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.
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.
Authors: Laura M Fayad; Yinpeng Jin; Andrew F Laine; Yahya M Berkmen; Gregory D Pearson; Benjamin Freedman; Ronald Van Heertum Journal: Radiology Date: 2002-06 Impact factor: 11.105
Authors: Maciej Pech; Konrad Mohnike; Gero Wieners; Ewa Bialek; Oliver Dudeck; Max Seidensticker; Nils Peters; Peter Wust; Günther Gademann; Jens Ricke Journal: Strahlenther Onkol Date: 2008-05 Impact factor: 3.621
Authors: Markus Oechsner; Barbara Chizzali; Michal Devecka; Stefan Münch; Stephanie Elisabeth Combs; Jan Jakob Wilkens; Marciana Nona Duma Journal: Strahlenther Onkol Date: 2017-07-19 Impact factor: 3.621
Authors: Markus Oechsner; Barbara Chizzali; Michal Devecka; Stephanie Elisabeth Combs; Jan Jakob Wilkens; Marciana Nona Duma Journal: Radiat Oncol Date: 2016-10-26 Impact factor: 3.481
Authors: Kai Joachim Borm; Markus Oechsner; Moritz Wiegandt; Andreas Hofmeister; Stephanie E Combs; Marciana Nona Duma Journal: BMC Cancer Date: 2018-07-24 Impact factor: 4.430