Search

PURPOSE In-beam Positron Emission Tomography (PET) is one of the modalities that can be used for in-vivo non-invasive 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. MATERIALS AND 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 inter-fractional 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 inter-fractional 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. This article is protected by copyright. All rights reserved.

Open physics 19(1), 383 - 394 (2021). doi:10.1515/phys-2021-0032, Published by de Gruyter, Berlin

Positron emission tomography (PET) is a well established imaging technique for range monitoring in hadrontherapy. Multiple fields are standard protocol in treatments, but because of washout and residual activity background from previous irradiation plans, to this date quantitative verification of the particle range for each beam field is still an open issue. In this paper, a new method for the evaluation with a PET detector of the activity range of the second field of a treatment is discussed. Two treatment plans with two parallel-opposed beam fields, one using protons and one using carbon ions, were delivered on PMMA phantoms. In both cases, the second beam field was extracted from the first irradiation residual activity and compared with a reference image, obtained from the experimental acquisition of the second field alone. Results demonstrate good agreement between the extracted second field and the reference image, with average difference in the activity range along the preferential direction of the beam less than 0.5 mm for protons, and 1.5 mm for carbon ions. Without taking into account any preferential direction, differences within 0.5 mm were found for both cases. The method will soon be tested with nonhomogeneous phantoms and, subsequently, with in-vivo clinical data.

Simultaneous PET/MR/EEG (Positron Emission Tomography – Magnetic Resonance – Electroencephalography), a new tool for the investigation of neuronal networks in the human brain, is presented here within the framework of the European Union Project TRIMAGE. The trimodal, cost-effective PET/MR/EEG imaging tool makes use of cutting edge technology both in PET and in MR fields. A novel type of magnet (1.5T, non-cryogenic) has been built together with a PET scanner that makes use of the most advanced photodetectors (i.e., SiPM matrices), scintillators matrices (LYSO) and digital electronics. The combined PET/MR/EEG system is dedicated to brain imaging and has an inner diameter of 260 mm and an axial Field-of-View of 160 mm.It enables the acquisition and assessment of molecular metabolic information with high spatial and temporal resolution in a given brain simultaneously. The dopaminergic system and the glutamatergic system in schizophrenic patients are investigated via PET, the same physiological/pathophysiological conditions with regard to functional connectivity, via fMRI, and its electrophysiological signature via EEG. In addition to basic neuroscience questions addressing neurovascular-metabolic coupling, this new methodology lays the foundation for individual physiological and pathological fingerprints for a wide research field addressing healthy aging, gender effects, plasticity and different psychiatric and neurological diseases.The preliminary performances of two components of the imaging tool (PET and MR) are discussed. Initial results of the search of possible candidates for suitable schizophrenia biomarkers are also presented as obtained with PET/MR systems available to the collaboration.

The 4D GRAPH-X (Dynamic GRAting-based PHase contrast x-ray imaging) project aims at developing a prototype of an x-ray grating-based phase-contrast imaging scanner in a laboratory setting, which is based on the Moirè single-shot acquisition method in order to be optimized for analysing moving objects (in the specific case, a dynamic thorax phantom), that could evolve into a suitable tool for biomedical applications although it can be extended to other application fields. When designing an x-ray Talbot-Lau interferometer, high visibility and sensitivity are two important figures of merit, strictly related to the performance of the system in obtaining high quality phase contrast and dark-field images. Wave field simulations are performed to optimize the setup specifications and construct a high-resolution and high-sensitivity imaging system. In this work, the design of a dynamic imaging setup using a conventional milli-focus x-ray source is presented. Optimization by wave front simulations leads to a symmetric configuration with 5.25 μm pitch at third Talbot order and 45 keV design energy. The simulated visibility is about 22%. Results from GATE based Monte Carlo simulations show a 19% transmission percentage of the incoming beam into the detector after passing through all the gratings and the sample. Such results are promising in view of building a system optimized for dynamic imaging.

This paper focuses on the design and the characterization of a frequency-selective shield for positron emission tomography (PET) detector modules of hybrid magnetic resonance-PET scanners, where the shielding of the PET cassettes is located close to the observed object. The proposed shielding configuration is designed and optimized to guarantee a high shielding effectiveness (SE) of up to 60 dB for ${B} _{1}$ -fields at the Larmor frequency of 64 MHz, thus preventing interactions between the radio-frequency (RF) coil and PET electronics. On the other hand, the shield is transparent to the gradient fields with the consequence that eddy-current artifacts in the acquired EPI images are significantly reduced with respect to the standard solid-shield configuration. The frequency-selective behavior of the shield is characterized and validated via simulation studies with CST MICROWAVE STUDIO in the megahertz and kilohertz range. Bench measurements with an RF coil built in-house demonstrated the high SE at the Larmor frequency. Moreover, measurements on a 4-T human scanner confirmed the abolishment of eddy current artifact and also provided an understanding of where the eddy currents occur with respect to the sequence parameters. Simulations and measurements for the proposed shielding concept were compared with a solid copper shielding configuration.

