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Title: A Comparison of Different Methods of Glow Curve Analysis for Thermoluminescent Dosimeter Data Article Type: Manuscript Draft - Full Length Article Section/Category: Radioactivity and Radiation Measurements - Elsevier Editorial System™ for Applied Radiation and Isotopes Keywords: thermoluminescent dosimeters; glow curve analysis; region of interest; computer code; TLDs; GCA; ROI; radiation dosimetry Corresponding Author: Dr. John A Harvey, BS,MS,PhD; Dr. Kimberlee Jane Kearfott, Sc.D. Corresponding Author’s Institution: University of Michigan First Author: Douglas Kripke Order of Authors: Douglas Kripke; Dr. John A Harvey, BS,MS,PhD; Dr. Kimberlee Jane Kearfott, Sc.D. Manuscript Region of Origin: USA Abstract: Thermoluminescent dosimeters (TLDs) are small crystalline materials that measure ionizing radiation dose to a person or the environment. When heated after receiving a radiation dose, TLDs emit light proportional to the dose received. The light signal is recorded as a function of temperature known as a glow curve, which consists of several peaks corresponding to different electron trap state energies. The area under the curve is directly proportional to the radiation dose. Various dosimetry applications require the individual glow peak areas. In order to determine these, a glow curve analysis (GCA) program was written to deconstruct the glow curve into its individual peaks. If individual glow peak areas are not required, a simpler region of interest (ROI) analysis program was written to determine the area under the glow curve while ignoring any noise appearing in the high temperature region. A comparison of using ROIs versus GCA will be made. This comparison will be made based on 10 TLD readings each containing the same 100 TLD chips all of which are type TLD-100 (LiF doped with Mg and Ti) irradiated at 4.4 mGy. The ROI and GCA programs will both be run over this data, and statistical analysis will be used to determine if the GCA program increases the standard deviation of glow curve areas when it generates a line of best fit to approximate the glow curve. Results show that the GCA program does not add additional variation when used to calculate the area under the glow curve.

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A Comparison of Different Methods of Glow Curve Analysis for Thermoluminescent Dosimeter Data

A Comparison of Different Methods of Glow Curve Analysis for Thermoluminescent

Dosimeter Data

Douglas Kripkea, John A. Harveya, Kimberlee J. Kearfotta,*

aDepartment of Nuclear Engineering and Radiological Sciences, Radiological Health

Engineering Laboratory, University of Michigan, 2355 Bonisteel Blvd., Ann Arbor, MI

48109-2104, USA

*Corresponding author at: Department of Nuclear Engineering and Radiological Sciences,

Radiological Health Engineering Laboratory, University of Michigan, 2355 Bonisteel

Blvd., Ann Arbor, MI 48109-2104, USA. Tel.: +1 734 763 9117; fax: +1 734 763 4540.

E-mail address: kearfott@umich.edu (K. J. Kearfott)

ABSTRACT

Thermoluminescent dosimeters (TLDs) are small crystalline materials that measure

ionizing radiation dose to a person or the environment. When heated after receiving a radiation

dose, TLDs emit light proportional to the dose received. The light signal is recorded as a

function of temperature known as a glow curve, which consists of several peaks corresponding to

different electron trap state energies. The area under the curve is directly proportional to the

radiation dose. Various dosimetry applications require the individual glow peak areas. In order

to determine these, a glow curve analysis (GCA) program was written to deconstruct the glow

curve into its individual peaks. If individual glow peak areas are not required, a simpler region of

interest (ROI) analysis program was written to determine the area under the glow curve while

ignoring any noise appearing in the high temperature region. A comparison of using ROIs

versus GCA peaks will be made.

This comparison will be made based on 10 TLD readings each containing the same 100

TLD chips all of which are type TLD-100 (LiF doped with Mg and Ti) irradiated at 4.4 mGy.

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The ROI and GCA programs will both be run over this data, and statistical analysis will be used

to determine if the GCA program increases the standard deviation of glow curve areas when it

generates a line of best fit to approximate the glow curve. Results show that the GCA program

does not add additional variation when used to calculate the area under the glow curve.

