A new optimization method using a compressed sensing inspired solver for real-time LDR-brachytherapy treatment planning

Abstract

This work discusses a novel strategy for inverse planning in low dose rate brachytherapy. It applies the idea of compressed sensing to the problem of inverse treatment planning and a new solver for this formulation is developed. An inverse planning algorithm was developed incorporating brachytherapy dose calculation methods as recommended by AAPM TG-43. For optimization of the functional a new variant of a matching pursuit type solver is presented. The results are compared with current state-of-the-art inverse treatment planning algorithms by means of real prostate cancer patient data. The novel strategy outperforms the best state-of-the-art methods in speed, while achieving comparable quality. It is able to find solutions with comparable values for the objective function and it achieves these results within a few microseconds, being up to 542 times faster than competing state-of-the-art strategies, allowing real-time treatment planning. The sparse solution of inverse brachytherapy planning achieved with methods from compressed sensing is a new paradigm for optimization in medical physics. Through the sparsity of required needles and seeds identified by this method, the cost of intervention may be reduced.

A fast multitarget inverse treatment planning strategy optimizing dosimetric measures for high‐dose‐rate (HDR) brachytherapy

“…Based on our empirical stability test and the comparison with the expected upper bound retrieved using the relaxed version of the MILP, we conclude that the retrieved solutions are near the global optimum (< 10%). This deviation is acceptable and also in the range of what we have observed for other ITP approaches for brachytherapy . Further we conclude that an initial solution with all dwell times set to zero is a valid initial guess that yields a high plan quality.…”

“…This deviation is acceptable and also in the range of what we have observed for other ITP approaches for brachytherapy. 17,27 Further we conclude that an initial solution with all dwell times set to zero is a valid initial guess that yields a high plan quality. An extension of the approach with multiple starting points to further increase the quality of the optimization is not necessary and may even lead a deterioration in quality.…”

Determining optimal eluter design by modeling physical dose enhancement in brachytherapy

“…The final model consists of different functions M ν / σ (·) that individually map the achieved dose distribution to the quality measure for the targets ( ν ) and for the surrounding organs at risk (OARs) (σ). A frequently used model is the dose‐based objective function, or more recently a mathematical model solely based on dose volume histogram (DVH)‐related dosimetric measures (DMs) .…”

“…The final model consists of different functions M m/r (Á) that individually map the achieved dose distribution to the quality measure for the targets (m) and for the surrounding organs at risk (OARs) (r). A frequently used model is the dose-based objective function, 25 or more recently a mathematical model solely based on dose volume histogram (DVH)-related dosimetric measures (DMs). 26,27 Additional models that are related to the EUD or to biological outcome such as tumor control probability and normal tissue complication probabilities are in use.…”

Mathematical Modelling of Dose Planning in High Dose-Rate Brachytherapy

“…When an integer solution is found, a local search is initiated to improve the TCP value. Moreover, in an LDR BT study [42], estimates of TCP and NTCP are included. Because the OAR have different radiobiological sensibility, small changes in the DVH curve for the rectum had a big impact on the NTCP, while a large change in the DVH curve for the urethra gave only a small change in the NTCP.…”