Int J Comput Assist Radiol Surg - Controlling motion prediction errors in radiotherapy with relevance vector machines.

Tópicos

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Resumo

RPOSE: Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms.METHODS: First, the duty cycle and the precision were correlated to the variance by interrupting the treatment if the variance exceeds a threshold. Second, two hybrid algorithms based on the variance were developed, one consisting of multiple RVMs (HYB(RVM)) and the other of a combination between a wavelet-based least mean square algorithm (wLMS) and a RVM (HYB(wLMS-RVM)). The variance for different motion traces was analyzed to reveal a characteristic variance pattern which gives insight in what kind of prediction errors can be controlled by the variance.RESULTS: Limiting the variance by a threshold resulted in an increased precision with a decreased duty cycle. All hybrid algorithms showed an increased prediction accuracy compared to using only their individual algorithms. The best hybrid algorithm, HYB(RVM), can decrease the mean RMSE over all 304 motion traces from 0.18 mm for a linear RVM to 0.17 mm.CONCLUSIONS: The predicted variance was shown to be an efficient metric to control prediction errors, resulting in a more robust radiotherapy treatment. The hybrid algorithm HYB(RVM) could be translated to clinical practice. It does not require further parameters, can be completely parallelised and easily further extended.

Resumo Limpo

rpose robot radiotherapi can precis ablat move tumor time latenc compens recent relev vector machin rvm probabilist regress techniqu outperform six predict algorithm respiratori compens method distinct advantag predict point assum drawn normal distribut secondord statist predict varianc use control rvm predict error treatment construct hybrid predict algorithmsmethod first duti cycl precis correl varianc interrupt treatment varianc exceed threshold second two hybrid algorithm base varianc develop one consist multipl rvms hybrvm combin waveletbas least mean squar algorithm wlms rvm hybwlmsrvm varianc differ motion trace analyz reveal characterist varianc pattern give insight kind predict error can control varianceresult limit varianc threshold result increas precis decreas duti cycl hybrid algorithm show increas predict accuraci compar use individu algorithm best hybrid algorithm hybrvm can decreas mean rmse motion trace mm linear rvm mmconclus predict varianc shown effici metric control predict error result robust radiotherapi treatment hybrid algorithm hybrvm translat clinic practic requir paramet can complet parallelis easili extend

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