763 research outputs found

    Multimodal MRI analysis using deep learning methods

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    Magnetic resonance imaging (MRI) has been widely used in scientific and clinical research. It is a non-invasive medical imaging technique that reveals anatomical structures and provides useful information for investigators to explore aging and pathological processes. Different MR modalities offer different useful properties. Automatic MRI analysis algorithms have been developed to address problems in many applications such as classification, segmentation, and disease diagnosis. Segmentation and labeling algorithms applied to brain MRIs enable evaluations of the volumetric changes of specific structures in neurodegenerative diseases. Reconstruction of fiber orientations using diffusion MRI is beneficial to obtain better understanding of the underlying structures. In this thesis, we focused on development of deep learning methods for MRI analysis using different image modalities. Specifically, we applied deep learning techniques on different applications, including segmentation of brain structures and reconstruction of tongue muscle fiber orientations. For segmentation of brain structures, we developed an end-to-end deep learning algorithm for ventricle parcellation of brains with ventriculomegaly using T1-w MR images. The deep network provides robust and accurate segmentation results in subjects with high variability in ventricle shapes and sizes. We developed another deep learning method to automatically parcellate the thalamus into a set of thalamic nuclei using T1-w MRI and features from diffusion MRI. The algorithm incorporates a harmonization step to make the network adapt to input images with different contrasts. We also studied the strains associated with tongue muscles during speech production using multiple MRI modalities. To enable this study, we first developed a deep network to reconstruct crossing tongue muscle fiber orientations using diffusion MRI. The network was specifically designed for the human tongue and accounted for the orthogonality property of the tongue muscles. Next, we proposed a comprehensive pipeline to analyze the strains associated with tongue muscle fiber orientations during speech using diffusion MRI, and tagged and cine MRI. The proposed pipeline provides a solution to analyze the cooperation between muscle groups during speech production

    MR in vivo tractography for the reconstruction of cranial nerves course

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    Aim The aim of my Ph.D. was to implement a diffusion tensor tractography (DTT) pipeline to reconstruct cranial nerve I (olfactory) to study COVID-19 patients, and anterior optic pathway (AOP, including optic nerve, chiasm, and optic tract) to study patients with sellar/parasellar tumors, and with Leber’s Hereditary Optic Neuropathy (LHON). Methods We recruited 23 patients with olfactory dysfunction after COVID-19 infection (mean age 37±14 years, 12 females); 27 patients with sellar/parasellar tumors displacing the optic chiasm eligible for endonasal endoscopic surgery (mean age 53. ±16.4 years, 13 female) and 6 LHON patients (mutation 11778/MT-ND4, mean age 24.9±15.7 years). Sex- and age-matched healthy control were also recruited. In LHON patients, optical coherence tomography (OCT) was performed. Acquisitions were performed on a clinical high field 3-T MRI scanner, using a multi-shell HARDI (High Angular Resolution Diffusion Imaging) sequence (b-values 0-300-1000-2000 s/mm2, 64 maximum gradient directions, 2mm3 isotropic voxel). DTT was performed with a multi-tissue spherical deconvolution approach and mean diffusivity (MD) DTT metrics were compared with healthy controls using an unpaired t-test. Correlations of DTT metrics with clinical data were sought by regression analysis. Results In all 23 hypo/anosmic patients with previous COVID-19 infection the CN I was successfully reconstructed with no DTT metrics alterations, thus suggesting the pathogenetic role of central olfactory cortical system dysfunction. In all 27 patients with sellar/parasellar tumors the AOP was reconstructed, and in 11/13 (84.7%) undergoing endonasal endoscopic surgery the anatomical fidelity of the reconstruction was confirmed; a significant decrease in MD within the chiasma (p<0.0001) was also found. In LHON patients a reduction of MD in the AOP was significantly associated with OCT parameters (p=0.036). Conclusions Multi-shell HARDI diffusion-weighted MRI followed by multi-tissue spherical deconvolution for the DTT reconstruction of the CN I and AOP has been implemented, and its utility demonstrated in clinical practice

    Surgical management of Glioma Grade 4: technical update from the neuro-oncology section of the Italian Society of Neurosurgery (SINch®): a systematic review

