1. Grand Valley State University
Erica Manduzzi, B.S.
Grand Valley State University
Dosimetric Impact of Radiotherapy Plans with the Use of a Metal Artifact Reduction Algorithm Applied during CT Simulation
Introduction
Results
Conclusion
Methods to overcome metal artifacts in computed tomography (CT) have been researched and developed for nearly 40 years. When x-rays pass through metal, physical effects are created that hinder the diagnostic quality of the image. Specifically for radiotherapy treatment plans, these physical effects (metal artifacts) in the CT images have the ability to lead to dose distribution calculation errors, potentially affecting patient treatment outcome. Multiple methods and algorithms have been proposed and developed to eliminate metal artifacts by correcting the underlying image data and improving image quality.
The GE Healthcare Smart MAR algorithm uses an automated, three-stage projection based process to improve CT image data. The purpose of this study is to assess the dosimetric impact that the GE Healthcare Smart MAR algorithm has on radiotherapy plans when applied during CT simulation.
The results of this study indicate that the GE Healthcare Smart MAR algorithm does not have a significant impact on dose to the PTV, nor on the conformity index or gradient measure value compared to plans without a form of MAR applied or with manual correction. Although the algorithm did not have a significant impact to these five measured values, visual change was seen in a few patients between plans with MAR applied and plans without, therefore, change is still possible in overall isodose distribution throughout the patient, as indicated in Figure 2.
Although a statistically significant dosimetric impact was not found between the three plans constructed for each patient, previous studies have shown that the GE Healthcare Smart MAR algorithm accurately reduces metal artifact, improves CT accuracy, and decreases image noise; therefore, this product can be implemented within the clinic to increase confidence in contouring near metal artifact, as well as reduce time required to manually correct HU values.
Figure 1. Axial slice of a sample patient containing metallic hardware with the Smart MAR applied (left) and without any MAR correction (right).
Methods
Figure 2. Plan evaluation assessing isodose distribution with respected DVH of a sample patient with metallic hardware. Plan evaluation displays a plan with the GE Healthcare Smart MAR applied (left top and bottom) and one without any form of MAR correction (right top and bottom) with their respective isodose lines.
DVH includes dose to volume comparisons of the left parotid gland (brown), spinal cord (blue), oral cavity (magenta), and PTV (red), for a 3DCRT plan with MAR applied (square) and without MAR applied (diamond).
External beam radiation therapy (EBRT) plans of fifteen (n=15) patients containing metallic hardware in their treatment area were retrospectively analyzed.
9 (60%) Head and neck plans
4 (27%) Pelvis plans
2 (13%) Spine plans
Additionally, 80% RT plans utilized VMAT and 20% utilized 3DCRT.
Two additional treatment plans (without any form of correction and with manual correction) were also constructed for each patient.
Plan settings (Isocenter placement, MUs, normalization, and plan geometry) were equivalent for each of the three plans within each patient.
Dosimetric impact was assessed by analyzing multiple metrics between the three plans per patient including:
PTV maximum dose, PTV minimum dose, PTV mean dose, conformity index, and gradient measure
References
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Variable
Test Statistic (F)
p value
Max PTV Dose
1.226
0.309
Min PTV Dose
0.239
0.687
Mean PTV Dose
1.054
0.343
Conformity Index
0.864
0.433
Gradient Measure
0.090
0.914
Table 1.
Multiple, independent repeated ANOVA tests were performed. The significance level was set at The Smart MAR algorithm did not have a significant dosimetric impact on any of the five variables when applied to the radiotherapy plans (GE MAR correction vs. no correction vs. manual correction).
Acknowledgements
Scott Green & Kristen Vu, Research Advisors
Maggie Kurp-Dilan, Ina Sala, & Ahpa Plypoo, Clinical Preceptors
GVSU Statistical Consulting Center
Dr. B. Sango Otieno, Associate Professor