The impact of CT window settings on the contouring of a moving target: A phantom study

Open Source 3D Printed Lung Tumor Movement Simulator for Radiotherapy Quality Assurance

In OECD (Organization for Economic Co-operation and Development) countries, cancer is one of the main causes of death, lung cancer being one of the most aggressive. There are several techniques for the treatment of lung cancer, among which radiotherapy is one of the most effective and least invasive for the patient. However, it has associated difficulties due to the moving target tumor. It is possible to reduce the side effects of radiotherapy by effectively tracking a tumor and reducing target irradiation margins. This paper presents a custom electromechanical system that follows the movement of a lung tumor. For this purpose, a hysteresis loop of human lung movement during breathing was studied to obtain its characteristic movement equation. The system is controlled by an Arduino, steppers motors and a customized 3D printed mechanism to follow the characteristic human breathing, obtaining an accurate trajectory. The developed device helps the verification of individualized radiation treatment plans and permits the improvement of radiotherapy quality assurance procedures.

Moving targets in 4D-CTs versus MIP and AIP: comparison of patients data to phantom data

Purpose

Maximum (MIP) and average intensity projection (AIP) CTs allow rapid definition of internal target volumes in a 4D-CT. The purpose of this study was to assess the accuracy of these techniques in a large patient cohort in combination with simulations on a lung phantom.

Methods

4DCT data from a self-developed 3D lung phantom and from 50 patients with lung tumors were analyzed. ITVs were contoured in maximum (ITV_MIP) and average intensity projection (ITV_AIP) and subsequently compared to ITVs contoured in 10 phases of a 4D-CT (ITV₁₀). In the phantom study additionally a theoretical target volume was calculated for each motion and compared to the contoured volumes.

Results

ITV₁₀ overestimated the actual target volume by 9.5% whereas ITV_MIP and ITV_AIP lead to an underestimation of − 1.8% and − 11.4% in the phantom study. The ITV_MIP (ITV_AIP) was in average − 10.0% (− 18.7%) smaller compared to the ITV₁₀. In the patient CTs deviations between ITV₁₀ and MIP/AIP were significantly larger (MIP: – 20.2% AIP: -33.7%) compared to this. Tumors adjacent to the chestwall, the mediastinum or the diaphragm showed lower conformity between ITV₁₀ and ITV_MIP (ITV_AIP) compared to tumors solely surrounded by lung tissue. Large tumor diameters (> 3.5 cm) and large motion amplitudes (> 1 cm) were associated with lower conformity between intensity projection CTs and ITV₁₀₋.

Conclusion

The application of MIP and AIP in the clinical practice should not be a standard procedure for every patient, since relevant underestimation of tumor volumes may occur. This is especially true if the tumor borders the mediastinum, the chest wall or the diaphragm and if tumors show a large motion amplitude.

The importance of surrounding tissues and window settings for contouring of moving targets

AIM

The aim of the study was to assess the importance of surrounding tissues for the delineation of moving targets in tissue-specific phantoms and to find optimal settings for lung, soft tissue, and liver tumors.

MATERIALS AND METHODS

Tumor movement was simulated by a water-filled table tennis ball (target volume, TV). Three phantoms were created: corkboards to simulate lung tissue (lung phantom, LunPh), animal fat as fatty soft tissue (fatty tissue phantom, FatPh), and water enhanced with contrast medium as the liver tissue (liver phantom, LivPh). Slow planning three-dimensional compute tomography images (3D-CTs) were acquired with and without phantom movements. One-dimensional tumor movement (1D), three-dimensional tumor movement (3D), as well as a real patient's tumor trajectories were simulated. The TV was contoured using two lung window settings, two soft-tissue window settings, and one liver window setting. The volumes were compared to mathematical calculated values.

RESULTS

TVs were underestimated in all phantoms due to movement. The use of soft-tissue windows in the LivPh led to a significant underestimation of the TV (70.8% of calculated TV). When common window settings [LunPh + 200 HU/-1,000 HU (upper window/lower window threshold); FatPh: + 240 HU/-120 HU; LivPh: + 175 HU/+ 50 HU] were used, the contoured TVs were: LivPh, 84.0%; LunPh, 93.2%, and FatPh, 92.8%. The lower window threshold had a significant impact on the size of the delineated TV, whereas changes of the upper threshold led only to small differences.

CONCLUSION

The decisive factor for window settings is the lower window threshold (for adequate TV delineation in the lung and fatty-soft tissue it should be lower than density values of surrounding tissue). The use of a liver window should be considered.