The high dose conformity and healthy tissue sparing achievable in Particle Therapy when using C ions calls for safety factors in treatment planning, to prevent the tumor under-dosage related to the possible occurrence of inter-fractional morphological changes during a treatment. This limitation could be overcome by a range monitor, still missing in clinical routine, capable of providing on-line feedback. The Dose Profiler (DP) is a detector developed within the INnovative Solution for In-beam Dosimetry in hadronthErapy (INSIDE) collaboration for the monitoring of carbon ion treatments at the CNAO facility (Centro Nazionale di Adroterapia Oncologica) exploiting the detection of charged secondary fragments that escape from the patient. The DP capability to detect inter-fractional changes is demonstrated by comparing the obtained fragment emission maps in different fractions of the treatments enrolled in the first ever clinical trial of such a monitoring system, performed at CNAO. The case of a CNAO patient that underwent a significant morphological change is presented in detail, focusing on the implications that can be drawn for the achievable inter-fractional monitoring DP sensitivity in real clinical conditions. The results have been cross-checked against a simulation study.

In-beam positron emission tomography (PET) monitoring can provide early treatment assessment in proton therapy. However, open ring configurations and low positron emitter production yields degrade image quality, hampering the assessment accuracy. To achieve the highest precision for range monitoring, it is compulsory to mitigate image noise and compensate for data truncation. The goal of this article is to study the performance of state-of-the-art algorithms for in-beam PET image reconstruction, by evaluating the impact of the system response model and assessing the accuracy of range measurements. The approaches investigated here were maximum- a-posteriori algorithms combined with total-variation and median-root priors. Maximum-likelihood-expectation-maximization was used as reference. To compute the system matrix, different models were compared: a precise Monte Carlo model, and single-ray tracing with and without Gaussian blurring in image space. The proposed methods were tested on simulations of spread-out-bragg-peaks delivered on phantoms. The in-beam PET innovative imaging scanner geometry was used as a case study. After image post-processing, the explored methods delivered similar results. This article demonstrates the feasibility and reliability of using a simple and fast reconstruction method to perform range evaluations, given the correct image post-processing.

Summary: Charged particle therapy exploits proton or 12C beams to treat deep-seated solid tumors. Due to the advantageous characteristics of charged particles energy deposition in matter, the maximum of the dose is released to the tumor at the end of the beam range, in the Bragg peak region. However, the beam nuclear interactions with the patient tissues induces fragmentation both of projectile and target nuclei and needs to be carefully taken into account. In proton treatments, target fragmentation produces low energy, short range fragments along all the beam range, which deposit a non negligible dose in the entry channel. In 12C treatments the main concern is represented by long range fragments due to beam fragmentation that release their dose in the healthy tissues beyond the tumor. The FOOT experiment (FragmentatiOn Of Target) of INFN is designed to study these processes, in order to improve the nuclear fragmentation description in next generation Treatment Planning Systems and the treatment plans quality. Target (16O and 12C nuclei) fragmentation induced by –proton beams at therapeutic energies will be studied via an inverse kinematic approach, where 16O and 12C therapeutic beams impinge on graphite and hydrocarbon targets to provide the nuclear fragmentation cross section on hydrogen. Projectile fragmentation of 16O and 12C beams will be explored as well. The FOOT detector includes a magnetic spectrometer for the fragments momentum measurement, a plastic scintillator for ΔE and time of flight measurements and a crystal calorimeter to measure the fragments kinetic energy. These measurements will be combined in order to make an accurate fragment charge and isotopic identification. Keywords: Hadrontherapy, Nuclear fragmentation cross sections, Tracking detectors, Scintillating detectors

Treatment quality assessment is a crucial feature for both present and next-generation ion therapy facilities. Several approaches are being explored, based on prompt radiation emission or on PET signals by [Formula: see text]-decaying isotopes generated by beam interactions with the body. In-beam PET monitoring at synchrotron-based ion therapy facilities has already been performed, either based on inter-spill data only, to avoid the influence of the prompt radiation, or including both in-spill and inter-spill data. However, the PET images either suffer of poor statistics (inter-spill) or are more influenced by the background induced by prompt radiation (in-spill). Both those problems are expected to worsen for accelerators with improved duty cycle where the inter-spill interval is reduced to shorten the treatment time. With the aim of assessing the detector performance and developing techniques for background reduction, a test of an in-beam PET detector prototype was performed at the CNAO synchrotron-based ion therapy facility in full-beam acquisition modality. Data taken with proton beams impinging on PMMA phantoms showed the system acquisition capability and the resulting activity distribution, separately reconstructed for the in-spill and the inter-spill data. The coincidence time resolution for in-spill and inter-spill data shows a good agreement, with a slight deterioration during the spill. The data selection technique allows the identification and rejection of most of the background originated during the beam delivery. The activity range difference between two different proton beam energies (68 and 72 MeV) was measured and found to be in sub-millimeter agreement with the expected result. However, a slightly longer (2 mm) absolute profile length is obtained for in-spill data when compared to inter-spill data.

