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Background and purpose Our aim was to evaluate the feasibility and potential advantages of dose guided patient positioning based on dose recalculation on scatter corrected cone beam computed tomography (CBCT) image data. Material and methods A scatter correction approach has been employed to enable dose calculations on CBCT images. A recently proposed tool for interactive multicriterial dose-guided patient positioning which uses interpolation between pre-calculated sample doses has been utilized. The workflow was retrospectively evaluated for two head and neck patients with a total of 39 CBCTs. Dose–volume histogram (DVH) parameters were compared to rigid image registration based isocenter corrections (clinical scenario). Results The accuracy of the dose interpolation was found sufficient, facilitating the implementation of dose guided patient positioning. Compared to the clinical scenario, the mean dose to the parotid glands could be improved for 2 out of 5 fractions for the first patient while other parameters were preserved. For the second patient, the mean coverage over all fractions of the high dose PTV could be improved by 4%. For this patient, coverage improvements had to be traded against organ at risk (OAR) doses within their clinical tolerance limits. Conclusions Dose guided patient positioning using in-room CBCT data is feasible and offers increased control over target coverage and doses to OARs. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to describe the experiments and methods that led to the geometrical interpretation of new-generation commercial PET-CT scanners, finalized to off-line PET-based treatment verification in ion beam therapy. Typically, the geometrical correspondence between the image domain (i.e., the dicom PET) and the sinogram domain (i.e., the PET raw data) is not explicitly described by scanner vendors. Hence, the proposed characterization can be applied to commercial PET-CT scanners used in biomedical research, for the development of technologies and methods requiring the use of PET raw data, without having access to confidential information from the vendors. [Copyright &y& Elsevier]

• Description of intra-fractional motion management workflow in MRI-guided radiotherapy. • Envisioned workflow with both highest dosimetric accuracy and duty cycle efficiency. • Focus on real-time implementation based on artificial intelligence. MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms. [ABSTRACT FROM AUTHOR]

• Comparison of three AI algorithms for real-time prediction of future tumor contours. • Usage of clinical cine MRI data from low-field MR-linacs from two institutions. • Prediction times compatible with reported MLC-tracking latencies on MR-linacs. Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tackle both rigid displacement and tumor deformation without prolonging treatment. However, the system latency must be accounted for by predicting future tumor contours in real-time. We compared the performance of three artificial intelligence (AI) algorithms based on long short-term memory (LSTM) modules for the prediction of 2D-contours 500 ms into the future. Models were trained (52 patients, 3.1 h of motion), validated (18 patients, 0.6 h) and tested (18 patients, 1.1 h) with cine MRs from patients treated at one institution. Additionally, we used three patients (2.9 h) treated at another institution as second testing set. We implemented 1) a classical LSTM network (LSTM-shift) predicting tumor centroid positions in superior-inferior and anterior-posterior direction which are used to shift the last observed tumor contour. The LSTM-shift model was optimized both in an offline and online fashion. We also implemented 2) a convolutional LSTM model (ConvLSTM) to directly predict future tumor contours and 3) a convolutional LSTM combined with spatial transformer layers (ConvLSTM-STL) to predict displacement fields used to warp the last tumor contour. The online LSTM-shift model was found to perform slightly better than the offline LSTM-shift and significantly better than the ConvLSTM and ConvLSTM-STL. It achieved a 50% Hausdorff distance of 1.2 mm and 1.0 mm for the two testing sets, respectively. Larger motion ranges were found to lead to more substantial performance differences across the models. LSTM networks predicting future centroids and shifting the last tumor contour are the most suitable for tumor contour prediction. The obtained accuracy would allow to reduce residual tracking errors during MRgRT with deformable MLC-tracking. [ABSTRACT FROM AUTHOR]

