| LDR | | 02667nam u200421 4500 |
| 001 | | 000000420128 |
| 005 | | 20190215164228 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438030428 |
| 035 | |
▼a (MiAaPQ)AAI10826617 |
| 035 | |
▼a (MiAaPQ)ucla:16854 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 610 |
| 100 | 1 |
▼a Landers, Angelia C. |
| 245 | 10 |
▼a Fully Automated Radiation Therapy Treatment Planning through Knowledge-based Dose Predictions. |
| 260 | |
▼a [S.l.]:
▼b University of California, Los Angeles.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 143 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Ke Sheng. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Los Angeles, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0031. |
| 650 | 4 |
▼a Biomedical engineering. |
| 690 | |
▼a 0541 |
| 710 | 20 |
▼a University of California, Los Angeles.
▼b Biomedical Physics 0119. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0031 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998911
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |