Nearest neighbours graph variational autoEncoder

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements

Validation of Geant4 nuclear reaction models for hadrontherapy and preliminary results with SMF and BLOB

Reliable nuclear fragmentation models are of utmost importance in hadrontherapy, where Monte Carlo (MC) simulations are used to compute the input parameters of the treatment planning software, to validate the deposited dose calculation, to evaluate the biological effectiveness of the radiation, to correlate the bþ emitters production in the patient body with the delivered dose, and to allow a non- invasive treatment verification. Despite of its large use, the models implemented in Geant4 have shown severe limitations in reproducing the measured secondaries yields in ions interaction below 100 MeV/A, in term of production rates, angular and energy distributions [1–3]. We will present a benchmark of the Geant4 models with double-differential cross sec- tion and angular distributions of the secondary fragments produced in the 12C fragmentation at 62 MeV/A on thin carbon target, such a benchmark includes the recently implemented model INCL++ [4,5]. Moreover, we will present the preliminary results, obtained in simulating the same interaction, with SMF [6] and BLOB [7]. Both, SMF and BLOB are semiclassical one-body approaches to solve the Boltzmann-Langevin equation. They include an identical treatment of the mean-field propagation, on the basis of the same effective interaction, but they differ in the way fluctuations are included. In particular, while SMF employs a Uehling-Uhlenbeck collision term and introduces fluctuations as projected on the density space, BLOB introduces fluctuations in full phase space through a modified collision term where nucleon-nucleon correlations are explicitly involved. Both of them, SMF and BLOB, have been developed to sim- ulate the heavy ion interactions in the Fermi-energy regime. We will show their capabilities in describing 12C fragmentation foreseen their implementation in Geant4

An Intraoperative β−\beta^- Detecting Probe For Radio-Guided Surgery in Tumour Resection

The development of the $\beta^-$ based radio-guided surgery aims to extend the technique to those tumours where surgery is the only possible treatment and the assessment of the resection would most profit from the low background around the lesion, as for brain tumours. Feasibility studies on meningioma, glioma, and neuroendocrine tumors already estimated the potentiality of this new treatment. To validate the technique, prototypes of the intraoperative probe required by the technique to detect $\beta^-$ radiation have been developed. This paper discusses the design details of the device and the tests performed in laboratory. In such tests particular care has to be taken to reproduce the surgical field conditions. The innovative technique to produce specific phantoms and the dedicated testing protocols is described in detail.Comment: 7 pages, 15 figure

A DROP-IN beta probe for robot-assisted 68Ga-PSMA radioguided surgery: first ex vivo technology evaluation using prostate cancer specimens

Background: Recently, a flexible DROP-IN gamma-probe was introduced for robot-assisted radioguided surgery, using traditional low-energy SPECT-isotopes. In parallel, a novel approach to achieve sensitive radioguidance using beta-emitting PET isotopes has been proposed. Integration of these two concepts would allow to exploit the use of PET tracers during robot-assisted tumor-receptor-targeted. In this study, we have engineered and validated the performance of a novel DROP-IN beta particle (DROP-INβ) detector. Methods: Seven prostate cancer patients with PSMA-PET positive tumors received an additional intraoperative injection of ~ 70 MBq 68Ga-PSMA-11, followed by robot-assisted prostatectomy and extended pelvic lymph node dissection. The surgical specimens from these procedures were used to validate the performance of our DROP-INβ probe prototype, which merged a scintillating detector with a housing optimized for a 12-mm trocar and prograsp instruments. Results: After optimization of the detector and probe housing via Monte Carlo simulations, the resulting DROP-INβ probe prototype was tested in a robotic setting. In the ex vivo setting, the probe—positioned by the robot—was able to identify 68Ga-PSMA-11 containing hot-spots in the surgical specimens: signal-to-background (S/B) was > 5 when pathology confirmed that the tumor was located < 1 mm below the specimen surface. 68Ga-PSMA-11 containing (and PET positive) lymph nodes, as found in two patients, were also confirmed with the DROP-INβ probe (S/B > 3). The rotational freedom of the DROP-IN design and the ability to manipulate the probe with the prograsp tool allowed the surgeon to perform autonomous beta-tracing. Conclusions: This study demonstrates the feasibility of beta-radioguided surgery in a robotic context by means of a DROP-INβ detector. When translated to an in vivo setting in the future, this technique could provide a valuable tool in detecting tumor remnants on the prostate surface and in confirmation of PSMA-PET positive lymph nodes. © 2020, The Author(s)

