Localization of reference points in electromagnetic tracking data and their application for treatment error detection in interstitial breast brachytherapy

Abstract

AbstractBackgroundElectromagnetic tracking (EMT) is a promising technology that holds great potential to advance patient‐specific pre‐treatment verification in interstitial brachytherapy (iBT). It allows easy determination of the implant geometry without line‐of‐sight restrictions and without dose exposure to the patient. What it cannot provide, however, is a link to anatomical landmarks, such as the exit points of catheters or needles on the skin surface. These landmarks are required for the registration of EMT data with other imaging modalities and for the detection of treatment errors such as incorrect indexer lengths, and catheter or needle shifts.PurposeTo develop an easily applicable method to detect reference points in the positional data of the trajectory of an EMT sensor, specifically the exit points of catheters in breast iBT, and to apply the approach to pre‐treatment error detection.MethodsSmall metal objects were attached to catheter fixation buttons that rest against the breast surface to intentionally induce a local, spatially limited perturbation of the magnetic field on which the working principle of EMT relies. This perturbation can be sensed by the EMT sensor as it passes by, allowing it to localize the metal object and thus the catheter exit point. For the proof‐of‐concept, different small metal objects (magnets, washers, and bushes) and EMT sensor drive speeds were used to find the optimal parameters. The approach was then applied to treatment error detection and validated in‐vitro on a phantom. Lastly, the in‐vivo feasibility of the approach was tested on a patient cohort of four patients to assess the impact on the clinical workflow.ResultsAll investigated metal objects were able to measurably perturb the magnetic field, which resulted in missing sensor readings, that is two data gaps, one for the sensor moving towards the tip end and one when retracting from there. The size of the resulting data gaps varied depending on the choice of gap points used for calculation of the gap size; it was found that the start points of the gaps in both directions showed the smallest variability. The median size of data gaps was ⩽8 mm for all tested materials and sensor drive speeds. The variability of the determined object position was ⩽0.5 mm at a speed of 1.0 cm/s and ⩽0.7 mm at 2.5 cm/s, with an increase up to 2.3 mm at 5.0 cm/s. The in‐vitro validation of the error detection yielded a 100% detection rate for catheter shifts of ≥2.2 mm. All simulated wrong indexer lengths were correctly identified. The in‐vivo feasibility assessment showed that the metal objects did not interfere with the routine clinical workflow.ConclusionsThe developed approach was able to successfully detect reference points in EMT data, which can be used for registration to other imaging modalities, but also for treatment error detection. It can thus advance the automation of patient‐specific, pre‐treatment quality assurance in iBT.