Brachytherapy can deliver high doses to the target while sparing healthy tissues due to its steep dose gradient leading to excellent clinical outcome. Treatment accuracy depends on several manual steps making brachytherapy susceptible to operational mistakes. Currently, treatment delivery verification is not routinely available and has led, in some cases, to systematic errors going unnoticed for years. The brachytherapy community promoted developments in in vivo dosimetry (IVD) through research groups and small companies. Although very few of the systems have been used clinically, it was demonstrated that the likelihood of detecting deviations from the treatment plan increases significantly with time-resolved methods. Time-resolved methods could interrupt a treatment avoiding gross errors which is not possible with time-integrated dosimetry. In addition, lower experimental uncertainties can be achieved by using source-tracking instead of direct dose measurements. However, the detector position in relation to the patient anatomy remains a main source of uncertainty. The next steps towards clinical implementation will require clinical trials and systematic reporting of errors and nearmisses. It is of utmost importance for each IVD system that its sensitivity to different types of errors is well understood, so that end-users can select the most suitable method for their needs. This report aims to formulate requirements for the stakeholders (clinics, vendors, and researchers) to facilitate increased clinical use of IVD in brachytherapy. The report focuses on high dose-rate IVD in brachytherapy providing an overview and outlining the need for further development and research. ☆ During the 1st ESTRO Physics Workshop celebrated in November 2017 in Glasgow, Scotland, a task group was created to stimulate the wider adoption of in vivo dosimetry for radiotherapy. The members of this task group, authors of this report, were selected on the basis of their expertise to contribute relevant input to the area of study and their long-term experience in the clinical implementation of in vivo dosimetry systems.

A hypothetical, generic HDR (192)Ir source was designed and implemented in two commercially available TPSs employing different MBDCAs. Reference dose distributions for this source were benchmarked and used for the evaluation of MBDCA calculations employing a virtual, cubic water phantom in the form of a CT DICOM image series. The implementation of a generic source of identical design in all TPSs using MBDCAs is an important step toward supporting univocal commissioning procedures and direct comparisons between TPSs.

The combination of the generic source and generic shielded applicator, together with the previously developed test cases and reference datasets (available in the Brachytherapy Source Registry), lay a solid foundation in supporting uniform commissioning procedures and direct comparisons among treatment planning systems for HDR Ir brachytherapy.

Brachytherapy is employed to treat a wide variety of cancers. However, an accurate treatment verification method is currently not available. This study describes a pre-treatment verification system that uses an imaging panel (IP) to verify important aspects of the treatment plan. A detailed modelling of the IP was only possible with an extensive calibration performed using a robotic arm. Irradiations were performed with a high dose rate (HDR) Ir source within a water phantom. An empirical fit was applied to measure the distance between the source and the detector so 3D Cartesian coordinates of the dwell positions can be obtained using a single panel. The IP acquires 7.14 fps to verify the dwell times, dwell positions and air kerma strength (Sk). A gynecological applicator was used to create a treatment plan that was registered with a CT image of the water phantom used during the experiments for verification purposes. Errors (shifts, exchanged connections and wrong dwell times) were simulated to verify the proposed verification system. Cartesian source positions (panel measurement plane) have a standard deviation of about 0.02 cm. The measured distance between the source and the panel (z-coordinate) have a standard deviation up to 0.16 cm and maximum absolute error of  ≈0.6 cm if the signal is close to sensitive limit of the panel. The average response of the panel is very linear with Sk. Therefore, Sk measurements can be performed with relatively small errors. The measured dwell times show a maximum error of 0.2 s which is consistent with the acquisition rate of the panel. All simulated errors were clearly identified by the proposed system. The use of IPs is not common in brachytherapy, however, it provides considerable advantages. It was demonstrated that the IP can accurately measure Sk, dwell times and dwell positions.

Purpose: Modern computed tomography (CT) scanners have an extended field-of-view (eFoV) for reconstructing images up to the bore size, which is relevant for patients with higher BMI or nonisocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data are used. This study introduces a new deep learningbased algorithm for extended field-of-view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy. Methods: A life-size three-dimensional (3D) printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state-of-the-art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning-based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by five physicians and five medical physicists. Results: The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of À18 HU for an inhaled lung insert while HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artifacts. Conclusion: To be able to reconstruct images outside the sFoV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning-based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning-based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.

TG-186-based calculations produce results that are different from TG-43 for the Axxent source. The differences depend strongly on the method of dose reporting. This study highlights the importance of backscatter to peak skin dose. Tissue heterogeneities, applicator, and patient geometries demonstrate the need for a more robust dose calculation method for low energy brachytherapy sources.

A novel system was developed to improve commissioning and quality assurance of brachytherapy applicators used in high dose rate (HDR). It employs an imaging panel to create reference images and to measure dwell times and dwell positions. As an example: two ring applicators of the same model were evaluated. An applicator was placed on the surface of an imaging panel and a HDR Ir source was positioned in an imaging channel above the panel to generate an image of the applicator, using the gamma photons of the brachytherapy source. The applicator projection image was overlaid with the images acquired by capturing the gamma photons emitted by the source dwelling inside the applicator. We verified 0.1, 0.2, 0.5 and 1.0 cm interdwell distances for different offsets, applicator inclinations and transfer tube curvatures. The data analysis was performed using in-house developed software capable of processing the data in real time, defining catheters and creating movies recording the irradiation procedure. One applicator showed up to 0.3 cm difference from the expected position for a specific dwell position. The problem appeared intermittently. The standard deviations of the remaining dwell positions (40 measurements) were less than 0.05 cm. The second ring applicator had a similar reproducibility with absolute coordinate differences from expected values ranging from  -0.10 up to 0.18 cm. The curvature of the transfer tube can lead to differences larger than 0.1 cm whilst the inclination of the applicator showed a negligible effect. The proposed method allows the verification of all steps of the irradiation, providing accurate information about dwell positions and dwell times. It allows the verification of small interdwell positions (⩽0.1 cm) and reduces measurement time. In addition, no additional radiation source is necessary since the HDRIr source is used to generate an image of the applicator.

Brachytherapy treatment planning systems that use model-based dose calculation algorithms employ a more accurate approach that replaces the TG43-U1 water dose formalism and adopt the TG-186 recommendations regarding composition and geometry of patients and other relevant effects. However, no recommendations were provided on the transit dose due to the source traveling inside the patient. This study describes a methodology to calculate the transit dose using information from the treatment planning system (TPS) and considering the source's instantaneous and average speed for two prostate and two gynecological cases. The trajectory of the (192)Ir HDR source was defined by importing applicator contour points and dwell positions from the TPS. The transit dose distribution was calculated using the maximum speed, the average speed and uniform accelerations obtained from the literature to obtain an approximate continuous source distribution simulated with a Monte Carlo code. The transit component can be negligible or significant depending on the speed profile adopted, which is not clearly reported in the literature. The significance of the transit dose can also be due to the treatment modality; in our study interstitial treatments exhibited the largest effects. Considering the worst case scenario the transit dose can reach 3% of the prescribed dose in a gynecological case with four catheters and up to 11.1% when comparing the average prostate dose for a case with 16 catheters. The transit dose component increases by increasing the number of catheters used for HDR brachytherapy, reducing the total dwell time per catheter or increasing the number of dwell positions with low dwell times. This contribution may become significant (>5%) if it is not corrected appropriately. The transit dose cannot be completely compensated using simple dwell time corrections since it may have a non-uniform distribution. An accurate measurement of the source acceleration and maximum speed should be incorporated in clinical practice or provided by the manufacturer to determine the transit dose component with high accuracy.