Stability and efficiency of a CMOS sensor as detector of low energy beta and gamma particles

Radio Guided Surgery (RGS) is a nuclear medicine technique allowing the surgeon to identify tumor residuals in real time with a millimetric resolution, thanks to a radiopharmaceutical as tracer and a probe as detector. The use of beta(-) emitters, instead of gamma or beta(+), has been recently proposed with the aim to increase the technique sensitivity and reducing both the administered activity to the patient and the medical exposure. In this paper, the possibility to use the commercial CMOS Image Sensor MT9V115, originally designed for visible light imaging, as beta(-) radiation detector RGS is discussed. Being crucial characteristics in a surgical environment, in particular its stability against time, operating temperature, integration time and gain has been studied on laboratory measurements. Moreover, a full Monte Carlo simulation of the detector has been developed. Its validation against experimental data allowed us to obtain efficiency curves for both beta and gamma particles, and also to evaluate the effect of the covering heavy resin protective layer that is present in the "off the shelf" detector. This study suggests that a dedicated CMOS Image Sensor (i.e. one produced without the covering protective layer) represents the ideal candidate detector for RGS, able to massively increase the amount of application cases and the efficacy of this technique

Localization of anatomical changes in patients during proton therapy with in-beam PET monitoring: a voxel-based morphometry approach exploiting Monte Carlo simulations

Purpose: In-beam positron emission tomography (PET) is one of the modalities that can be used for in vivo noninvasive treatment monitoring in proton therapy. Although PET monitoring has been frequently applied for this purpose, there is still no straightforward method to translate the information obtained from the PET images into easy-to-interpret information for clinical personnel. The purpose of this work is to propose a statistical method for analyzing in-beam PET monitoring images that can be used to locate, quantify, and visualize regions with possible morphological changes occurring over the course of treatment. Methods: We selected a patient treated for squamous cell carcinoma (SCC) with proton therapy, to perform multiple Monte Carlo (MC) simulations of the expected PET signal at the start of treatment, and to study how the PET signal may change along the treatment course due to morphological changes. We performed voxel-wise two-tailed statistical tests of the simulated PET images, resembling the voxel-based morphometry (VBM) method commonly used in neuroimaging data analysis, to locate regions with significant morphological changes and to quantify the change. Results: The VBM resembling method has been successfully applied to the simulated in-beam PET images, despite the fact that such images suffer from image artifacts and limited statistics. Three dimensional probability maps were obtained, that allowed to identify interfractional morphological changes and to visualize them superimposed on the computed tomography (CT) scan. In particular, the characteristic color patterns resulting from the two-tailed statistical tests lend themselves to trigger alarms in case of morphological changes along the course of treatment. Conclusions: The statistical method presented in this work is a promising method to apply to PET monitoring data to reveal interfractional morphological changes in patients, occurring over the course of treatment. Based on simulated in-beam PET treatment monitoring images, we showed that with our method it was possible to correctly identify the regions that changed. Moreover we could quantify the changes, and visualize them superimposed on the CT scan. The proposed method can possibly help clinical personnel in the replanning procedure in adaptive proton therapy treatments

Monitoring Carbon Ion Beams Transverse Position Detecting Charged Secondary Fragments: Results From Patient Treatment Performed at CNAO

