Introduction:
The Pinnacle3 Auto-Planning (AP) package is an automated inverse planning tool employing a multi-sequence optimisation algorithm. The nature of the optimisation aims to improve the overall quality of radiotherapy plans but at the same time may produce higher modulation, increasing plan complexity and challenging linear accelerator delivery capability.
Methods and materials:
Thirty patients previously treated with intensity-modulated radiotherapy (IMRT) to the prostate with or without pelvic lymph node irradiation were replanned with locally developed AP techniques for step-and-shoot IMRT (AP-IMRT) and volumetric-modulated arc therapy (AP-VMAT). Each case was also planned with VMAT using conventional inverse planning. The patient cohort was separated into two groups, those with a single primary target volume (PTV) and those with dual PTVs of differing prescription dose levels. Plan complexity was assessed using the modulation complexity score.
Results:
Plans produced with AP provided equivalent or better dose coverage to target volumes whilst effectively reducing organ at risk (OAR) doses. For IMRT plans, the use of AP resulted in a mean reduction in bladder V50Gy by 4·2 and 4·7 % (p ≤ 0·01) and V40Gy by 4·8 and 11·3 % (p < 0·01) in the single and dual dose level cohorts, respectively. For the rectum, V70Gy, V60Gy and V40Gy were all reduced in the dual dose level AP-VMAT plans by an average of 2·0, 2·7 and 7·3 % (p < 0·01), respectively. A small increase in plan complexity was observed only in dual dose level AP plans.
Findings:
The automated nature of AP led to high quality treatment plans with improvement in OAR sparing and minimised the variation in achievable dose planning metrics when compared to the conventional inverse planning approach.
Over the past seven years, Smiths and BAE SYSTEMS have launched collaborative work to evolve a certifiable practical Structural Prognostic Health Management (SPHM) system. The collaborative work has built on BAE SYSTEMS' vast advanced technology experience and on Smiths' unique experience that has produced intelligent Fleet and Usage Management Software (FUMS TM ) including fusion, prognostic and decision support algorithms combining model-based and Artificial Intelligence (AI) techniques. This paper describes the recent advances and optimisation of the Smiths algorithms for damage detection and Operational Load Monitoring (OLM). A combination of FUMS TM signal processing and AI techniques have been applied to acoustic emission sensor data to locate and classify damage of different types in composite and metallic structures. The FUMS TM damage detection software has been embedded in real-time hardware to support ground tests. Techniques have been implemented to enable adequate calibration of OLM algorithms using data from flight tests. The techniques should address concerns raised about the accuracy of algorithms trained to synthesise strains throughout the entire flight envelope from data recorded close to the edge of the flight envelope. Working with the UK MOD, Smiths has continued the evaluation of FUMS TM software that allows aircraft design authorities and military operators to build their force life management applications without the need for software rewriting. 1,2
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