The first step for neuroimaging data analysis: DICOM to NIfTI conversion

BACKGROUND: Clinical imaging data are typically stored and transferred in the DICOM format, whereas the NIfTI format has been widely adopted by scientists in the neuroimaging community. Therefore, a vital initial step in processing the data is to convert images from the complicated DICOM format to the much simpler NIfTI format. While there are a number of tools that usually handle DICOM to NIfTI conversion seamlessly, some variations can disrupt this process. NEW METHOD: We provide some insight into the challenges faced with image conversion. First, different manufacturers implement the DICOM format differently which complicates the conversion. Second, different modalities and sub-modalities may need special treatment during conversion. Lastly, the image transferring and archiving can also impact the DICOM conversion. RESULTS: We present results in several error-prone domains, including the slice order for functional imaging, phase encoding direction for distortion correction, effect of diffusion gradient direction, and effect of gantry correction for some imaging modality. COMPARISON WITH EXISTING METHODS: Conversion tools are often designed for a specific manufacturer or modality. The tools and insight we present here are aimed at different manufacturers or modalities. CONCLUSIONS: The imaging conversion is complicated by the variation of images. An understanding of the conversion basics can be helpful for identifying the source of the error. Here we provide users with simple methods for detecting and correcting problems. This also serves as an overview for developers who wish to either develop their own tools or adapt the open source tools created by the authors