This paper describes the development and characterization of a Δ E-TOF detector composed of a plastic scintillator bar coupled at both ends to silicon photomultipliers. This detector is a prototype of a larger version which will be used in the FOOT (FragmentatiOn Of Target) experiment to identify the fragments produced by ion beams accelerated onto a hydrogen-enriched target. The final Δ E-TOF detector will be composed of two layers of plastic scintillator bars with orthogonal orientation and will measure, for each crossing fragment, the energy deposited in the plastic scintillator ( Δ E), the time of flight (TOF), and the coordinates of the interaction position in the scintillator. To meet the FOOT experimental requirements, the detector should have energy resolution of a few percents and time resolution of 70 ps, and it should allow to discriminate multiple fragments belonging to the same event. To evaluate the achievable performances, the detector prototype was irradiated with protons of kinetic energy in the 70–230 MeV range and interacting at several positions along the bar. The measured energy resolution σ Δ E ∕ Δ E was 6–14%, after subtracting the fluctuations of the deposited energy. A time resolution σ between 120 and 180 ps was obtained with respect to a trigger detector. A spatial resolution σ of 1.9 cm was obtained for protons interacting at the center of the bar.

In hadron therapy, the accelerated ions, interacting with the body of the patient, cause the fragmentation of both projectile and target nuclei. The fragments interact with the human tissues depositing energy both in the entrance channel and in the volume surrounding the tumor. The knowledge of the fragments features is crucial to determine the energy amount deposited in the human body, and - hence - the damage to the organs and to the tissues around the tumor target. The FOOT (FragmentatiOn Of Target) experiment aims at studying the fragmentation induced by the interaction of a proton beam (150-250 MeV/n) inside the human body. The FOOT detector includes an electronic setup for the identification of Z ≥ 3 fragments integrated with an emulsion spectrometer to measure Z ≤ 3 fragments. Charge identification by nuclear emulsions is based on the development of techniques of controlled fading of the particle tracks inside the nuclear emulsion, that extend the dynamical range of the films developed for the tracking of minimum ionising particles. The controlled fading strongly depends on temperature, relative humidity and treatment duration. In this study the performances in terms of charge separation of proton, helium and carbon particles, obtained on a batch of new emulsion films produced in Japan are reported.

The TRIMAGE project aims at developing a brain-dedicated PET/MR/EEG system able to perform simultaneous PET, MR, and EEG acquisitions for application in schizophrenia. Both PET and MR components have been designed in this project. The PET component consists of a full ring with 18 sectors each comprising three square detector modules. The modules are based on dual-layer staggered matrices of LYSO crystals read out by silicon photomultipliers. This paper describes in full detail the final version of the PET detectors and the related electronics. It also reports on the preliminary performance of a pair of sectors in terms of pixel resolvability index (RI), energy resolution, singles count rate capability, and coincidence time resolution (CTR). The procedures used for the optimization and calibration of PET detector are described. Results demonstrate the pixel/layer identification performance with an RI of about 0.2 while the energy resolution resulted in 20% and 22% FWHM for the bottom and top layer, respectively. The maximum singles count rate of a PET detector is about 700 kcps and the CTR of two sectors is 515 ps.

Purpose: M5L, a fully automated computer-aided detection (CAD) system for the detection and segmentation of lung nodules in thoracic computed tomography (CT), is presented and validated on several image datasets. Methods: M5L is the combination of two independent subsystems, based on the Channeler Ant Model as a segmentation tool [lung channeler ant model (lungCAM)] and on the voxel-based neural approach. The lungCAM was upgraded with a scan equalization module and a new procedure to recover the nodules connected to other lung structures; its classification module, which makes use of a feed-forward neural network, is based of a small number of features (13), so as to minimize the risk of lacking generalization, which could be possible given the large difference between the size of the training and testing datasets, which contain 94 and 1019 CTs, respectively. The lungCAM (standalone) and M5L (combined) performance was extensively tested on 1043 CT scans from three independent datasets, including a detailed analysis of the full Lung Image Database Consortium/Image Database Resource Initiative database, which is not yet found in literature. Results: The lungCAM and M5L performance is consistent across the databases, with a sensitivity of about 70% and 80%, respectively, at eight false positive findings per scan, despite the variable annotation criteria and acquisition and reconstruction conditions. A reduced sensitivity is found for subtle nodules and ground glass opacities (GGO) structures. A comparison with other CAD systems is also presented. Conclusions: The M5L performance on a large and heterogeneous dataset is stable and satisfactory, although the development of a dedicated module for GGOs detection could further improve it, as well as an iterative optimization of the training procedure. The main aim of the present study was accomplished: M5L results do not deteriorate when increasing the dataset size, making it a candidate for supporting radiologists on large scale screenings and clinical programs.

Hadrontherapy is a method for treating cancer with very targeted dose distributions and enhanced radiobiological effects. To fully exploit these advantages, in vivo range monitoring systems are required. These devices measure, preferably during the treatment, the secondary radiation generated by the beam-tissue interactions. However, since correlation of the secondary radiation distribution with the dose is not straightforward, Monte Carlo (MC) simulations are very important for treatment quality assessment. The INSIDE project constructed an in-beam PET scanner to detect signals generated by the positron-emitting isotopes resulting from projectile-target fragmentation. In addition, a FLUKA-based simulation tool was developed to predict the corresponding reference PET images using a detailed scanner model. The INSIDE in-beam PET was used to monitor two consecutive proton treatment sessions on a patient at the Italian Center for Oncological Hadrontherapy (CNAO). The reconstructed PET images were updated every 10 s providing a near real-time quality assessment. By half-way through the treatment, the statistics of the measured PET images were already significant enough to be compared with the simulations with average differences in the activity range less than 2.5 mm along the beam direction. Without taking into account any preferential direction, differences within 1 mm were found. In this paper, the INSIDE MC simulation tool is described and the results of the first in vivo agreement evaluation are reported. These results have justified a clinical trial, in which the MC simulation tool will be used on a daily basis to study the compliance tolerances between the measured and simulated PET images.

