Процес «Спілки Визволення України» та зростання селянського опору в умовах суцільної колективізації

Мета даної роботи полягає у з’ясуванні механізму використання матеріалів процесу «СВУ» на території сучасної Чернігівщини, пропагандистських цілях та реакції на нього з боку як населення, лояльного до влади, так і селян, які вперто чинили опір політиці колективізації

Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours

OBJECTIVE: Dose prediction using deep-learning networks prior to radiotherapy might lead to more efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. MATERIAL &METHODS: Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5mm/3%) and volumetric-modulated arc therapy (VMAT) plans (5mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation. The networks' performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔDmean= DVMAT-DPBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔDmean. RESULTS: Average DVH differences between planned and predicted dose distributions were ≤|6%|for both modalities. The networks classified the organs-at-risk difference as a gain (ΔDmean>0) with 98% precision. An identical modality selection based on planned compared to predicted ΔDmean was made for 18/20 patients. CONCLUSION: Deep-learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualization might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable

ReconSocket : A low-latency raw data streaming interface for real-time MRI-guided radiotherapy

With the recent advent of hybrid MRI-guided radiotherapy systems, continuous intra-fraction MR imaging for motion monitoring has become feasible. The ability to perform real-time custom image reconstructions is however often lacking. In this work we present a low-latency streaming solution, ReconSocket, which provides a real-time stream of k-space data from the magnetic resonance imaging (MRI) to custom reconstruction servers. We determined the performance of the data streaming by measuring the streaming latency (i.e. non-zero time delay due to data transfer and processing) and jitter (i.e. deviations from periodicity) using an ultra-fast 1D MRI acquisition of a moving phantom. Simultaneously, its position was recorded with near-zero time delay. The feasibility of low-latency custom reconstructions was tested by measuring the imaging latency (i.e. time delay between physical change and appearance of that change on the image) for several non-Cartesian 2D and 3D acquisitions using an in-house implemented reconstruction server. The measured streaming latency of the ReconSocket interface was ms. 98% of the incoming data packets arrived within a jitter range of 367 s. This shows that the ReconSocket interface can provide reliable real-time access to MRI data, acquired during the course of a MRI-guided radiotherapy fraction. The total imaging latency was measured to be 221 ms (2D) and 3889 ms (3D) for exemplary acquisitions, using the custom image reconstruction server. These imaging latencies are approximately equal to half of the temporal footprint (T acq/2) of the respective 2D and 3D golden-angle radial sequences. For radial sequences, it was previously showed that T acq/2 is the expected contribution of only the data acquisition to the total imaging latency. Indeed, the contribution of the non-Cartesian reconstruction to the total imaging latency was minor (<10%): 21 ms for 2D, 300 ms for 3D, indicating that the acquisition, i.e. the physical encoding of the image itself is the major contributor to the total imaging latency

Modelling individual temperature profiles from an isolated perfused bovine tongue

To predict the temperature distribution during hyperthermia treatments a thermal model that accounts for the thermal effect of blood flow is mandatory. The DIscrete VAsculature (DIVA) thermal model developed at our department is able to do so; geometrically described vessels are handled individually and the remaining vasculature is modelled collectively. The goal of this paper is to experimentally validate the DIVA model by comparing measured with modelled temperature profiles on an individual basis. Temperature profiles in an isolated bovine tongue heated with three hot water tubes were measured at three controlled perfusion levels, 0, 6 and 24 ml (100 g)(-1) min(-1). The geometries of the tongue, the hot water tubes, thermocouples and discrete vasculature down to 0.5 mm diameter were reconstructed by using cryo-microtome slices at 0.1 mm cubic resolution. This reconstruction of the experimental set-up is used for the modelling of individual profiles. In a no-flow agar-agar phantom, DIVA showed nearly perfect correspondence between measurements and simulations. In the isolated bovine tongue the correspondence at no flow was slightly disturbed due to geometrical distortion in the reconstruction of the experimental set-up. Measurements showed decreasing temperature profiles with increasing perfusion. DIVA correctly predicted this decrease in temperature as well as the thermal impact of a large vessel close to a thermocouple. Blood flow was modelled using discrete vasculature and using a heat sink model. Although at 24 ml (100 g)(-1) min(-1) correspondence between heat sink simulations and measurements was reasonable, modelling discrete vasculature yielded the best correspondence at both 6 and 24 ml (100 g)(-1) min(-1). The results strongly suggest that with accurate data acquisition DIVA can predict temperature profiles on an individual basis. For this kind of patient-specific treatment planning in the clinic, geometrical reconstruction of the anatomy, vasculature and the heating implant is necessary. MRI is capable of providing these data. Further research will be done on thermal simulations of actual clinical hyperthermia treatment