Particle therapy in which deep seated tumours are treated using 12C ions (Carbon Ions RadioTherapy or CIRT) exploits the high conformity in the dose release, the high relative biological effectiveness and low oxygen enhancement ratio of such projectiles. The advantages of CIRT are driving a rapid increase in the number of centres that are trying to implement such technique. To fully profit from the ballistic precision achievable in delivering the dose to the target volume an online range verification system would be needed, but currently missing. The 12C ions beams range could only be monitored by looking at the secondary radiation emitted by the primary beam interaction with the patient tissues and no technical solution capable of the needed precision has been adopted in the clinical centres yet. The detection of charged secondary fragments, mainly protons, emitted by the patient is a promising approach, and is currently being explored in clinical trials at CNAO. Charged particles are easy to detect and can be back-tracked to the emission point with high efficiency in an almost background-free environment. These fragments are the product of projectiles fragmentation, and are hence mainly produced along the beam path inside the patient. This experimental signature can be used to monitor the beam position in the plane orthogonal to its flight direction, providing an online feedback to the beam transverse position monitor chambers used in the clinical centres. This information could be used to cross-check, validate and calibrate, whenever needed, the information provided by the ion chambers already implemented in most clinical centres as beam control detectors. In this paper we study the feasibility of such strategy in the clinical routine, analysing the data collected during the clinical trial performed at the CNAO facility on patients treated using 12C ions and monitored using the Dose Profiler (DP) detector developed within the INSIDE project. On the basis of the data collected monitoring three patients, the technique potential and limitations will be discussed

Perspectives of Gas Phase Ion Chemistry: Spectroscopy and Modeling

The study of ions in the gas phase has a long history and has involved both chemists and physicists. The interplay of their competences with the use of very sophisticated commercial and/or homemade instrumentations and theoretical models has improved the knowledge of thermodynamics and kinetics of many chemical reactions, even if still many stages of these processes need to be fully understood. The new technologies and the novel free-electron laser facilities based on plasma acceleration open new opportunities to investigate the chemical reactions in some unrevealed fundamental aspects. The synchrotron light source can be put beside the FELs, and by mass spectrometric techniques and spectroscopies coupled with versatile ion sources it is possible to really change the state of the art of the ion chemistry in different areas such as atmospheric and astro chemistry, plasma chemistry, biophysics, and interstellar medium (ISM). In this manuscript we review the works performed by a joint combination of the experimental studies of ion–molecule reactions with synchrotron radiation and theoretical models adapted and developed to the experimental evidence. The review concludes with the perspectives of ion–molecule reactions by using FEL instrumentations as well as pump probe measurements and the initial attempt in the development of more realistic theoretical models for the prospective improvement of our predictive capability

Filoblu: Sentiment Analysis Application to Doctor-Patient Interactions

Domiciliary care for oncologic patients may have clear advantage in terms of improved quality of life, psychological well-being and cost effectiveness. Coordination and collaboration between oncologists and patients are essential in order to guarantee the healthcare provision and treatment adherence in cancer home therapy. FILO BLU is a software application that aims to improve the communication in the doctor-patient relationship composed of two messaging APPs for smartphone, one for the patient/caregiver and one for the medical team, it is equipped with a module for the interoperability with portable medical monitoring systems and it is integrated with the patients’ electronic medical records. This allows the doctor to respond to requests having always available all the clinical information. To improve decision making and workload management we develop an expert system for the analysis of medical-patient communications that aims to score the patient's clinical status and that analyzes the flow of communications in order to signal to doctors, through an "attention" score, potential critical situations keeping into account both the written texts and any physiological values monitored. We generate synthetic data, composed of a simulation of the patient physiology and semi-automatically generated sentences, to test operative workflow. To easily combine numerical and textual data sources for the classification we choose a deep learning approach. We tested multiple neural networks architectures used in sentiment analysis to classify patients’ messages according to the severity of the clinical status both alone and in conjunction with physiological parameters recordings. While we do not expect that our synthetic data can replace data gathered through the usage in clinical settings, it creates a controlled test environment for classification and context sensitive spelling correction algorithm