Particle therapy exploits the energy deposition pattern of hadron beams. The narrow Bragg Peak at the end of range is a major advantage but range uncertainties can cause severe damage and require online verification to maximise the effectiveness in clinics. In-beam Positron Emission Tomography (PET) is a non-invasive, promising in-vivo technique, which consists in the measurement of the β+ activity induced by beam-tissue interactions during treatment, and presents the highest correlation of the measured activity distribution with the deposited dose, since it is not much influenced by biological washout. Here we report the first clinical results obtained with a state-of-the-art in-beam PET scanner, with on-the-fly reconstruction of the activity distribution during irradiation. An automated time-resolved quantitative analysis was tested on a lacrimal gland carcinoma case, monitored during two consecutive treatment sessions. The 3D activity map was reconstructed every 10 s, with an average delay between beam delivery and image availability of about 6 s. The correlation coefficient of 3D activity maps for the two sessions (above 0.9 after 120 s) and the range agreement (within 1 mm) prove the suitability of in-beam PET for online range verification during treatment, a crucial step towards adaptive strategies in particle therapy.

In vivo range monitoring techniques are necessary in order to fully take advantage of the high dose gradients deliverable in hadrontherapy treatments. Positron emission tomography (PET) scanners can be used to monitor beam-induced activation in tissues and hence measure the range. The INSIDE (Innovative Solutions for In-beam DosimEtry in Hadrontherapy) in-beam PET scanner, installed at the Italian National Center of Oncological Hadrontherapy (CNAO, Pavia, Italy) synchrotron facility, has already been successfully tested in vivo during a proton therapy treatment. We discuss here the system performance evaluation with carbon ion beams, in view of future in vivo tests. The work is focused on the analysis of activity images obtained with therapeutic treatments delivered to polymethyl methacrylate (PMMA) phantoms, as well as on the test of an innovative and robust Monte Carlo simulation technique for the production of reliable prior activity maps. Images are reconstructed using different integration intervals, so as to monitor the activity evolution during and after the treatment. Three procedures to compare activity images are presented, namely Pearson correlation coefficient, Beam's eye view and overall view. Images of repeated irradiations of the same treatments are compared to assess the integration time necessary to provide reproducible images. The range agreement between simulated and experimental images is also evaluated, so as to validate the simulation capability to provide sound prior information. The results indicate that at treatment end, or at most 20 s afterwards, the range measurement is reliable within 1-2 mm, when comparing both different experimental sessions and data with simulations. In conclusion, this work shows that the INSIDE in-beam PET scanner performance is promising towards its in vivo test with carbon ions.

Background and objective: Standardised Uptake Value (SUV), in clinical research and practice, is a marker of tumour avidity in Positron Emission Tomography/Computed Tomography (PET/CT). Since many technical, physical and physiological factors affect the SUV absolute measurement, the liver uptake is often used as reference value both in quantitative and semi-quantitative evaluation. The purpose of this investigation was to automatically detect the liver position in whole-body PET/CT scans and extract its average SUV value. Methods: We developed an algorithm, called LIver DEtection Algorithm (LIDEA), that analyses PET/CT scans, and under the assumption that the liver is a large homogeneous volume near the centre of mass of the patient, finds its position and automatically places a region of interest (ROI) in the liver, which is used to calculate the average SUV. The algorithm was validated on a population of 630 PET/CT scans coming from more than 60 different scanners. The SUV was also calculated by manually placing a large ROI in the liver. Results: LIDEA identified the liver with a 97.3% sensitivity with PET/CT images only and reached a 98.9% correct detection rate when using the co-registered CT scan to avoid liver misidentification in the right lung. The average liver SUV obtained with LIDEA was successfully validated against its manual assessment, with no systematic difference (0.11 ± 0.36 SUV units) and a R 2 = 0.89 correlation coefficient. Conclusions: LIDEA proved to be a reliable tool to automatically identify and extract the average SUV of the liver in oncological whole-body PET/CT scans.

The INSIDE collaboration aims to build an on-line hadrontherapy monitoring system, based on a dual-head in-beam PET scanner and a secondary charged particles profiler. In this work preliminary experimental results are presented. The validation of the FLUKA-based Monte Carlo simulation tool is shown together with the expected scanner performances.

