according to  claim 8 , wherein decision logic is used to compare the results of said peak-fitting algorithm and said learning and classification algorithms. 
     
     
         10 . The system according to  claim 1 , wherein said at least one rule distinguishes between positive and negative sample data.