Objectives Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Methods We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. T1-weighted contrast-enhanced MRI

In-beam positron emission tomography (in-beam PET) is currently the only method for an in situ monitoring of highly tumour-conformed charged hadron therapy. At the experimental carbon ion tumour therapy facility, running at the Gesellschaft für Schwerionenforschung, Darmstadt, Germany, all treatments have been monitored by means of a specially adapted dual-head PET scanner. The positive clinical impact of this project triggered the construction of a hospital-based hadron therapy facility, with in-beam PET expected to monitor more delicate radiotherapeutic situations. Therefore, we have studied possible in-beam PET improvements by optimizing the arrangement of the gamma-ray detectors. For this, a fully 3D, rebinning-free, maximum likelihood expectation maximization algorithm applicable to several closed-ring or dual-head tomographs has been developed. The analysis of beta(+)-activity distributions simulated from real-treatment situations and detected with several detector arrangements allows us to conclude that a dual-head tomograph with narrow gaps yields in-beam PET images with sufficient quality for monitoring head and neck treatments. For monitoring larger irradiation fields, e.g. treatments in the pelvis region, a closed-ring tomograph was seen to be highly desirable. Finally, a study of the space availability for patient and bed, tomograph and beam portal proves the implementation of a closed-ring detector arrangement for in-beam PET to be feasible.

An independent assessment of the dose delivery in ion therapy can be performed using positron emission tomography (PET). For that a distribution of positron emitters which appear as the result of interaction between ions of the therapeutic beam and the irradiated tissue is measured during or after the irradiation. Three concepts for PET monitoring implemented in various therapy facilities are considered in this paper. The in-beam PET concept relies on the PET measurement performed simultaneously to the irradiation by means of a PET scanner which is completely integrated into the irradiation site. The in-room PET concept allows measurement immediately after irradiation by a standalone PET scanner which is installed very close to the irradiation site. In the off-line PET scenario the measurement is performed by means of a standalone PET/CT scanner 10-30 min after the irradiation. These three concepts were evaluated according to image quality criteria, integration costs, and their influence onto the workflow of radiotherapy. In-beam PET showed the best performance. However, the integration costs were estimated as very high for this modality. Moreover, the performance of in-beam PET depends heavily on type and duty cycle of the accelerator. The in-room PET is proposed for planned therapy facilities as a good compromise between the quality of measured data and integration efforts. For facilities which are close to the nuclear medicine departments off-line PET can be suggested under several circumstances.

We extrapolate the impact of recent detector and scintillator developments, enabling sub-nanosecond coincidence timing resolution (tau), onto in-beam positron emission tomography (in-beam PET) for monitoring charged-hadron radiation therapy. For tau < or = 200 ps full width at half maximum, the information given by the time-of-flight (TOF) difference between the two opposing gamma-rays enables shift-variant, artefact-free in-beam tomographic imaging by means of limited-angle, dual-head detectors. We present the corresponding fast, TOF-based and backprojection-free, 3D reconstruction algorithm that, coupled with a real-time data acquisition and a fast detector encoding scheme, allows the sampled beta+-activity to be visualized in the object during the course of the irradiation. Despite the very low statistics scenario typical of in-beam PET, real-treatment simulations show that in-beam TOF-PET enables high-precision images to be obtained in real-time, either with closed-ring or with fixed, dual-head in-beam TOF-PET systems. The latter greatly alleviates the installation of in-beam PET at radiotherapeutic sites.

The proposed approach for automatic segmentation of GB proved to be robust on routine clinical data and showed on all tumor compartments a high automatic detection rate and a high accuracy, comparable to interrater variability. Further work on improvements of the segmentation accuracy for the necrosis compartments should be guided by the evaluation of the clinical relevance.Therefore, we propose this approach as a suitable building block for automatic tumor segmentation to support radiologists or neurosurgeons in the preoperative reading of GB MRI images and characterization of primary GB.