Objective. In brachytherapy for gynecological cancers using intracavitary applicators, implant reconstruction is commonly performed using applicator libraries. These libraries contain applicator geometry models as well as dwell position (DP) models defined in respect to the applicator geometry. In this study, we investigate whether an afterloader integrated electromagnetic tracking (EMT) system can be utilized for DP model definition and quality assurance in such applicators. Approach. DPs in four sets of two configurations of the Elekta Venezia Advanced Gynaecological Applicator (22 mm ovoids/40 mm intrauterine (IU) and 26 mm ovoids/70 mm IU) were measured using an afterloader integrated EMT system. Measurements were evaluated for reproducibility and compared against manufacturer-specified (MS) DPs and a computed tomography (CT)-corrected DP model. Main Results. Excellent EMT measurement reproducibility was observed, with values of ⩽0.2 mm for both configurations. The overall reproducibility, including applicator geometry reproducibility, was ⩽0.4 mm for both configurations. Significant discrepancies from the manufacturer’s DP model were observed, with a mean ± sd deviation of 1.13 ± 0.66 mm (22/40) and 1.37 ± 0.63 (26/70), particularly in the IU channel, where MS DPs were not experimentally defined. Discrepancies were reduced to 0.89 ± 0.41 mm (22/40) and 0.81 ± 0.33 mm (26/70) when the CT-corrected DP model was used as baseline, highlighting the need for experimentally defined DP models. The overall uncertainty of single measurements was below the clinically acceptable 2 mm limit. Significance. This study confirms that afterloader integrated EMT can accurately reconstruct source paths in gynecological brachytherapy applicators and supports its incorporation into clinical workflows for improved quality assurance and treatment precision. The importance of EMT for quality assurance was highlighted by measured deviations from manufacturer’s DP model in a clinical relevant part of the IU channel.

Background and purpose
Hyperthermia, the elevation of target temperature to 39–44 °C, is monitored using temperature probes. However, these provide limited spatial information, sampling only a few discrete locations. Magnetic resonance (MR) thermometry currently offers an option for three-dimensional (3D) temperature monitoring during hyperthermia. This study compares and correlates temperatures measured by intraluminal probes with MR-based temperatures in (1) the anatomical region containing the intraluminal probes and (2) the hyperthermia target volume (HTV), located at a distance from the probes and representing the primary region of clinical interest.

Methods
Thirteen locally advanced cervical cancer (LACC) patients treated with radiotherapy and hyperthermia were included. Hyperthermia was monitored using intraluminal probes and MR thermometry. MR-based temperatures were compared to intraluminal probe temperatures. Repeated measures correlation was applied to correlate probe and MR-based temperatures in the HTV across all data and on a patient-specific basis.

Results
MR-based temperatures at probe locations showed good agreement with probe measurements (median absolute error ≤ 0.7 °C). In the HTV, MR-based temperatures deviated by a median absolute error of 0.5 °C from probe temperatures. Repeated measures correlations (rrm) between MR and probe-based HTV temperatures ranged from 0.74 to 0.79 across all data and 0.64–0.96 on a patient-specific basis.

Conclusions
MR thermometry demonstrated promising performance for retrospective evaluation of temperature distributions in the HTV. While its current reliability for real-time treatment guidance remains limited, our results support further development towards broader clinical implementation in hyperthermia.

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of computed tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = 0e0) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to magnetic resonance imaging scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.

Purpose: Bi-objective treatment planning for high-dose-rate prostate brachytherapy is a novel treatment planning method with two separate objectives that represent target coverage and organ-at-risk sparing. In this study, we investigated the feasibility and plan quality of this method by means of a retrospective observer study. Methods and Materials: Current planning sessions were recorded to configure a bi-objective optimization model and to assess its applicability to our clinical practice. Optimization software, GOMEA, was then used to automatically generate a large set of plans with different trade-offs in the two objectives for each of 18 patients treated with high-dose-rate prostate brachytherapy. From this set, five plans per patient were selected for comparison to the clinical plan in terms of satisfaction of planning criteria and in a retrospective observer study. Three brachytherapists were asked to evaluate the blinded plans and select the preferred one. Results: Recordings demonstrated applicability of the bi-objective optimization model to our clinical practice. For 14/18 patients, GOMEA plans satisfied all planning criteria, compared with 4/18 clinical plans. In the observer study, in 53/54 cases, a GOMEA plan was preferred over the clinical plan. When asked for consensus among observers, this ratio was 17/18 patients. Observers highly appreciated the insight gained from comparing multiple plans with different trade-offs simultaneously. Conclusions: The bi-objective optimization model adapted well to our clinical practice. GOMEA plans were considered equal or superior to the clinical plans. In addition, presenting multiple high-quality plans provided novel insight into patient-specific trade-offs.