亞洲知識產權資訊網為知識產權業界提供一個一站式網上交易平台，協助業界發掘知識產權貿易商機，並與環球知識產權業界建立聯繫。無論你是知識產權擁有者正在出售您的知識產權，或是製造商需要購買技術以提高操作效能，又或是知識產權配套服務供應商，你將會從本網站發掘到有用的知識產權貿易資訊。

返回搜索結果

Method To Predict Three-Dimensional Radiotherapy Dose Distribution

技術應用: This technology opens up a number of new possible applications in treatment planning optimization and plan evaluation. Some of these include: · Immediately after contour approval, radiation oncologists would have an expected treatment plan dose distribution to review, both guiding and accelerating the clinical decision making process for fractionation, target/organ prioritization, etc. · Treatment planners would have a powerful new diagnostic tool for understanding exactly where sub-optimal plans are failing, increasing efficiency and further eliminating human failure from the planning process. · New voxel-based optimization methods could be designed around these predictions, eliminating the need to convert DVH predictions to DVH-based optimization objectives and relative priorities. It is possible that this technology will herald yet another huge leap forward in clinical treatment planning, and in time the history of treatmentplanning advances could seen as: 2D àà 3D-conformal àà IMRT/RapidArc àà 1D-KBP àà 3D-KBP.

詳細技術說明: At UC San Diego, we have developed a major advance in knowledge-based treatment planning: full 3D dose distribution prediction. Similar to existing art1 our method relies on learning from a plurality of previously treated plans, but our method moves far beyond current technology to synthesize past experience into voxel-by-voxel predictions for expected dose distributions for new patients. Our current embodiment already meets or exceeds the accuracy of existing technology in DVH prediction, with the invaluable addition of identifying (a) precisely where the dose is expected to be deposited in the patient and (b) the degree of confidence in that voxel-by-voxel prediction. 1 See in particular US20120310615 (on which Dr. Kevin Moore was the lead inventor) and US20120014507.

*Abstract: A painting is not the amount of blue, yellow, and red paint on a canvas; it is their arrangement that makes it art.
The advent of knowledge-based planning (KBP) represents a critical step forward in clinical radiotherapy, comfortably mentioned in the company of the other major advances in treatment planning (2D àà 3D-conformal àà IMRT/RapidArc àà KBP). While incredibly powerful, current knowledge-based methods result in dose-volume histogram (DVH) predictions that are ultimately limited by the inherent loss in spatial information of a DVH. In current incarnations of KBP (e.g. Varian’s RapidPlanTM), these predictions must be converted into DVH-based optimization parameters to enact automated planning, and when treatment plan DVHs differ from their knowledge-based DVH predictions it requires significant expertise to discern the origin of the deviation. Troublingly, the investigation and resolution of these discrepancies necessarily falls into the hands of the same human treatment planners that knowledge-based planning purports to outperform.

*Principal Investigation: Name: Kevin Moore
Department:

Name: Satomi Shiraishi
Department:

欲了解更多信息，請點擊這裡