A greedy heuristic using adjoint functions for the optimization of seed and needle configurations in prostate seed implant

A radiobiology-based inverse treatment planning method for optimisation of permanent l-125 prostate implants in focal brachytherapy

Treatment plans for ten patients, initially treated with a conventional approach to low dose-rate brachytherapy (LDR, 145 Gy to entire prostate), were compared with plans for the same patients created with an inverse-optimisation planning process utilising a biologically-based objective. The 'biological optimisation' considered a non-uniform distribution of tumour cell density through the prostate based on known and expected locations of the tumour. Using dose planning-objectives derived from our previous biological-model validation study, the volume of the urethra receiving 125% of the conventional prescription (145 Gy) was reduced from a median value of 64% to less than 8% whilst maintaining high values of TCP. On average, the number of planned seeds was reduced from 85 to less than 75. The robustness of plans to random seed displacements needs to be carefully considered when using contemporary seed placement techniques. We conclude that an inverse planning approach to LDR treatments, based on a biological objective, has the potential to maintain high rates of tumour control whilst minimising dose to healthy tissue. In future, the radiobiological model will be informed using multi-parametric MRI to provide a personalised medicine approach.

An open-source genetic algorithm for determining optimal seed distributions for low-dose-rate prostate brachytherapy

PURPOSE

An open source optimizer that generates seed distributions for low-dose-rate prostate brachytherapy was designed, tested, and validated.

METHODS

The optimizer was a simple genetic algorithm (SGA) that, given a set of prostate and urethra contours, determines the optimal seed distribution in terms of coverage of the prostate with the prescribed dose while avoiding hotspots within the urethra. The algorithm was validated in a retrospective study on 45 previously contoured low-dose-rate prostate brachytherapy patients. Dosimetric indices were evaluated to ensure solutions adhered to clinical standards. The SGA performance was further benchmarked by comparing solutions obtained from a commercial optimizer (inverse planning simulated annealing [IPSA]) with the same cohort of 45 patients.

RESULTS

Clinically acceptable target coverage by the prescribed dose (V100) was obtained for both SGA and IPSA, with a mean ± standard deviation of 98 ± 2% and 99.5 ± 0.5%, respectively. For the prostate D90, SGA and IPSA yielded 177 ± 8 Gy and 186 ± 7 Gy, respectively, which were both clinically acceptable. Both algorithms yielded reasonable dose to the rectum, with V100 < 0.3 cc. A reduction in dose to the urethra was seen using SGA. SGA solutions showed a slight prostate volume dependence, with smaller prostates (<25 cc) yielding less desirable, although still clinically viable, dosimetric outcomes. SGA plans used, on average, fewer needles than IPSA (21 vs. 24, respectively), which may lead to a reduction in urinary toxicity and edema that alters post-implant dosimetry.

CONCLUSIONS

An open source SGA was validated that provides a research tool for the brachytherapy community.

A novel greedy heuristic-based approach to intraoperative planning for permanent prostate brachytherapy

This paper presents a greedy heuristic‐based double iteration and rectification (DIR) approach to intraoperative planning for permanent prostate brachytherapy. The DIR approach adopts a greedy seed selection (GSS) procedure to obtain a preliminary plan. In this process, the potential seeds are evaluated according to their ability to irradiate target while spare organs at risk (OARs), and their impact on dosimetric homogeneity within target volume. A flexible termination condition is developed for the GSS procedure, which guarantees sufficient dose within target volume while avoids overdosing the OARs. The preliminary treatment plan generated by the GSS procedure is further refined by the double iteration (DI) and rectification procedure. The DI procedure removes the needles containing only one seed (single seed) and implements the GSS procedure again to get a temporary plan. The DI procedure terminates until the needles number of the temporary plan does not decrease. This process is guided by constantly removing undesired part rather than imposing extra constrains. Following the DI procedure, the rectification procedure attempts to replace the remaining single seeds with the acceptable ones within the existing needles. The change of dosimetric distribution (DD) after the replacement is evaluated to determine whether to accept or to withdraw the replacement. Experimental results demonstrate that the treatment plans obtained by the DIR approach caters to all clinical considerations. Compared with currently available methods, DIR approach is faster, more reliable, and more suitable for intraoperative treatment planning in the operation room.

PACS number: 87

Fast patient-specific Monte Carlo brachytherapy dose calculations via the correlated sampling variance reduction technique

PURPOSE

To demonstrate potential of correlated sampling Monte Carlo (CMC) simulation to improve the calculation efficiency for permanent seed brachytherapy (PSB) implants without loss of accuracy.

METHODS

CMC was implemented within an in-house MC code family (PTRAN) and used to compute 3D dose distributions for two patient cases: a clinical PSB postimplant prostate CT imaging study and a simulated post lumpectomy breast PSB implant planned on a screening dedicated breast cone-beam CT patient exam. CMC tallies the dose difference, ΔD, between highly correlated histories in homogeneous and heterogeneous geometries. The heterogeneous geometry histories were derived from photon collisions sampled in a geometrically identical but purely homogeneous medium geometry, by altering their particle weights to correct for bias. The prostate case consisted of 78 Model-6711 (125)I seeds. The breast case consisted of 87 Model-200 (103)Pd seeds embedded around a simulated lumpectomy cavity. Systematic and random errors in CMC were unfolded using low-uncertainty uncorrelated MC (UMC) as the benchmark. CMC efficiency gains, relative to UMC, were computed for all voxels, and the mean was classified in regions that received minimum doses greater than 20%, 50%, and 90% of D(90), as well as for various anatomical regions.

RESULTS

Systematic errors in CMC relative to UMC were less than 0.6% for 99% of the voxels and 0.04% for 100% of the voxels for the prostate and breast cases, respectively. For a 1 × 1 × 1 mm(3) dose grid, efficiency gains were realized in all structures with 38.1- and 59.8-fold average gains within the prostate and breast clinical target volumes (CTVs), respectively. Greater than 99% of the voxels within the prostate and breast CTVs experienced an efficiency gain. Additionally, it was shown that efficiency losses were confined to low dose regions while the largest gains were located where little difference exists between the homogeneous and heterogeneous doses. On an AMD 1090T processor, computing times of 38 and 21 sec were required to achieve an average statistical uncertainty of 2% within the prostate (1 × 1 × 1 mm(3)) and breast (0.67 × 0.67 × 0.8 mm(3)) CTVs, respectively.

CONCLUSIONS

CMC supports an additional average 38-60 fold improvement in average efficiency relative to conventional uncorrelated MC techniques, although some voxels experience no gain or even efficiency losses. However, for the two investigated case studies, the maximum variance within clinically significant structures was always reduced (on average by a factor of 6) in the therapeutic dose range generally. CMC takes only seconds to produce an accurate, high-resolution, low-uncertainly dose distribution for the low-energy PSB implants investigated in this study.