Particle therapy uses protons or $^{12}$C beams for the treatment of deep-seated solid tumors. Due to the features of the energy deposition of charged particles in matter, a limited amount of dose is released to the healthy tissue in the beam entrance region, while the maximum of the dose is released to the tumor at the end of the beam range, in the Bragg peak region. However nuclear interactions between beam and patient tissues induce fragmentation both of projectile and target. This has to be carefully taken into account since different ions have different effectiveness in producing a biological damage. par In $^{12}$C treatments the main concern are long range forward emitted secondary ions produced in projectile fragmentation that release dose in the healthy tissue after the tumor. Instead, in a proton treatment, the target fragmentation produces low energy, short range fragments along all the beam range. The FOOT experiment (FragmentatiOn Of Target) is designed to study these processes. Target nuclei ($^{16}$O, $^{12}$C) fragmentation induced by 150-250 MeV proton beam will be studied by means of the inverse kinematic approach: $^{16}$O,$^{12}$C therapeutic beams, at the quoted kinetic energy per nucleon, collide on graphite and hydrocarbons target. The cross section on Hydrogen can be then extracted by subtraction. This configuration explores also the projectile fragmentation of these O and C beams, or other ions of therapeutic interest, such as $^4$He for instance. The detector includes a magnetic spectrometer based on silicon pixel and strip detectors, a scintillating crystal calorimeter able to stop the heavier produced fragments, and a $\Delta E$ detector, with TOF capability, to achieve the needed energy resolution and particle identification. In addition to the electronic apparatus, an alternative setup based on the concept of the "Emulsion Cloud Chamber", coupled with the interaction region of the electronic FOOT setup, will provide the measurement of lighter charged fragments: protons, deuterons, tritons and Helium nuclei. The FOOT data taking is foreseen in the available experimental rooms existing in the presently operational charged particle therapy facilities in Europe, and possibly at GSI. An initial phase with the emulsion setup will start in early 2018, while the complete electronic detector will take data starting in 2019. In this work a general description of the FOOT experiment and of its expected performances is presented.

The quality assurance of particle therapy treatment is a fundamental issue that can be addressed by developing reliable monitoring techniques and indicators of the treatment plan correctness. Among the available imaging techniques, positron emission tomography (PET) has long been investigated and then clinically applied to proton and carbon beams. In 2013, the Innovative Solutions for Dosimetry in Hadrontherapy (INSIDE) collaboration proposed an innovative bimodal imaging concept that combines an in-beam PET scanner with a tracking system for charged particle imaging. This paper presents the general architecture of the INSIDE project but focuses on the in-beam PET scanner that has been designed to reconstruct the particles range with millimetric resolution within a fraction of the dose delivered in a treatment of head and neck tumors. The in-beam PET scanner has been recently installed at the Italian National Center of Oncologic Hadrontherapy (CNAO) in Pavia, Italy, and the commissioning phase has just started. The results of the first beam test with clinical proton beams on phantoms clearly show the capability of the in-beam PET to operate during the irradiation delivery and to reconstruct on-line the beam-induced activity map. The accuracy in the activity distal fall-off determination is millimetric for therapeutic doses.

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

Contains fulltext : 179531.pdf (Publisher’s version ) (Open Access) Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified. This paper describes the setup of LUNA16 and presents the results of the challenge so far. Moreover, the impact of combining individual systems on the detection performance was also investigated. It was observed that the leading solutions employed convolutional networks and used the provided set of nodule candidates. The combination of these solutions achieved an excellent sensitivity of over 95% at fewer than 1.0 false positives per scan. This highlights the potential of combining algorithms to improve the detection performance. Our observer study with four expert readers has shown that the best system detects nodules that were missed by expert readers who originally annotated the LIDC-IDRI data. We released this set of additional nodules for further development of CAD systems. 13 p.

The TRIMAGE consortium aims to develop a multimodal PET/MR/EEG brain scanner dedicated to the early diagnosis of schizophrenia and other mental health disorders. The PET component features a full ring made of 18 detectors, each one consisting of twelve 8x8 Silicon PhotoMultipliers (SiPMs) tiles coupled to two segmented LYSO crystal matrices with staggered layers. In each module, the crystals belonging to the bottom layer are coupled one to one to the SiPMs, while each crystal of the top layer is coupled to four crystals of the bottom layer. This configuration allows to increase the crystal thickness while reducing the depth of interaction uncertainty, as photons interacting in different layers are expected to produce different light patterns on the SiPMs. The PET scanner will implement the pixel/layer identification on a front-end FPGA. This will allow increasing the effective bandwidth, setting at the same time restrictions on the complexity of the algorithms to be implemented. In this work two algorithms whose implementation is feasible directly on an FPGA are presented and evaluated. The first algorithm implements a method based on adaptive thresholding, while the other uses a linear Support Vector Machine (SVM) trained to distinguish the light pattern coming from two different layers. The validation of the algorithm performance is carried out by using simulated data generated with the GAMOS Monte Carlo. The obtained results show that the achieved accuracy in layer and pixel identification is above the 90% for both the proposed approaches.

Background Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible. Subjects A reference group composed of 144 AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24 month follow-up (MCI-non converters). All subjects came from the ADNI database. Methods We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm. Results We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC) = 0.97 (sensitivity ≃ 89% at specificity ≃ 94%) and Controls from MCI-converters with an AUC = 0.92 (sensitivity ≃ 89% at specificity ≃ 80%). MCI-converters are separated from MCI-non converters with AUC = 0.74(sensitivity ≃ 72% at specificity ≃ 65%). Findings The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population.

Positron emission tomography is one of the most mature techniques for monitoring the particles range in hadron therapy, aiming to reduce treatment uncertainties and therefore the extent of safety margins in the treatment plan. In-beam PET monitoring has been already performed using inter-spill and post-irradiation data, i.e. while the particle beam is off or paused. The full beam acquisition procedure is commonly discarded because the particle spills abruptly increase the random coincidence rates and therefore the image noise. This is because random coincidences cannot be separated by annihilation photons originating from radioactive decays and cannot be corrected with standard random coincidence techniques due to the time correlation of the beam-induced background with the ion beam microstructure. The aim of this paper is to provide a new method to recover in-spill data to improve the images obtained with full-beam PET acquisitions. This is done by estimating the temporal microstructure of the beam and thus selecting input PET events that are less likely to be random ones. The PET detector we used was the one developed within the INSIDE project and tested at the CNAO synchrotron-based facility. The data were taken on a PMMA phantom irradiated with 72 MeV proton pencil beams. The obtained results confirm the possibility of improving the acquired PET data without any external signal coming from the synchrotron or ad hoc detectors.

