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Biomedical Imaging and Instrumentation

Biomedical Imaging and Instrumentation - Poster Session D

Poster F8 - Optimization of X-ray Cabinet System for Characterized Low-Dosage Irradiation of Cells

Friday, October 25, 2024
3:30 PM – 4:30 PM EST
Location: Exhibit Hall E, F & G

Introduction:  Radiation is often utilized in the medical field for both therapeutic treatments and diagnostic tests. Whereas the effects of high-level radiation on cells and tissues are well studied, such as with radiotherapy research, there is a lack of comprehensive understanding of low-level radiation effects, despite its common presence in both daily life, such as with naturally occurring background radiation, and medical diagnostics, such as with X-rays and CT scans. Therefore, due to the importance of understanding the effects of low-level radiation better, the development of a proper apparatus to irradiate biological samples is required. Recently, work has been done by our lab to study the effects of very low-dose radiation on various cells using an X-ray cabinet fluorescence irradiation setup; however, this setup has limitations such as the following: an inability to provide sufficient energy to certain biological samples, such as tissue, in an acceptable time period, inadequate data for optimal calibration of the XPIN®-XT X-Ray Detector, and a cumbersome process for calculating total energy exposure. In order to address these limitations, our team worked together to optimize and design various subcomponents, both in regard to the hardware and software aspects, of the X-ray cabinet. Ultimately, our goal in implementing these optimization steps for the X-ray cabinet was to create a more potent, accurate, and streamlined irradiation process to not only allow for a more capable X-ray cabinet but also allow for a more efficient system to irradiate biological samples.

Materials and

Methods:  Firstly, to accurately determine the total energy that the detector is being exposed to, we had to create a calibration equation that converted the channel number of the XPIN®-XT X-Ray Detector to energy in KeV, as shown in Figure 2. We did this by using the X-ray cabinet in the characteristic setup to strike various targets, including a copper plate, steel plate, lead plate, and calcium powder. Using the emission energy data, which is characteristic of each element and appears at a specific channel number, we made a graph with the channel number vs. energy and fit a linear equation to the data points, which allows us to convert channel number to energy. Secondly, in order to streamline the process of calculating the total energy produced, we developed a Python program that creates a polynomial fit of the counts vs. energy data, and integrates the polynomial with respect to energy. Finally, it converts the total energy in KeV to Joules by using the conversion factor of 1 Joule of energy being 6.242e+15 KeV, which is shown in Figure 3. We developed the code with assistance from ChatGPT’s 3.5 model, the validity of the calculations of which we verified by using an integral calculator. Lastly, in order to maximize the total energy that a biological sample is exposed to, we changed the thickness of the filter, as well as the voltage that the X-ray source runs at, and verified the total energy the detector was exposed to using our Python program.

Results, Conclusions, and Discussions: Due to the usage of the emission energy data of a large variety of materials for the channel vs. energy calibration curve, as well as the calculated R^2 value of 1.00, which indicates a strong level of linearity, we were able to develop a robust equation to convert channel number to energy, in KeV. Furthermore, by developing the Python program, we streamlined the calculations for the total energy value and were able to use it to analyze filter optimization data. Finally, we determined that compared to the control, unoptimized setup of 0.8 mm of aluminum filter (10 kV, 4 µA), the optimized setup of 2.5 mm of aluminum filter (35 kV, 4 µA) produced an 8.19-fold increase in total energy, as shown in figure 4.
It is important to gain a more comprehensive understanding of the effects of low-level radiation on biological matter, as we are constantly exposed to low-level radiation from various sources. For that reason, it is crucial to be able to use an X-ray irradiation setup, such as the X-ray cabinet, in the most accurate and effective manner possible. By creating a calibration curve from the emission data from various sources, as well as a linear fit curve with an R^2 value of 1.00, scientists employing a similar setup are able to measure the energy a biological sample is exposed to with more accuracy. Furthermore, by developing a Python program that calculates total energy simply by inputting energy vs. counts data, scientists with a lack of a robust background in physics and programming are able to determine and analyze the total energy intended samples are exposed to, thus helping to bridge the gap between biological and physical radiation research. Finally, by optimizing the filter and voltage setup, scientists may be able to not only test cells at higher dose rates, and thus faster times, but due to the previous limitations of a lack of energy prohibiting effective irradiation of tissue, this new setup may allow for the testing of a larger variety of biological samples, including tissue.

Acknowledgements (Optional):  This research was done at Clemson University for the NSF REU entitled "Nature’s Machinery through the Prism of Physics, Biology, Chemistry and Engineering" and was made possible due to the support of the NSF grant award #2349368. Further acknowledgment is extended to Dr. Dean’s lab as well as Dr.Takacs’ lab for providing mentorship and access to key equipment.