US 2013/0173220 A1 · Radiation Analysis System and Method

Abstract

A radiation analysis system/method that automatically optimizes the efficiency calibration of a counting system based on benchmark data and variable parameters associated with radiation source/sensor/environment (RSSE) combinations is disclosed. The system/method bifurcates RSSE context (SSEC) model parameters into WELL-KNOWN (fixed) parameters (WNP) and NOT-WELL-KNOWN (variable) parameters (NWP). The NWP have associated lower/upper limit values (LULV) and a shape distribution (LUSD) describing NWP characteristics. SSEC models are evaluated using randomized statistical NWP variations or by using smart routines that perform a focused search within the LULV/LUSD to generate model calibration values (MCV) and calibration uncertainty values (UCV) describing the overall SSEC efficiencies. Sensor measurements using the MCV/UCV generate a measurement value and uncertainty estimation value. An exemplary embodiment optimizes geometry models of radiation sources by benchmarking with respect to measurement data from spectroscopy detectors and/or dose rate detectors.

Claims28

Dependent claims

A radiation analysis system comprising:

(a) radiation source detection subsystem (RSDS);
(b) batch definition processor (BDFP);
(c) pre-analysis processor (PREP);
(d) efficiency optimization processor (EOPT);
and
(e) post-analysis processor (POST);
wherein
said RSDS is configured with a radiation sensor (RSEN) to detect radiation emitted from a radiation sample (RSAM) and output digital radiation detection values (DRDV) associated with said emitted radiation;
said BDFP is configured to accept user input from a graphical user interface (GUI) to define radiation analysis parameters (RAP) to be used in analyzing said RS;
said PREP is configured to read said DRDV and perform a preliminary analysis to define a RSAM efficiency estimate (RSEE);
said EOPT is configured to analyze said DRDV and perform an automated efficiency value optimization (AEVO) to generate absolute efficiency values (AEV) for said RSAM using said RAP and said RSEE as a starting point for said analysis;
said EOPT is configured to rank said AEVO with a Figure-Of-Merit (FOM) based on the correlation of said DRDV to model functions comprising WELL-KNOWN-PARAMETERS (WNP) and NOT-WELL-KNOWN-PARAMETERS (NWP);
and said POST is configured to generate reports of said AEV to said GUI.

10 more elements

A radiation analysis method, said method operating in conjunction with a radiation analysis system, said system comprising:

(a) radiation source detection subsystem (RSDS);
(b) batch definition processor (BDFP);
(c) pre-analysis processor (PREP);
(d) efficiency optimization processor (EOPT);
and
(e) post-analysis processor (POST);
wherein
said RSDS is configured with a radiation sensor (RSEN) to detect radiation emitted from a radiation sample (RSAM) and output digital radiation detection values (DRDV) associated with said emitted radiation;
said BDFP is configured to accept user input from a graphical user interface (GUI) to define radiation analysis parameters (RAP) to be used in analyzing said RS;
said PREP is configured to read said DRDV and perform a preliminary analysis to define a RSAM efficiency estimate (RSEE);
said EOPT is configured to analyze said DRDV and perform an automated efficiency value optimization (AEVO) to generate absolute efficiency values (AEV) for said RSAM using said RAP and said RSEE as a starting point for said analysis;
said EOPT is configured to rank said AEVO with a Figure-Of-Merit (FOM) based on the correlation of said DRDV to model functions comprising WELL-KNOWN-PARAMETERS (WNP) and NOT-WELL-KNOWN-PARAMETERS (NWP);
and
said POST is configured to generate reports of said AEV to said GUI;
wherein said method comprises the steps of:
(1) defining a radiation source/sensor environment (RSSE) model comprising a mathematical model of said RSAM and said RSEN;
(2) defining default/expected/fixed dimensions/values for parameters associated with said RSSE model;
(3) defining values/parameters that are model variables within said RSSE model;
(4) defining a range of variation and distribution shape for each said model variable;
(5) randomly selecting a value for each said model variable using distribution shape/limits to create a test mathematical model of a possible source-detector measurement configuration in said RSEE;
(6) selecting optimization benchmark(s) available from data measured from said RSDS;
(7) computing the source-detector measurement efficiency (SDME) in said RSEE using said test mathematical model;
(8) computing the Figure-Of-Merit (FOM) for said SDME;
(9) determining if statistical accuracy has been reached in said FOM, and if not, proceeding to said step (5);
(10) selecting a “best” mathematical model(s) by either:
(a) selecting a predefined number of said test mathematical models that correspond to optimal values of said FOM for said SDME using a Best Random Fit;
or
(b) alternatively selecting an optimal test mathematical model using a focused Smart Routine search algorithm;
and
(11) calculating the mean and standard deviation describing the uncertainty of said SDME for said “best” mathematical model(s).

27 more elements

A radiation analysis method comprising:

(1) locating a set of radiation detectors at various pre-selected measurement geometry locations with respect to a radioactive item;
(2) recording radiation data measurement values collected from said radiation detectors at said pre-selected measurement geometry locations;
(3) transferring said radiation data to a computer system;
(4) activating an automated optimization process on said computer system;
(5) reading an initial geometry parameters file with said computer system and retrieving inputs from predefined files to define optimization process setup parameters;
(6) using an initial geometry model to calculate with said computer system radiation detection values at all said pre-selected measurement geometry locations;
(7) computing with said computer system the Figure-Of-Merit (FOM) for selected benchmarks;
(8) determining with said computer system if the same geometry model gives consistent results for all measurements at all measurement locations, and if so, proceeding to step (11);
(9) varying with said computer system the variable geometry parameters using a Best Random Fit Routine or Smart Routine;
(10) for each model or iteration, calculating with said computer system a new radiation value and the benchmark FOM by proceeding to said step (7);
(11) selecting with said computer system an optimum geometry model as the one that gives consistent results with all said measurement values;
(12) reporting optimized radiation measurement values to a user with said computer system;
and
(13) calculating with said computer system measurement values at a selected location with respect to said radioactive item.

11 more elements

Dependencies are inferred from the claim text.

Radiation Analysis System and Method

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Radiation Analysis System and Method

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Abstract

Claims28