Intraoperative factors associated with stranded source placement accuracy in low-dose-rate prostate brachytherapy

Flexible Needle Bending Model for Spinal Injection Procedures

An in situ needle manipulation technique used by physicians when performing spinal injections is modeled to study its effect on needle shape and needle tip position. A mechanics-based model is proposed and solved using finite element method. A test setup is presented to mimic the needle manipulation motion. Tissue phantoms made from plastisol as well as porcine skeletal muscle samples are used to evaluate the model accuracy against medical images. The effect of different compression models as well as model parameters on model accuracy is studied, and the effect of needle-tissue interaction on the needle remote center of motion is examined. With the correct combination of compression model and model parameters, the model simulation is able to predict needle tip position within submillimeter accuracy.

Post-implant analysis in permanent breast seed implant: automated plan reconstruction using simulated annealing

Purpose

Post-implant analysis in permanent breast seed implant (PBSI) brachytherapy is an important component of the quality assurance process that indicates dosimetric quality relevant to patient outcome, indicating salvage therapy if inadequate, as well as providing feedback to the brachytherapy team to improve future treatments. To measure geometric indices on implant quality, plan reconstruction must be performed to correlate each planned and post-implant seed location. In this work, a simulated-annealing-based algorithm is developed to perform this plan reconstruction automatically.

Material and methods

The plan reconstruction algorithm was developed in MATLAB, taking the patient pre-treatment and post-implant (Day 0) plan and associated contours as inputs. For 19 treated patients, a reconstruction was obtained that defined the correspondence between each planned and post-implant seed. The simulated-annealing algorithm was used to reconstruct each patient 10 times to assess the variability in convergence. Manual reconstructions performed by at least two independent observers to obtain consensus were defined as the ground truth; these were compared to the automatic reconstructions obtained by the algorithm. Metrics on seed placement accuracy and needle strand angulation were calculated for the patients.

Results

The algorithm performed reconstructions on 19 patients (1235 seeds) with ground-truth reconstructions, obtaining 97 ± 8% correct matches. This strong performance indicates the ability to incorporate this algorithm into the clinical quality assurance workflow.

Conclusions

The plan reconstruction algorithm developed herein performed very well in a 19-patient cohort. This algorithm can be incorporated into the clinical process to assist in the assessment of center-specific seed placement accuracy and can be used to gather implant metrics in an automated, standardized fashion for future PBSI trials.