INTRODUCTION

Various techniques are used to analyze the glow curves of thermoluminescent

dosimeters. A computerized glow curve analysis (GCA) program separates the glow curve into

glow peaks, which is useful for studying fading but takes a long time since it requires generating

a line of best fit for each TLD reading. A simpler method is looking at the area under the glow

curve ignoring noise in the high temperature region known as a region of interest (ROI). A

program was recently written in VBA for Microsoft Excel to automate the process of taking

ROIs. The ROI program is quicker than the GCA program, and it uses the original data points to

determine the glow curve area.

A lot of writing and editing of computer programs was needed before a quantitative

comparison of ROIs versus GCA peaks could be made. The GCA code was recently re-written

(in Matlab R2008b with Curve Fitting Toolbox, The MathWorks Inc., 3 Apple Hill Drive,

Natrick, MA 01760) to work for all different TLD types instead of just one, and it plots the light

signal versus temperature. The high constant temperature at the end of a TLD reading created a

vertical cluster of data which caused additional error in the fitting function. Therefore, the

constant temperature data and accompanying light signal data were truncated. All data was

generated using a Harshaw TLD reader (WinREMS version PL-26732.8.0.0.0,

BICRON/Harshaw, 6801 Cochran Road, Solon, OH 44139, USA). However, when light signal

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data is exported, it is converted to arbitrary units. The conversion factor was determined, and it

was written into the GCA code to convert the light signal data back into nanoamperes.

METHODS

The data used for comparison was collected in 2008 by Elizabeth Thomas. A group of

100 TLDs (type TLD-100) was irradiated at 4.4 mGy and read out within two hours afterwards.

This same set was irradiated and then read out 10 times within a two week period using a

Harshaw Model 4500 TLD Reader with nitrogen gas. The GCA Program used Matlab to fit and

then deconstruct the glow curve. The sum of the area of peaks two, three, four, and five was

used as the GCA glow curve area (Fig 1). Many chips were not read out properly due to

improper placing on the heating plate in the reader. Readings with a figure of merit (FOM)

greater than 2 were either thrown out or refit using altered fitting parameters. 27 readings were

thrown out. The ROI program used VBA for Microsoft Excel and determined a region of

interest using the original glow curve data points ignoring noise in the high temperature region.

Once the ROI program was written, it was necessary to compare it to the old method of

taking ROIs by hand. The Radiological Health Engineering Lab at the University of Michigan

had a set of approximately 3000 TLD readings in which the old ROI method had been used.

ROIs were then generated using the program over this same data set for comparison (Table 1).

The ROI program varies greatly in run time depending on how many charts it is told to generate.

When used on a set of 100 TLDs, the average runtime with no graphs generated was 9 seconds

compared to 48 seconds with ninety graphs generated. To determine the same number of ROIs

via the old method would take anywhere from 30 minutes to an hour. For the program to work,

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the TLD numbers or identifiers must be less than six characters long. Also, errors may arise

when using different WinREMS export scripts. (See suggested export script)

For TLD types 200, 300, 400, and 900, a ROI program is not necessary because there is

no high temperature region. Hence, the ROI is equal to the entire glow curve area, and this value

can be exported from WinREMS provided it is in the export script (ROI1). Nevertheless, the

ROI program prompts the user for TLD type, and if it is a TLD type listed above, then the

program will sum the entire glow curve generating the same values as the WinREMS export

glow curve areas.

TLD-100 does have a high temperature which the ROI program ignores. It does this

based on the unique shape of TLD-100 glow curves (Fig 3&4). The program first finds the

absolute maximum of the glow curve. From this point, it continues along the glow curve until

there is an increase in the light signal between consecutive data points. If the increase is above a

certain light signal value, it continues along the glow curve. Once an increase is found below the

threshold, the program determines the local slope of the glow curve. It moves a certain distance

from the increase on each side, and determines if the slope is close to zero. These two

parameters are called interval and SlopeCheck respectively, and their value was determined

experimentally to be 1 and 0.2 (See Fig 2 and Table 1)

Data was collected and analyzed in Microsoft Excel. Each glow curve area (sum of

peaks 2, 3, 4, and 5) generated by the GCA program was compared to the corresponding ROI

generated by the ROI Program (Fig 5) without outliers. Outliers were removed using

Chauvenet’s criterion as follows. A glow curve area was deemed an outlier if it fell outside of its

confidence interval.

𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙 = 1 −1

2 ∗ 𝑆𝑎𝑚𝑝𝑙𝑒 𝑆𝑖𝑧𝑒

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Every glow curve was compared across all ten readings, across the set of one-hundred TLDs it

was read with, and across all 1000 TLDs read to determine if it was an outlier. If a reading was

an outlier in one program, it was thrown out in both. For the most part, reading that were an

outlier in the ROI method were also outliers according to the GCA method. 45 pairs of glow

curves were deemed outliers. Additionally, the standard deviation for each method was divided

by the average glow curve area for each method to calculate the coefficient of variation (Fig 6)

including outliers.

RESULTS

Quantitatively, the old and new methods for calculating ROIs have no practical

difference. The ROI program tends to underestimate the ROIs by .1, but the ROIs generated

have the exact same spread as the values determined with the old method. The ROI program is

approximately 40 to 400 times faster than the old method.

The GCA and ROI programs were compared using the methods outlines above.

Individual glow curves for both the GCA and ROI program ranged from 220 to 320. The GCA

glow curves averaged 268.6 nC, which was 6.3 nC less than the ROI program ROIs. The

normalized standard deviation for the GCA program was .03% less than that of the ROI

program. This was the case for both outliers removed and outliers included.

CONCLUSION

Since the ROI program generates nearly the same ROIs as the old method, the large time

difference in the ROI program run time versus the old method makes the ROI program a much

better way of calculating ROIs.

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With outliers removed, the average of the 100 TLD chips divided by the average standard

deviation of these 100 chips for the ROI program was 5.00%, compared to 4.97% for the GCA

program. Therefore, while the GCA program has inherent error in using a function to

approximate the glow curve data, this has no statistically significant effect on the relative

standard deviation of glow curve areas for TLD-100 irradiated at 4.4 mGy. The lower GCA

relative standard deviation can be explained by the fitting function smoothing out glow curves

and removing spontaneous spikes in the glow curve. The average lower glow curve area

generated by the GCA program is as expected since the program removes background noise (Fig

3) while the ROI program does not (Fig 4).

More research needs to be done to determine if this is the case for different TLD types

and at different levels of irradiation (see Harvey doctoral dissertation, Chapter VIII: Does

Response Linearity and Practical Factors Influencing Minimum Detectable Dose for Various

Thermoluminescent Detector Types, pgs.159-177). As the does level is lowered, the GCA

program has a harder time fitting the data, and the ROI program may fail altogether. It is likely

that the ROI program would need to be modified to look for an increase in the mean of several

data points after the absolute maximum.

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Fig 1 “Flow chart of entire computerized glow curve analysis program. The fitting function called by the main code is given in the dashed box” (Harvey, 2011, pg.149).

Fig 2 Flow chart of the ROI program, written in VBA for Microsoft Excel.

Fig 3 Output of GCA program for TLD-100 (LiF:Mg, Ti) irradiated to 4.4 mGy. The area under peaks two, three, four, and five were added together and compared to the region of interest of the ROI program.

Fig 4 Output of ROI program for TLD-100 (LiF:Mg, Ti) irradiated to 4.4 mGy. The ROI was compared to the area under peaks two, three, four, and five the GCA program. Fig 5 Coefficient of Variation for GCD versus ROI with outliers Fig 6 Glow Curve Areas Comparison with outliers removed Table 1 Comparison between ROI program and ROI old method using Gunter data (approximately 3,000 glow curves for 1,000 TLDs). Table 2 Different methods of glow curve analysis comparison using Elizabeth Thomas Data (approximately 1000 glow curves for 100 TLDs in 2008) run through each program. REFERENCES

Harvey J. A., Rodrigues, M. L., Kearfott, K. J., "A Computerized Glow Curve Analysis (GCA) Method for WinREMS Thermoluminescent Dosimeter Data using MATLAB”, Appl Radiat Isot, submitted November 11, 2010; accepted April 26, 2011.