Background And Purpose: Dose planning requires a CT scan which provides the electron density distribution for dose calculation. MR provides superior soft tissue contrast compared to CT and the use of MR-alone for prostate planning would provide further benefits such as lower cost to the patient. This study compares the accuracy of MR-alone based dose calculations with bulk electron density assignment to CT-based dose calculations for prostate radiotherapy.
Materials And Methods: CT and whole pelvis MR images were contoured for 39 prostate patients. Plans with uniform density and plans with bulk density values assigned to bone and tissue were compared to the patient's gold standard full density CT plan. The optimal bulk density for bone was calculated using effective depth measurements. The plans were evaluated using ICRU point doses, dose volume histograms, and Chi comparisons. Differences in spatial uniformity were investigated for the CT and MR scans.
Results: The calculated dose for CT bulk bone and tissue density plans was 0.1±0.6% (mean±1 SD) higher than the corresponding full density CT plan. MR bulk bone and tissue density plans were 1.3±0.8% lower than the full density CT plan. CT uniform density plans and MR uniform density plans were 1.4±0.9% and 2.6±0.9% lower, respectively. Paired t-tests performed on specific points on the DVH graphs showed that points on DVHs for all bulk electron density plans were equivalent with two exceptions. There was no significant difference between doses calculated on Pinnacle and Eclipse. The dose distributions of six patients produced Chi values outside the acceptable range of values when MR-based plans were compared to the full density plan.
Conclusions: MR-alone bulk density planning is feasible provided bone is assigned a density, however, manual segmentation of bone on MR images will have to be replaced with automatic methods. The major dose differences for MR bulk density plans are due to differences in patient external contours introduced by the MR couch-top and pelvic coil.
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http://dx.doi.org/10.1016/j.radonc.2011.01.012 | DOI Listing |
Front Plant Sci
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Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China.
Three-dimensional (3D) LiDAR is crucial for the autonomous navigation of orchard mobile robots, offering comprehensive and accurate environmental perception. However, the increased richness of information provided by 3D LiDAR also leads to a higher computational burden for point cloud data processing, posing challenges to real-time navigation. To address these issues, this paper proposes a 3D point cloud optimization method based on the octree data structure for autonomous navigation of orchard mobile robots.
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Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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View Article and Find Full Text PDFSmall
January 2025
Department of Nano-scale Semiconductor Engineering, Hanyang University, Seoul, 04763, Republic of Korea.
Spiking neurons are essential for building energy-efficient biomimetic spatiotemporal systems because they communicate with other neurons using sparse and binary signals. However, the achievable high density of artificial neurons having a capacitor for emulating the integrate function of biological neurons has a limit. Furthermore, a low-voltage operation (<1.
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January 2025
School of Urban Planning and Municipal Engineering, Xi'an Polytechnic University, Xi'an, 710048, China.
With the increase in coal mining depths, soft and fractured roadway surrounding rocks require grouting and a sprayed protective layer for maintenance. Simultaneously, extensive accumulation of coal gangue causes diverse environmental issues. To enhance on-site coal gangue utilization, this study replaced river sand and cement with coal gangue to develop a novel cement-based mortar for supporting coal mine roadways.
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January 2025
School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity.
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