ECCENTRIC: a fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR

A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed and implemented on 7 Tesla human MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method optimized for random undersampling of magnetic resonance spectroscopic imaging (MRSI) at ultra-high field. The approach provides flexible and random (k,t) sampling without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC needs low gradient amplitudes and slew-rates that reduces electrical, mechanical and thermal stress of the scanner hardware, and is robust to timing imperfection and eddy-current delays. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole-brain at 2-3mm isotropic resolution in 4-10 minutes with high signal-to-noise ratio. In 20 healthy volunteers and 20 glioma patients ECCENTRIC demonstrated unprecedented mapping of fine structural details of metabolism in healthy brains and an extended metabolic fingerprinting of glioma tumors.Comment: 20 pages, 7 figures,2 tables, 10 pages supplementary materia

Prostate MRI radiomics for prediction of gleason score

Tese de Mestrado, Bioinformática e Biologia Computacional, 2021, Universidade de Lisboa, Faculdade de CiênciasO cancro da próstata é um dos cancros mais prevalentes em Portugal, estando entre as 4 principais causas de morte por neoplasias em 2018, com uma taxa bruta de mortalidade de 38.23 mortes por 100 000 homens. O atual diagnóstico e classificação do cancro da próstata não é ideal, baseando­se em medidas pouco específicas como os níveis de PSA e DRE, seguidos de biópsia, onde é atribuído um nível de agressivi dade sob a forma da classificação de Gleason. Foi demonstrado no passado que o exame de ressonância magnética multiparamétrica é útil na deteção de lesões de cancro da próstata. No entanto, a interpretação deste exame, sendo um processo subjetivo, está inevitavelmente afetada por uma elevada taxa de variabil idade entre observadores. Foi demonstrado também que a classificação de Gleason atribuída a uma lesão aquando da biópsia, irá provavelmente ser corrigida após prostatectomia radical. Portanto, um método confiável e de preferência não invasivo para classificação do cancro da próstata é necessário. Com este objetivo, esforços têm sido feitos no passado para usar radiómica e aprendizagem automática para prever a classificação de Gleason a partir de imagens clínicas, apresentando resultados promissores. Radiómica é a transformação de imagens médicas em dados quantitativos de alta dimensão. Assim, com base na hipótese de que as características do tumor que são causa ou consequência da classificação de Gleason estão refletidas nas variáveis radiómicas extraídas da imagem de ressonância magnética, estas podem ser usadas para construir modelos de aprendizagem automática capazes de avaliar este parâmetro. Dito isso, o objetivo principal deste trabalho foi desenvolver modelos de aprendizagem automática explorando var iáveis radiómicas extraídas de exames de ressonância magnética para prever a agressividade biológica na forma de classificação de Gleason. Neste trabalho, 288 modelos foram desenvolvidos, correspondendo a diferentes combinações de aspetos de uma pipeline típica, mais especificamente, origem dos dados de treino, estratégia de pre processamento dos dados, método de seleção de variáveis e algoritmo de aprendizagem automática. Num conjunto de 281 lesões (210 para treino, 71 para validação) e 183 pacientes (137 para treino, 46 para vali dação), verificou­se que as variáveis radiómicas extraídas do VOI da glândula inteira produziram modelos extremamente mais confiáveis do que as variáveis radiómicas extraídas dos VOIs das lesões. Sugerindo que as áreas em volta das lesões tumorais oferecem informações relevantes sobre a classificação de Glea son que é atribuída a essa lesão. Além de sugerir que o trabalho monótono de segmentação das lesões realizado pelo radiologista pode não ser necessário ou mesmo prejudicar a assinatura radiómica.Prostate cancer is one of the most prevalent cancers in Portugal, being among the top 4 malignant neo plasm causes of death in 2018, with a crude mortality rate of 38.23 deaths per 100 000 males. Prostate cancer diagnosis and classification is not ideal, relying on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is attributed in the form of Gleason score. Multiparametric MRI has proven to be useful in the detection of prostate cancer. However, it is unavoidably affected by a high rate of inter­reader variability. It has also been shown that the Gleason score attributed to a lesion after biopsy is likely to change after radical prostatectomy. Therefore, a reliable, and preferably non­invasive, method for classification of PCa is in urgent de mand. With this goal in mind, efforts have been made in the past to use computer­aided diagnosis (CAD) coupled with radiomics and machine learning to predict Gleason score from clinical images, showing promising results. Radiomics is the transformation of medical images into high dimension mineable data. Hence, based on the hypothesis that tumour characteristics that are cause or consequence of Gleason score are reflected in the radiomic features extracted from the MRI image, these can be used to build supervised machine learning models capable of assessing this parameter. That being said, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from mpMRI exam inations, to predict biological aggressiveness in the form of Gleason Score. In this work, 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data (i.e. lesion features vs whole gland features), sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions (210 for training, 71 for validation) and 183 patients (137 for training, 46 for validation), it was found that radiomic features extracted from the whole gland VOI produced extremely more reliable classifiers than radiomic features extracted from the lesions’ VOIs. Suggesting that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion. In addition to suggesting that the monotonous lesion segmentation work performed by radiologists may not be necessary or even be harming to the radiomics signature

