자료유형 | 학위논문 |
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서명/저자사항 | Fully Automated Radiation Therapy Treatment Planning through Knowledge-based Dose Predictions. |
개인저자 | Landers, Angelia C. |
단체저자명 | University of California, Los Angeles. Biomedical Physics 0119. |
발행사항 | [S.l.]: University of California, Los Angeles., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 143 p. |
기본자료 저록 | Dissertation Abstracts International 79-10B(E). Dissertation Abstract International |
ISBN | 9780438030428 |
학위논문주기 | Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Adviser: Ke Sheng. |
요약 | Intensity-modulated radiotherapy treatment planning is an inverse problem that typically includes numerous parameters that have to be manually tuned by expert planners. This process can take hours or even days and can often lead to suboptimal pl |
요약 | Knowledge-based planning (KBP) dose prediction provides patient-specific estimations for the capabilities and limitations of a plan. Statistical voxel dose learning (SVDL) was developed to predict the voxel dose of new patients. The method was c |
요약 | To remove any dependence on hyperparameters that require manual tuning, voxel-based non-coplanar 4pi radiotherapy and coplanar volumetric modulated arc therapy (VMAT) optimization problems were modified to include the KBP predicted doses. The ne |
요약 | In the case of no existing high quality training set, evolving-knowledge-base (EKB) planning was developed. An initial, low quality training set was used for the first epoch of automated planning. In subsequent epochs, the superior plans from th |
요약 | Through the course of this work, we established a robust and accurate KBP dose prediction technique, which we then utilized in our automated planning protocol. Both the use of high quality training sets and EKB planning created high quality plan |
일반주제명 | Biomedical engineering. |
언어 | 영어 |
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