Dosimétrie clinique en radiothérapie moléculaire

La radiothérapie moléculaire (RTM) est une radiothérapie systémique, où le produit radiopharmaceutique se lie spécifiquement sur les tumeurs pour détruire sélectivement les cibles cancéreuses tout en préservant les organes sains. Lutathera® (177Lu-DOTATATE) est un radiopharmaceutique récemment approuvé par la FDA/EMA pour le traitement des tumeurs neuroendocrines gastro-entéro-pancréatiques (GEP-NETs). Dans la pratique clinique, les patients reçoivent une activité fixe de Lutathera®, 4 cycles de 7,4 GBq, en supposant que la pharmacocinétique du radiopharmaceutique est même entre les patients. La dosimétrie spécifique au patient permet un changement de paradigme majeur dans l'administration de la RTM, passant d'une approche "taille unique" à une véritable médecine personnalisée où l'activité administrée est évaluée spécifiquement sur la base de l'irradiation délivrée à chaque patient. Pour ce faire, il faut généralement déterminer la distribution spatiale du radiopharmaceutique dans les organes par imagerie à différents moments (imagerie quantitative), estimer le nombre total de désintégrations radioactives en intégrant l'activité dans le temps (évaluation pharmacocinétique) et calculer la dose absorbée à partir des caractéristiques physiques du radionucléide et du transport de l'énergie dans les tissus du patient. Actuellement, il n'existe pas de procédures normalisées pour effectuer la dosimétrie clinique. En outre, l'évaluation des incertitudes associées à la procédure de dosimétrie n'est pas triviale. Le projet DosiTest a été lancé pour évaluer les incertitudes associées à chacune des étapes du flux de travail de la dosimétrie clinique, via une inter-comparaison multicentrique basée sur la modélisation de Monte Carlo (MC). La première phase de la thèse a consisté à comparer les analyses dosimétriques effectuées par différents centres utilisant le même logiciel et le même protocole sur le même ensemble de données de patients dans le cadre du projet IAEA-CRP E23005 afin d'évaluer la précision de la dosimétrie clinique. À notre connaissance, c'est la première fois qu'une comparaison dosimétrique multicentrique d'un seul ensemble de données cliniques sur un patient a été entreprise en utilisant le même protocole et le même logiciel par de nombreux centres dans le monde entier. Elle a mis en évidence le besoin crucial d'établir des points de contrôle et d'effectuer des vérifications de bon sens pour éliminer les disparités significatives entre les résultats et distinguer les pratiques erronées de la variabilité inter-opérateurs acceptable. Un résultat important de ce travail a été le manque d'assurance qualité en dosimétrie de médecine nucléaire clinique et la nécessité de développer des procédures de contrôle qualité. Alors que la dosimétrie gagne en popularité en médecine nucléaire, les meilleures pratiques doivent être adoptées pour garantir la fiabilité, la traçabilité et la reproductibilité des résultats. Cela met également en avant la nécessité de dispenser une formation suffisante après l'acquisition des progiciels relativement nouveaux, au-delà de quelques jours. Ceci est clairement insuffisant dans le contexte d'un domaine émergent où l'expérience professionnelle fait souvent défaut. Ensuite, l'étude de l'exactitude de la dosimétrie clinique nécessite de générer des ensembles de données de test, afin de définir la vérité de base par rapport à laquelle les procédures de dosimétrie clinique peuvent être comparées. La deuxième section de la thèse traite de la simulation de l'imagerie TEMP scintigraphique tridimensionnelle en implémentant le mouvement du détecteur d'auto-contournement dans la boîte à outils Monte Carlo GATE. Après la validation des projections TEMP/TDM sur des modèles anthropomorphes, une série d'images réalistes de patients cliniques a été générée. La dernière partie de la thèse a établi la preuve de concept du projet DosiTest, en utilisant un ensemble de données TEMP/TDM virtuelles (simulées) à différents moments, avec différentes gamma-caméras, permettant de comparer différentes techniques dosimétriques et d'évaluer la faisabilité clinique du projet dans certains départements de médecine nucléaire.Molecular radiotherapy (MRT) is a systemic radiotherapy where the radiopharmaceutical binds specifically to tumours to selectively destroy cancer targets while sparing healthy organs. Lutathera® (177Lu-DOTATATE) is a radiopharmaceutical that was recently FDA/EMA approved for the treatment of the GastroEnteroPancreatic NeuroEndocrine Tumours (GEP-NETs). In clinical practice, patients are administered with a fixed activity of Lutathera®, assuming that radiopharmaceutical distribution is the same for all patients. Patient-specific dosimetry allows for a major paradigm shift in the administration of MRT from "one-size-fits-all" approach, to real personalised medicine where administered activity is assessed specifically on the base of the irradiation delivered to each patient. This usually requires determining the spatial distribution of the radiopharmaceutical in various organs via imaging at different times (quantitative imaging), estimating the total number of radioactive decays by integrating activity over time (pharmacokinetic assessment) and calculating the absorbed dose using the physical characteristics of the radionuclide and implementing radiation transport in patient's tissues. Currently, there are no standardised procedures to perform clinical dosimetry. In addition, the assessment of the uncertainties associated with the dosimetry procedure is not trivial. The DosiTest project (http://www.dositest.org/) was initiated to evaluate uncertainties associated with each of the steps of the clinical dosimetry workflow, via a multicentric inter-comparison based on Monte Carlo (MC) modelling. The first phase of the thesis compared dosimetry analysis performed by various centres using the same software and protocol on the same patient dataset as a part of IAEA-CRP E23005 project in order to appraise the precision of clinical dosimetry. To our knowledge, this is the first time that a multi-centric dosimetry comparison of a single clinical patient dataset has been undertaken using the same protocol and software by many centres worldwide. It highlighted the critical need to establish checkpoints and conduct sanity checks to eliminate significant disparities among results, and distinguish erroneous practice with acceptable inter-operator variability. A significant outcome of this work was the lack of quality assurance in clinical nuclear medicine dosimetry and the need for the development of quality control procedures. While dosimetry is gaining popularity in nuclear medicine, best practices should be adopted to ensure that results are reliable, traceable, and reproducible. It also brings forward the need to deliver sufficient training after the acquisition of the relatively new software packages beyond a couple of days. This is clearly insufficient in a context of an emerging field where the professional experience is quite often lacking. Next, the study of clinical dosimetry accuracy requires generating test datasets, to define the ground truth against which clinical dosimetry procedures can be benchmarked. The second section of the thesis addressed the simulation of three-dimensional scintigraphic SPECT imaging by implementing auto-contouring detector motion in the GATE Monte Carlo toolkit. Following the validation of SPECT/CT projections on anthropomorphic models, a series of realistic clinical patient images were generated. The last part of the thesis established the proof of concept of the DosiTest project, using a virtual (simulated) SPECT/CT dataset at various time points, with various gamma cameras, enabling comparison of various dosimetric techniques and to assess the clinical feasibility of the project in selected nuclear medicine departments

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Tools for conversion, analysis, and annotation of histology images

Research presented in this thesis is aimed at making significant contributions in the field of computational pathology through the development of TIAToolbox, an open-source comprehensive Python library for computational pathology image analysis. The toolbox provides functionalities for tasks such as WSI reading, patch extraction, model inference, and visualization, with pre-trained models available for patch classification, semantic segmentation, and nucleus instance segmentation and classification. Additionally, the thesis explores the utility of alternative representations, including self-supervised and pooled multi-level wavelet representations, for histology image analysis. Moreover, the research addresses the challenges in annotation storage and querying, with the development of efficient storage systems capable of handling millions of annotations. These tools and techniques contribute to advancing computational pathology and provide valuable resources for future research, enabling more accurate and efficient analysis of histopathology images, thus having the potential to improve outcomes for patients

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI