Pattern recognition methods applied to medical imaging: lung nodule detection in computed tomography images

Lung cancer is one of the main public health issues in developed countries. The overall 5-year survival rate is only 10−16%, although the mortality rate among men in the United States has started to decrease by about 1.5% per year since 1991 and a similar trend for the male population has been observed in most European countries. By contrast, in the case of the female population, the survival rate is still decreasing, despite a decline in the mortality of young women has been ob- served over the last decade. Approximately 70% of lung cancers are diagnosed at too advanced stages for the treatments to be effective. The five-year survival rate for early-stage lung cancers (stage I), which can reach 70%, is sensibly higher than for cancers diagnosed at more advanced stages. Lung cancer most commonly manifests itself as non-calcified pulmonary nodules. The CT has been shown as the most sensitive imaging modality for the detection of small pulmonary nodules, particularly since the introduction of the multi-detector-row and helical CT technologies. Screening programs based on Low Dose Computed Tomography (LDCT) may be regarded as a promising technique for detecting small, early-stage lung cancers. The efficacy of screening programs based on CT in reducing the mortality rate for lung cancer has not been fully demonstrated yet, and different and opposing opinions are being pointed out on this topic by many experts. However, the recent results obtained by the National Lung Screening Trial (NLST), involving 53454 high risk patients, show a 20% reduction of mortality when the screening program was carried out with the helical CT, rather than with a conventional chest X-ray. LDCT settings are currently recommended by the screening trial protocols. However, it is not trivial in this case to identify small pulmonary nodules,due to the noisier appearance of the images in low-dose CT with respect to the standard-dose CT. Moreover, thin slices are generally used in screening programs, thus originating datasets of about 300 − 400 slices per study. De- pending on the screening trial protocol they joined, radiologists can be asked to identify even very small lung nodules, which is a very difficult and time- consuming task. Lung nodules are rather spherical objects, characterized by very low CT values and/or low contrast. Nodules may have CT values in the same range of those of blood vessels, airway walls, pleura and may be strongly connected to them. It has been demonstrated, that a large percent- age of nodules (20 − 35%) is actually missed in screening diagnoses. To support radiologists in the identification of early-stage pathological objects, about one decade ago, researchers started to develop CAD methods to be applied to CT examinations. Within this framework, two CAD sub-systems are proposed: CAD for internal nodules (CADI), devoted to the identification of small nodules embedded in the lung parenchyma, i.e. Internal Nodules (INs) and CADJP, devoted the identification of nodules originating on the pleura surface, i.e. Juxta-Pleural Nodules (JPNs) respectively. As the training and validation sets may drastically influence the performance of a CAD system, the presented approaches have been trained, developed and tested on different datasets of CT scans (Lung Image Database Consortium (LIDC), ITALUNG − CT) and finally blindly validated on the ANODE09 dataset. The two CAD sub-systems are implemented in the ITK framework, an open source C++ framework for segmentation and registration of medical im- ages, and the rendering of the obtained results are achieved using VTK, a freely available software system for 3D computer graphics, image processing and visualization. The Support Vector Machines (SVMs) are implemented in SVMLight. The two proposed approaches have been developed to detect solid nodules, since the number of Ground Glass Opacity (GGO) contained in the available datasets has been considered too low. This thesis is structured as follows: in the first chapter the basic concepts about CT and lung anatomy are explained. The second chapter deals with CAD systems and their evaluation methods. In the third chapter the datasets used for this work are described. In chapter 4 the lung segmentation algorithm is explained in details, and in chapter 5 and 6 the algorithms to detect internal and juxta-pleural candidates are discussed. In chapter 7 the reduction of false positives findings is explained. In chapter 8 results of the train and validation sessions are shown. Finally in the last chapter the conclusions are drawn

A Computer-Aided Detection system for lung nodules in CT images

Lung cancer is the leading cause of cancer-related mortality in developed countries. To support radiologists in the identification of early-stage lung cancers, we propose a Computer-Aided Detection (CAD) system, composed by two different procedures: VBNACADI devoted to the identification of small nodules embedded in the lung parenchyma (internal nodules) and VBNACADJP devoted the identification of nodules originating on the pleura surface (juxta-pleural nodules). The CAD system has been developed and tested on a dataset of low-dose and thin-slice CT scans collected in the framework of the first Italian randomized and controlled screening trial (ITALUNG-CT). This work has been carried out in the framework of MAGIC-5 (Medical Application on a Grid Infrastructure Connection), an Italian collaboration funded by Istituto Nazionale di Fisica Nucleare (INFN) and Ministero dell’Universit`a e della Ricerca (MIUR), which aims at developing models and algorithms for a distributed analysis of biomedical images, by making use of the GRID services

Evaluation of algorithms for photon depth of interaction estimation for the TRIMAGE PET component

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

Analysis of time-profiles with in-beam PET monitoring in charged particle therapy

Background: Treatment verification with PET imaging in charged particle therapy is conventionally done by comparing measurements of spatial distributions with Monte Carlo (MC) predictions. However, decay curves can provide additional independent information about the treatment and the irradiated tissue. Most studies performed so far focus on long time intervals. Here we investigate the reliability of MC predictions of space and time (decay rate) profiles shortly after irradiation, and we show how the decay rates can give an indication about the elements of which the phantom is made up. Methods and Materials: Various phantoms were irradiated in clinical and near-clinical conditions at the Cyclotron Centre of the Bronowice proton therapy centre. PET data were acquired with a planar 16x16 cm$^2$ PET system. MC simulations of particle interactions and photon propagation in the phantoms were performed using the FLUKA code. The analysis included a comparison between experimental data and MC simulations of space and time profiles, as well as a fitting procedure to obtain the various isotope contributions in the phantoms. Results and conclusions: There was a good agreement between data and MC predictions in 1-dimensional space and decay rate distributions. The fractions of $^{11}$C, $^{15}$O and $^{10}$C that were obtained by fitting the decay rates with multiple simple exponentials generally agreed well with the MC expectations. We found a small excess of $^{10}$C in data compared to what was predicted in MC, which was clear especially in the PE phantom.Comment: 9 pages, 5 figures, 1 table. Proceedings of the 20th International Workshop on Radiation Imaging Detectors (iWorid2018), 24-28 June 2018, Sundsvall, Swede

First full-beam PET acquisitions in proton therapy with a modular dual-head dedicated system

During particle therapy irradiation, positron emitters with half-lives ranging from 2 to 20 min are generated from nuclear processes. The half-lives are such that it is possible either to detect the positron signal in the treatment room using an in-beam positron emission tomography (PET) system, right after the irradiation, or to quickly transfer the patient to a close PET/CT scanner. Since the activity distribution is spatially correlated with the dose, it is possible to use PET imaging as an indirect method to assure the quality of the dose delivery. In this work, we present a new dedicated PET system able to operate in-beam. The PET apparatus consists in two 10 cm × 10 cm detector heads. Each detector is composed of four scintillating matrices of 23 × 23 LYSO crystals. The crystal size is 1.9 mm × 1.9 mm × 16 mm. Each scintillation matrix is read out independently with a modularized acquisition system. The distance between the two opposing detector heads was set to 20 cm. The system has very low dead time per detector area and a 3 ns coincidence window, which is capable to sustain high single count rates and to keep the random counts relatively low. This allows a new full-beam monitoring modality that includes data acquisition also while the beam is on. The PET system was tested during the irradiation at the CATANA (INFN, Catania, Italy) cyclotron-based proton therapy facility. Four acquisitions with different doses and dose rates were analysed. In all cases the random to total coincidences ratio was equal or less than 25%. For each measurement we estimated the accuracy and precision of the activity range on a set of voxel lines within an irradiated PMMA phantom. Results show that the inclusion of data acquired during the irradiation, referred to as beam-on data, improves both the precision and accuracy of the range measurement with respect to data acquired only after irradiation. Beam-on data alone are enough to give precisions better than 1 mm when at least 5 Gy are delivered

Proton range monitoring with in-beam PET: Monte Carlo activity predictions and comparison with cyclotron data

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First tests for an online treatment monitoring system with in-beam PET for proton therapy

PET imaging is a non-invasive technique for particle range verification in proton therapy. It is based on measuring the beta+ annihilations caused by nuclear interactions of the protons in the patient. In this work we present measurements for proton range verification in phantoms, performed at the CNAO particle therapy treatment center in Pavia, Italy, with our 10 x 10 cm^2 planar PET prototype DoPET. PMMA phantoms were irradiated with mono-energetic proton beams and clinical treatment plans, and PET data were acquired during and shortly after proton irradiation. We created 1-D profiles of the beta+ activity along the proton beam-axis, and evaluated the difference between the proximal rise and the distal fall-off position of the activity distribution. A good agreement with FLUKA Monte Carlo predictions was obtained. We also assessed the system response when the PMMA phantom contained an air cavity. The system was able to detect these cavities quickly after irradiation.Comment: 11 pages, 6 figures, Proceedings for International Workshop on Radiation Imaging Detectors, 201

Online monitoring for proton therapy: A real-time procedure using a planar PET system

In this study a procedure for range verification in proton therapy by means of a planar in-beam PET system is presented. The procedure consists of two steps: the measurement of the β+-activity induced in the irradiated body by the proton beam and the comparison of these distributions with simulations. The experimental data taking was performed at the CNAO center in Pavia, Italy, irradiating plastic phantoms. For two different cases we demonstrate how a real-time feedback of the delivered treatment plan can be obtained with in-beam PET imaging