Jouko Tervo

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The paper considers the intensity modulated radiation therapy (inverse) treatment planning. An approach to determine the trajectories of the leaves of the multileaf collimator (MLC) in order to produce the prescribed intensity distribution is developed. The paper concentrates on the multiple static delivery technique. A mathematical model for calculating(More)
A method for the stabilization of stationary and time-varying autoregressive models is presented. The method is based on the hyperstability constrained LS-problem with nonlinear constraints. The problems are solved iteratively with Gauss-Newton type algorithm that sequentially linearizes the constraints. The proposed method is applied to simulated data in(More)
The stability of autoregressive (AR) models is an important issue in many applications such as spectral estimation, simulation and decoding of linear prediction coded (LPC) signals. There are methods for AR parameter estimation that guarantee the stability of the model, that is, all roots of the characteristic polynomial of the model have moduli less than(More)
Stability of time-varying autoregressive (TVAR) models is an important issue in many applications such as time-varying spectral estimation, simulation and analysis of EEG and time-varying linear prediction coding (TVLPC). For stationary AR-models there are methods that guarantee stability, but the for non-adaptive time-varying approaches there are no such(More)
A new approach for the inverse treatment planning in radiation therapy with the multileaf colli-mator (MLC) technique is presented. The application of the MLC-techniques requires an algorithm for the computation of the positions or velocities of leaves as a function of time such that the prescribed dose in the patient space is obtained. First the intensity(More)
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