跟踪(教育)
门控
核医学
医学
通风(建筑)
放射治疗计划
体积热力学
肺
计算机科学
放射治疗
放射科
物理
内科学
热力学
量子力学
生理学
教育学
心理学
作者
Stefanie Ehrbar,Alexander Jöhl,Adrianna Tartas,L. S. Stark,Oliver Riesterer,Stephan Klöck,Matthias Gückenberger,Stephanie Tanadini‐Lang
标识
DOI:10.1016/j.radonc.2017.05.016
摘要
Respiratory motion-management techniques (MMT) aim to ensure tumor dose coverage while sparing lung tissue. Dynamic treatment-couch tracking of the moving tumor is a promising new MMT and was compared to the internal-target-volume (ITV) concept, the mid-ventilation (MidV) principle and the gating approach in a planning study based on 4D dose calculations.For twenty patients with lung lesions, planning target volumes (PTV) were adapted to the MMT and stereotactic body radiotherapy treatments were prepared with the 65%-isodose enclosing the PTV. For tracking, three concepts for target volume definition were considered: Including the gross tumor volume of one phase (single-phase tracking), including deformations between phases (multi-phase tracking) and additionally including tracking latencies of a couch tracking system (reliable couch tracking). The accumulated tumor and lung doses were estimated with 4D dose calculations based on 4D-CT datasets and deformable image registration.Single-phase tracking showed the lowest ipsilateral lung Dmean (median: 3.3Gy), followed by multi-phase tracking, gating, reliable couch tracking, MidV and ITV concepts (3.6, 3.8, 4.1, 4.3 and 4.8Gy). The 4D dose calculations showed the MidV and single-phase tracking overestimated the target mean dose (-2.3% and -1.3%), while it was slightly underestimated by the other MMT (<+1%).The ITV concept ensures tumor coverage, but exposes the lung tissue to a higher dose. The MidV, gating and tracking concepts were shown to reduce the lung dose. Neglecting non-translational changes of the tumor in the target volume definition for tracking results in a slightly reduced target coverage. The slightly inferior dose coverage for MidV should be considered when applying this technique clinically.
科研通智能强力驱动
Strongly Powered by AbleSci AI