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    Purpose: The extent of resection (EOR) is an independent prognostic factor for overall survival (OS) in adult patients with Glioma Grade 4 (GG4). The aim of the neuro-oncology section of the Italian Society of Neurosurgery (SINch®) was to provide a general overview of the current trends and technical tools to reach this goal. Methods: A systematic review was performed. The results were divided and ordered, by an expert team of surgeons, to assess the Class of Evidence (CE) and Strength of Recommendation (SR) of perioperative drugs management, imaging, surgery, intraoperative imaging, estimation&nbsp;of EOR, surgery at tumor progression and surgery in elderly patients. Results: A total of 352 studies were identified, including 299 retrospective studies and 53 reviews/meta-analysis. The use of Dexamethasone and the avoidance of prophylaxis with anti-seizure medications reached a CE I and SR A. A preoperative imaging standard protocol was defined with CE II and SR B and usefulness of an early postoperative MRI, with CE II and SR B. The EOR was defined the strongest independent risk factor for both OS and tumor recurrence with CE II and SR B. For intraoperative imaging only the use of 5-ALA reached a CE II and SR B. The estimation of EOR was established to be fundamental in planning postoperative adjuvant treatments with CE II and SR B and the stereotactic image-guided brain biopsy to be the procedure of choice when an extensive surgical resection is not feasible (CE II and SR B). Conclusions: A growing number of evidences evidence support the role of maximal safe resection as primary OS predictor in GG4 patients. The ongoing development of intraoperative techniques for a precise real-time identification of peritumoral functional pathways enables surgeons to maximize EOR minimizing the post-operative morbidity

    Advanced intraoperative MRI in pediatric brain tumor surgery

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    Introduction: In the pediatric brain tumor surgery setting, intraoperative MRI (ioMRI) provides “real-time” imaging, allowing for evaluation of the extent of resection and detection of complications. The use of advanced MRI sequences could potentially provide additional physiological information that may aid in the preservation of healthy brain regions. This review aims to determine the added value of advanced imaging in ioMRI for pediatric brain tumor surgery compared to conventional imaging.Methods: Our systematic literature search identified relevant articles on PubMed using keywords associated with pediatrics, ioMRI, and brain tumors. The literature search was extended using the snowball technique to gather more information on advanced MRI techniques, their technical background, their use in adult ioMRI, and their use in routine pediatric brain tumor care.Results: The available literature was sparse and demonstrated that advanced sequences were used to reconstruct fibers to prevent damage to important structures, provide information on relative cerebral blood flow or abnormal metabolites, or to indicate the onset of hemorrhage or ischemic infarcts. The explorative literature search revealed developments within each advanced MRI field, such as multi-shell diffusion MRI, arterial spin labeling, and amide-proton transfer-weighted imaging, that have been studied in adult ioMRI but have not yet been applied in pediatrics. These techniques could have the potential to provide more accurate fiber tractography, information on intraoperative cerebral perfusion, and to match gadolinium-based T1w images without using a contrast agent.Conclusion: The potential added value of advanced MRI in the intraoperative setting for pediatric brain tumors is to prevent damage to important structures, to provide additional physiological or metabolic information, or to indicate the onset of postoperative changes. Current developments within various advanced ioMRI sequences are promising with regard to providing in-depth tissue information

    Aiding the conservation of two wooden Buddhist sculptures with 3D imaging and spectroscopic techniques

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    The conservation of Buddhist sculptures that were transferred to Europe at some point during their lifetime raises numerous questions: while these objects historically served a religious, devotional purpose, many of them currently belong to museums or private collections, where they are detached from their original context and often adapted to western taste. A scientific study was carried out to address questions from Museo d'Arte Orientale of Turin curators in terms of whether these artifacts might be forgeries or replicas, and how they may have transformed over time. Several analytical techniques were used for materials identification and to study the production technique, ultimately aiming to discriminate the original materials from those added within later interventions

    Neuroimaging methods for analysing connectivity in the presence of white matter lesions

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    White Matter Hyperintensities (WMHs) are often observed in the MRI scans of the ageing brain. Previous studies show that the WMH load correlates with cognitive decline, as well as with an increased risk of stroke and dementia. Most of the studies use a global WMH load across the whole brain as the only metric. This thesis proposes new insight into such data by introducing a methodology to analyse the WMH impact on the structural and functional brain connectivity in a localised manner, using white matter tracts. The thesis also supports other studies by exploring the potential impact of WMHs presence on tractography modelling, as well as adapts the method to Multiple Sclerosis (MS) lesion data. In the first part of the thesis, we presented and evaluated two possible measures of the WMHs impact on the WM tracts from the structural perspective. We showed that despite the different distributions of the two measures, both show a similar relationship with the functional connectivity measure. We explored several possible options for quantifying the resting state functional connectivity between the endpoints of the WM tract in the presence of WMHs. We showed that the choice of connectivity measure (full or partial correlation), as well as gray matter parcellation, made a substantial difference in the results. We also observed variability in the results among the tracts and compared our findings to the results from the literature. The majority of this work depends on the correct definition of the white matter tracts. Therefore, the second part of the thesis focuses on evaluating the impact of the presence of WMHs on tractography modelling. Despite changes in the microstructural parameters within the WMHs and their proximity, we found no meaningful alterations in the shape of the WM tracts in the presence of WMHs on the tract. Finally, we explored the possibility to apply some of this methodology to data from patients with Multiple Sclerosis, which is the most common cause of neurological disability in young adults. MS lesions have different aetiology to the WMHs but may have a similar appearance. We showed that directly applying the method between those two conditions may not be possible, and we proposed an alternative approach, suited to the MS data

    Integer and Fractional Charge Transfer in the Doping of Poly- and Oligothiophenes

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    In the p-doping of organic semiconductors with small molecular dopants integer-charge transfer forming ion-pairs (IPAs) and fractional charge transfer through the formation of ground state charge-transfer complexes (CPXs) have been identified as competing fundamental processes. IPAs and CPXs differently affect the performance of doped organic electronic devices, however, the conditions leading to either phenomenon are still to be fully understood. This thesis focuses on the conjugated polymer poly(3-hexylthiophene) (P3HT) p-doped with the strong molecular electron acceptor tetrafluorotetracyanoquinodimethane (F4TCNQ) and its derivatives of lower electron affinity (F2TCNQ, FTCNQ, TCNQ). Under consideration of their different dopant strengths, the role of the critical dopant concentration promoting the one phenomenon over the other is investigated. Cyclic voltammetry is used to determine ionization energy and electron affinity values of the materials involved to gauge their influence on IPA and CPX occurrence, as identified through optical and vibrational spectroscopy. Supported by electrostatic modeling taking into account the width of the Gaussian density of states (DOS) related to the highest occupied molecular orbital in P3HT, DOS broadening upon doping is considered to explain IPA formation with weaker dopants. Grazing incidence x-ray diffraction is employed toassess the interplay between the supramolecular structure and the two doping phenomena, supporting the hypothesis that a CPX polymorph can occurs that effectively prevents IPA formation for a given host-dopant stoichiometry. Conductivity data on doped films highlights the application-related impacts of these findings. Finally, for a series of custom thiophene oligomers of different lengths, instead of P3HT, the common observation of CPX formation being promoted in the molecular doping of (small) conjugated molecules is investigated. The threshold of transition into the doping phenomenology of the polymer limit is observed at a chain length of 10 thiophene units - a parameter to be considered when employing oligothiophene semiconductors in applications demanding molecular doping. Overall, due the multi-technique approach targeting doping phenomena and mechanisms of a prototypical polymer and oligomer equivalents doped with a systematic series of p-dopants, the database presented here provides a consistent and coherent point of reference for assessing the performance and phenomenology encountered with novel dopants

    EXplainable Artificial Intelligence: enabling AI in neurosciences and beyond

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    The adoption of AI models in medicine and neurosciences has the potential to play a significant role not only in bringing scientific advancements but also in clinical decision-making. However, concerns mounts due to the eventual biases AI could have which could result in far-reaching consequences particularly in a critical field like biomedicine. It is challenging to achieve usable intelligence because not only it is fundamental to learn from prior data, extract knowledge and guarantee generalization capabilities, but also to disentangle the underlying explanatory factors in order to deeply understand the variables leading to the final decisions. There hence has been a call for approaches to open the AI `black box' to increase trust and reliability on the decision-making capabilities of AI algorithms. Such approaches are commonly referred to as XAI and are starting to be applied in medical fields even if not yet fully exploited. With this thesis we aim at contributing to enabling the use of AI in medicine and neurosciences by taking two fundamental steps: (i) practically pervade AI models with XAI (ii) Strongly validate XAI models. The first step was achieved on one hand by focusing on XAI taxonomy and proposing some guidelines specific for the AI and XAI applications in the neuroscience domain. On the other hand, we faced concrete issues proposing XAI solutions to decode the brain modulations in neurodegeneration relying on the morphological, microstructural and functional changes occurring at different disease stages as well as their connections with the genotype substrate. The second step was as well achieved by firstly defining four attributes related to XAI validation, namely stability, consistency, understandability and plausibility. Each attribute refers to a different aspect of XAI ranging from the assessment of explanations stability across different XAI methods, or highly collinear inputs, to the alignment of the obtained explanations with the state-of-the-art literature. We then proposed different validation techniques aiming at practically fulfilling such requirements. With this thesis, we contributed to the advancement of the research into XAI aiming at increasing awareness and critical use of AI methods opening the way to real-life applications enabling the development of personalized medicine and treatment by taking a data-driven and objective approach to healthcare
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