Contains fulltext : 89675.pdf (Publisher’s version ) (Open Access) Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements. 01 december 2010

A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by twelve morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with 6 FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).

The purpose of this study is to develop a software for the extraction of the hippocampus and surrounding medial temporal lobe (MTL) regions from T1-weighted magnetic resonance (MR) images with no interactive input from the user, to introduce a novel statistical indicator, computed on the intensities in the automatically extracted MTL regions, which measures atrophy, and to evaluate the accuracy of the newly developed intensity-based measure of MTL atrophy to (a) distinguish between patients with Alzheimer disease (AD), patients with amnestic mild cognitive impairment (aMCI), and elderly controls by using established criteria for patients with AD and aMCI as the reference standard and (b) infer about the clinical outcome of aMCI patients. For the development of the software, the study included 61 patients with mild AD (17 men, 44 women; mean age +/- standard deviation (SD), 75.8 years +/- 7.8; Mini Mental State Examination (MMSE) score, 24.1 +/- 3.1), 42 patients with aMCI (11 men, 31 women; mean age +/- SD, 75.2 years +/- 4.9; MMSE score, 27.9 +/- 1.9), and 30 elderly healthy controls (10 men, 20 women; mean age +/- SD, 74.7 years +/- 5.2; MMSE score, 29.1 +/- 0.8). For the evaluation of the statistical indicator, 150 patients with mild AD (62 men, 88 women; mean age +/- SD, 76.3 years +/- 5.8; MMSE score, 23.2 +/- 4.1), 247 patients with aMCI (143 men, 104 women; mean age +/- SD, 75.3 years +/- 6.7; MMSE score, 27.0 +/- 1.8), and 135 elderly healthy controls (61 men, 74 women; mean age +/- SD, 76.4 years +/- 6.1). Fifty aMCI patients were evaluated every 6 months over a 3 year period to assess conversion to AD. For each participant, two subimages of the MTL regions were automatically extracted from T1-weighted MR images with high spatial resolution. An intensity-based MTL atrophy measure was found to separate control, MCI, and AD cohorts. Group differences were assessed by using two-sample t test. Individual classification was analyzed by using receiver operating characteristic (ROC) curves. Compared to controls, significant differences in the intensity-based MTL atrophy measure were detected in both groups of patients (AD vs controls, 0.28 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001; aMCI vs controls, 0.31 +/- 0.03 vs 0.34 +/- 0.03, P < 0.001). Moreover, the subgroup of aMCI converters was significantly different from controls (0.27 +/- 0.034 vs 0.34 +/- 0.03, P < 0.001). Regarding the ROC curve for intergroup discrimination, the area under the curve was 0.863 for AD patients vs controls, 0.746 for all aMCI patients vs controls, and 0.880 for aMCI converters vs controls. With specificity set at 85%, the sensitivity was 74% for AD vs controls, 45% for aMCI vs controls, and 83% for aMCI converters vs controls. The automated analysis of MTL atrophy in the segmented volume is applied to the early assessment of AD, leading to the discrimination of aMCI converters with an average 3 year follow-up. This procedure can provide additional useful information in the early diagnosis of AD.

Quality assessment of particle therapy treatments by means of PET systems has been carried out since late `90 and it is one of the most promising in-vivo non invasive monitoring techniques employed clinically. It can be performed with a diagnostic PET scanners installed outside the treatment room (off-line monitoring) or inside the treatment room (in-room monitoring). However the most efficient way is by integrating a PET scanner with the treatment delivery system (on-line monitoring) so that the biological wash out and the patient repositioning errors are minimized. In this work we present the performance of the in-beam PET scanner developed within the INSIDE project. The INSIDE PET scanner is made of two planar heads, 10 cm wide (transaxially) and 25 cm long (axially), composed of pixellated LFS crystals coupled to Hamamatsu MPPCs. Custom designed Front-End Electronics (FE) and Data AcQuisition (DAQ) systems allow an on-line reconstruction of PET images from separated in-spill and inter-spill data sets. The INSIDE PET scanner has been recently delivered at the CNAO (Pavia, Italy) hadrontherapy facility and the first experimental measurements have been carried out. Homogeneous PMMA phantoms and PMMA phantoms with small air and bone inserts were irradiated with monoenergetic clinical proton beams. The activity range was evaluated at various benchmark positions within the field of view to assess the homogeneity of response of the PET system. Repeated irradiations of PMMA phantoms with clinical spread out Bragg peak proton beams were performed to evaluate the reproducibility of the PET signal. The results found in this work show that the response of the INSIDE PET scanner is independent of the position within the radiation field. Results also show the capability of the INSIDE PET scanner to distinguish variations of the activity range due to small tissue inhomogeneities. Finally, the reproducibility of the activity range measurement was within 1 mm.

Lung cancer is one of the leading causes of death in the world. Early diagnosis is crucial to limit mortality. In order to support radiologists in the diagnosis several Computer-Assisted Detection (CAD) systems were developed

A-hypenuclei are produced and studied, with the FINUDA spectrometer, for the first time at an e(+)e(-) collider: DA Phi NE, the Frascati phi-factory. The slow negative kaons from phi(1020) decay are stopped in thin (0.2 g/cm(2)) nuclear targets, and A-hypemuclei formation is detected by measuring the momentum of the outgoing pi(-). A preliminary analysis on C-12(Lambda) shows X an energy resolution of 1.29 MeV FWHM on the hypernuclear levels, the best obtained so far with magnetic spectrometers at hadron facilities. Capture rates for the ground state and the excited ones are reported, and compared with previous experiments. (c) 2005 Elsevier B.V. All rights reserved

An overview is presented of the characteristics of HEP computing and its mapping to the Grid paradigm. This is followed by a synopsis of the main experiences and lessons learned by HEP experiments in their use of DataGrid middleware using both the EDG application testbed and the LCG production service. Particular reference is made to experiment ‘data challenges’, and a forward look is given to necessary developments in the framework of the EGEE project.

A fully automated and three-dimensional (3D) segmentation method for the identification of the pulmonary parenchyma in thorax X-ray computed tomography (CT) datasets is proposed. It is meant to be used as pre-processing step in the computer-assisted detection (CAD) system for malignant lung nodule detection that is being developed by the Medical Applications in a Grid Infrastructure Connection (MAGIC-5) Project. In this new approach the segmentation of the external airways (trachea and bronchi), is obtained by 3D region growing with wavefront simulation and suitable stop conditions, thus allowing an accurate handling of the hilar region, notoriously difficult to be segmented. Particular attention was also devoted to checking and solving the problem of the apparent ‘fusion’ between the lungs, caused by partial-volume effects, while 3D morphology operations ensure the accurate inclusion of all the nodules (internal, pleural, and vascular) in the segmented volume. The new algorithm was initially developed and tested on a dataset of 130 CT scans from the Italung-CT trial, and was then applied to the ANODE09-competition images (55 scans) and to the LIDC database (84 scans), giving very satisfactory results. In particular, the lung contour was adequately located in 96% of the CT scans, with incorrect segmentation of the external airways in the remaining cases. Segmentation metrics were calculated that quantitatively express the consistency between automatic and manual segmentations: the mean overlap degree of the segmentation masks is 0.96 ± 0.02, and the mean and the maximum distance between the mask borders (averaged on the whole dataset) are 0.74 ± 0.05 and 4.5 ± 1.5, respectively, which confirms that the automatic segmentations quite correctly reproduce the borders traced by the radiologist. Moreover, no tissue containing internal and pleural nodules was removed in the segmentation process, so that this method proved to be fit for the use in the framework of a CAD system. Finally, in the comparison with a two-dimensional segmentation procedure, inter-slice smoothness was calculated, showing that the masks created by the 3D algorithm are significantly smoother than those calculated by the 2D-only procedure.

Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible.

Dosimetry in Hadrontherapy M. Marafini, A. Attili, G. Battistoni, N. Belcari, M.G. Bisogni, N. Camarlinghi, F. Cappucci, M. Cecchetti, P. Cerello, F. Ciciriello , G.A.P. Cirrone, S. Coli, F. Corsi , G. Cuttone, E. De Lucia, S. Ferretti, R. Faccini, E. Fiorina, P.M. Frallicciardi, G. Giraudo, E. Kostara, A. Kraan, F. Licciulli , B. Liu, N. Marino, C. Marzocca , G. Matarrese , C. Morone, M. Morrocchi, S. Muraro, V. Patera, F. Pennazio, C. Peroni, L. Piersanti, M.A. Piliero, G. Pirrone, A. Rivetti, F. Romano, V. Rosso, P. Sala, A. Sarti, A. Sciubba, G. Sportelli, C. Voena, R. Wheadon and A. Del Guerra Museo Storico della Fisica e Centro Studi e Ricerche E. Fermi , Roma, Italy b−f INFN Sezione di: Roma, Roma; Torino, Torino; Milano, Milano; Pisa, Pisa; Bari, Bari; Italy Laboratori Nazionali del Sud dell'INFN, Catania, Italy Laboratori Nazionali di Frascati dell'INFN, Frascati, Italy Dipartimento di Scienze di Base e Applicate per Ingegneria, Sapienza Universita di Roma, Roma, Italy

Multi-slice computed tomography (MSCT) is a valuable tool for lung cancer detection thanks to its ability to identify non-calcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction and false positive (FP) reduction is presented. The selection of ROIs is based on a multi-threshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved a 84% and 71% sensitivity at a false positive/scan rate of 10 and 4 respectively. For nodules having a diameter greater 45 than or equal to 4 mm, the sensitivities were 97% and 80% at a false positive/scan rate of 10 and 4 respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists. 50 Introduction Lung cancer is the leading cause of cancer deaths. The 5-year survival rate is estimated to be only 16% [1]. The survival rate increases up to 49% for cases detected when the disease is still localized; however, only 16% of lung cancers are diagnosed at this early stage [1]. Screening tests for lung cancer focus on trying to detect the disease at an earlier and more curable stage, in particular for high-risk 55 individuals. In the past, chest radiography and sputum cytology were investigated as modalities for lung cancer screening [2,3], showing a limited effectiveness in reducing lung cancer mortality. Lowdose helical computed tomography (CT) has provided promising results in the detection of early-stage lung cancer [4-6]. The large number of images that need to be interpreted by the radiologists in CT screening stimulated the development of various CAD systems for lung nodules [7-22]. Some of the 60 ongoing CT lung screening trials employ multi-slice CT (MSCT) [23,24] which, compared to singleslice helical CT, provides a higher resolution in the axial direction. The availability of tools for a truly three-dimensional data visualization and analysis is particularly important in order to take full advantage of the MSCT isotropic resolution. A complete CAD system for lung nodule detection, structured in four modules, is presented.

The annihilation fractions and cross sections for the n p annihilation reaction in flight (∼50–400 MeV/c) into ηπ+ and η′π+ final states have been measured. The first ones follow the trend expected for P wave annihilations. From the ratios of the yields an evaluation of the pseudoscalar mixing angle in the standard form is possible. The value found shows that for pseudoscalar meson production from P wave the Quark Line Rule is not grossly violated.

The p D and p 4 He total annihilation cross sections have been measured with the Obelix apparatus at LEAR at, respectively, three and two values of the p incident momentum between 70 MeV/c and 36 MeV/c. The values of the p D annihilation cross section, at such low energies, are in agreement with a surprising result obtained in a recent low statistics measurement of the antiprotonic deuterium K α X-rays [M. Augsburger et al., Phys. Lett. B 461 (1999) 417] (see following article), concerning the unexpected narrow width of the antiprotonic deuterium 1s level.

A confirming evidence for two pseudoscalar states with masses M 1 =1405±5, Γ 1 =50±4 MeV/ c 2 and M 2 =1500±10, Γ 2 =100±20 MeV/ c 2 is reported. The second pseudoscalar fills the nonet and leaves the first as an outsider supporting the hypothesis for its exotic nature.

The reaction p p→K + K − π 0 was analysed for antiproton annihilations at rest at three hydrogen target densities. A strong dependence of the p p→φπ 0 yield on the quantum numbers of the initial state is observed. The branching ratio of the φπ0 channel from the 3S1 initial state is more than 15 times larger than the one from the 1P1 state. A large apparent violation of the OZI rule for tensor meson production from p p -annihilations from the P-waves (1+++2++) is observed: Rexp(f′2π0/f2π0)=(149±20)·10−3, significantly exceeding the OZI-rule prediction R=16·10−3.

The study of the I =1, J PC =0 ++ states with the OBELIX detector in the channel p p→K ± K 0 S π ∓ at three different target densities is reported. The data show the evidence for an extra scalar state with mass 1.29±0.01 GeV/ c 2 and width 0.080±0.005 GeV/ c 2 .

The results of the spin-parity analysis of annihilations at very low momentum (≈ 50 MeV/c) are reported. To describe the data the production of the ϱ, f2, a2 and a1 mesons and the presence of the ππ interaction in S-wave (the σ term) in the final state are necessary. The best fit solution requires also the presence of a ϱ′ state of mass and width M = 1.282 ± 0.037, Γ = 0.236 ± 0.036 GeV/c2 and of a heavy pion π(1300) of mass and width M = 1.275 ± 0.015, Γ = 0.218 ± 0.100 GeV/c2. The measured fraction of the annihilation cross section into 2π+2π− is (7.61 ± 0.35) · 10−2.

The study of the K±Kmiss0π∓π+π− channel (7016 events), from p p annihilations in a gaseous hydrogen target at NTP, with the OBELIX spectrometer at LEAR (CERN), is presented. A spin-parity analysis provides evidence for two axial vectors, one in the low K K π mass region, the well established f1(1285), decaying to a0(980)π, the second, the 1++ component of the old puzzling E l , the f1(1420), decaying mainly to K ∗ K , seen for the first time in p p annihilation at rest. The analysis confirms the dominant production of the pseudoscalar η(1405), decaying mainly to (Kπ) S K , and the existence of a second pseudoscalar in the same mass region, the η(1460), decaying mainly to K ∗ K .

The CALMA (Computer Assisted Library for MAmmography) project is a five years plan developed in a physics research frame in collaboration between INFN (Istituto Nazionale di Fisica Nucleare) and many Italian hospitals. At present a large database of digitized mammographic images (more than 6000) was collected and a software based on neural network algorithms for the search of suspicious breast lesions was developed. Two tools are available: a microcalcification clusters hunter, based on supervised and unsupervised feedforward neural network, and a massive lesions searcher, based on a hibrid approach. Both the algorithms analyzed preprocessed digitized images by high frequency filters. Clinical tests were performed to evaluate sensitivity and specificity of the system, considering the system as alone and as secon reader. Results show that the system is ready to be implemented by medical industry. The CALMA project, just ended, has its natural development in the GPCALMA (Grid Platform for CALMA) project, where distributed users join common resources (images, tools, statistical analysis)., Comment: 3 pages, 1 figure; to appear in the Proceedings of Frontier Detectors For Frontier Physics, 9th Pisa Meeting on Advanced Detectors, 25-31 May 2003, La Biodola, Isola d'Elba, Italy