Background and purpose In clinical applications of Positron Emission Tomography (PET)-based treatment verification in ion beam therapy (PT-PET), detection and interpretation of inconsistencies between Measured PET and Expected PET are mostly limited by Measured PET noise, due to low count statistics, and by Expected PET bias, especially due to inaccurate washout modelling in off-line implementations. In this work, a recently proposed 4D Maximum Likelihood (ML) reconstruction algorithm which considers Measured PET and Expected PET as two different motion phases of a 4D dataset is assessed on clinical 4D PET-CT datasets acquired after carbon ion therapy. Material and methods The 4D ML reconstruction algorithm estimates: (1) Measured PET of enhanced image quality with respect to the conventional Measured PET, thanks to the exploitation of Expected PET; (2) the deformation field mapping the Expected PET onto the Measured PET as a measure of the occurred displacements. Results Results demonstrate the desired sensitivity to inconsistencies due to breathing motion and/or setup modification, robustness to noise in different count statistics scenarios, but a limited sensitivity to Expected PET washout inaccuracy. Conclusions The 4D ML reconstruction algorithm supports clinical 4D PT-PET in ion beam therapy. The limited sensitivity to washout inaccuracy can be detected and potentially overcome. [ABSTRACT FROM AUTHOR]

• Deep learning PET and CT-based DM and OS time-to-event models showed predictive capability. • PET-only achieved the best prognosis performance. • Unlike CT, GTV segmentation is not necessary for PET-based prognosis. Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC). However, lesion segmentation is typically required, resulting in a predictive power susceptible to variations in primary and lymph node gross tumor volume (GTV) segmentation. This study aimed at achieving prognosis without GTV segmentation, and extending single modality prognosis to joint PET/CT to allow investigating the predictive performance of combined- compared to single-modality inputs. We employed a 3D-Resnet combined with a time-to-event outcome model to incorporate censoring information. We focused on the prognosis of DM and OS for HNC patients. For each clinical endpoint, five models with PET and/or CT images as input were compared: PET-GTV, PET-only, CT-GTV, CT-only, and PET/CT-GTV models, where -GTV indicates that the corresponding images were masked using the GTV contour. Publicly available delineated CT and PET scans from 4 different Canadian hospitals (293) and the MAASTRO clinic (74) were used for training by 3-fold cross-validation (CV). For independent testing, we used 110 patients from a collaborating institution. The predictive performance was evaluated via Harrell's Concordance Index (HCI) and Kaplan-Meier curves. In a 5-year time-to-event analysis, all models could produce CV HCIs with median values around 0.8 for DM and 0.7 for OS. The best performance was obtained with the PET-only model, achieving a median testing HCI of 0.82 for DM and 0.69 for OS. Compared with the PET/CT-GTV model, the PET-only still had advantages of up to 0.07 in terms of testing HCI. The Kaplan-Meier curves and corresponding log-rank test results also demonstrated significant stratification capability of our models for the testing cohort. Deep learning-based DM and OS time-to-event models showed predictive capability and could provide indications for personalized RT. The best predictive performance achieved by the PET-only model suggested GTV segmentation might be less relevant for PET-based prognosis. [ABSTRACT FROM AUTHOR]

Abstract: Background and purpose: We report on the implementation of offline PET/CT-based treatment verification at the Heidelberg Ion Beam Therapy Centre (HIT) and present first clinical cases for post-activation measurements after scanned carbon ion irradiation. Key ingredient of this in-vivo treatment verification is the comparison of irradiation-induced patient activation measured by a PET scanner with a prediction simulated by means of Monte Carlo techniques. Material and methods: At HIT, a commercial full-ring PET/CT scanner has been installed in close vicinity to the treatment rooms. After selected irradiation fractions, the patient either walks to the scanner for acquisition of the activation data or is transported using a shuttle system. The expected activity distribution is obtained from the production of β+-active isotopes simulated by the FLUKA code on the basis of the patient-specific treatment plan, post-processed considering the time course of the respective treatment fraction, the estimated biological washout of the induced activity and a simplified model of the imaging process. Results: We present four patients with different indications of head, head/neck, liver and pelvic tumours. A clear correlation between the measured PET signal and the simulated activity pattern is observed for all patients, thus supporting a proper treatment delivery. In the case of a pelvic tumour patient it was possible to detect minor treatment delivery inaccuracies. Conclusions: The initial clinical experience proves the feasibility of the implemented strategy for offline confirmation of scanned carbon ion irradiation and therefore constitutes a first step towards a comprehensive PET/CT-based treatment verification in the clinical routine at HIT. [Copyright &y& Elsevier]