Transcompartmental Sodium Imaging in Brain Cancer

According to the World Health Organization (WHO), cancers are the second-leading cause of death in the United States, next to cardiovascular disease. Although treatments of some solid tumors with high mortality are showing promise, there is one exception: glioblastoma multiforme (GBM). Even with advanced therapies, median survival rate for GBM patients is dismal because most are not diagnosed early enough. A hallmark of normal physiology is maintenance of large ion gradients across the cell membrane, mainly for sodium (Na + ) and potassium (K + ) ions. Under normal conditions, high and low Na + in extracellular (Na + ) and intracellular (Na + ) milieu, respectively (and e i the opposite is true for K + ), produce strong transmembrane and weak transendothelial Na + gradients, which contribute to the cell membrane potential (+ \u3c ) and blood-brain-barrier (BBB) integrity, respectively. The Na + /K + -ATPase in the cell membrane plays a crucial role in transporting Na + and K + against their respective electrochemical gradients by consuming ATP. A universal cancer hallmark is reduced oxidation due to downregulation of Na + /K + -ATPase, a consequence of inefficient metabolism which aids tumor survival. Electrolyte balance in the body is crucial for proper functioning of processes like action potential propagation, muscle movement, and maintaining cell volume, but electrolyte imbalances can lead to pathophysiologies like cancer. Translational magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI) methods are an integral part of diagnosing and tracking cancer. Clinical MRI is largely based on detection of the 1 H nuclei in water molecules in soft tissues, where intrinsic 1 H-MRI contrasts provide anatomical separation between healthy tissue and lesion. But paramagnetic lanthanide(III) ions (Ln 3+ ), specifically gadolinium (Gd 3+ ), conjugated with a chelating molecule consisting of electron donors, provide superior 1 H-MRI contrast as the agent (e.g., Dotarem) extravasates into the lesion through leaky blood vessels to enhance the lesion’s appearance. Other nuclei are rarely considered for clinical applications, but these so-called “X-nuclei” (e.g., 23 Na, 31 P, or 17 O) can offer illuminating insights into the physiological processes. 23 Na-MRI has the potential to be a helpful screen for early cancer detection. However, normal 23 Na-MRI cannot separate the overlapping signals between Na + from different compartments like the blood vessel (Na + ), extracellular space (Na + ), and intracellular b e space (Na + ). In this thesis, I have developed a rigorous model which can be applied in i vivo to separate these individual signals by introducing small amounts of a paramagnetic contrast agent based on Ln 3+ metal ions. The model predicts that the induced 23 Na chemical shift and line broadening are monotonically increasing functions of both the agent’s concentration and negative charge, which was validated on a set of nine agents. This established model was then considered for in vivo applications to compartmentalize the 23 Na-MRSI signals in rat models. Since these agents extravasate from blood vessels, but are too negatively charged to enter cells, this method is able to separate and readout the 23 Na signals from Na + , Na + , and Na + with high fidelity by inducing chemical shift differences. b e i Examination of several GBM models in rodent brain, shows that tumors redistribute Na + from the extracellular milieu to dramatically weaken the transmembrane Na + gradient (compared to normal tissue) and concomitantly strengthen the transendothelial Na + gradient. This is a significant finding because the lower level of Na + imply that the e membrane of cancer cells is depolarized (i.e., and thus are they are non-excitable). Others have shown that high + \u3c is common to cells in a proliferative state. Since even immune cells can sense the level of Na + , it is evident the role of compartmental Na + in the tumor e niche will be important to study in vivo. I also report novel findings regarding variations in induced 23 Na line broadening and electrical activity between tumors and healthy tissue which have considerable oncologic significance. Despite the clinical popularity of 1 1 H-MRI, H-MRSI, and general imaging-based approaches, this project demonstrates the vast potential of 23 Na spectroscopic methods to allow novel explorations of the tumor habitat for early diagnosis and tracking treatments

Childhood status epilepticus: Structural consequences and assessment of a novel treatment

Status epilepticus (SE) is the commonest medical neurological emergency in childhood. In animal models of SE, the hippocampus is frequently damaged. The histological features resemble mesial temporal sclerosis (MTS), the commonest pathology in temporal lobe resections in adult humans. The commonest association with MTS is prolonged febrile convulsion (PFC). Hippocampal damage only occurs if seizures persist for at least 30 minutes. Early termination of seizures may decrease the incidence of MTS. Treatment with rectal diazepam is not always acceptable. An effective, convenient and acceptable method of treating SE would be advantageous. To address the question of whether MTS has different magnetic resonance (MR) characteristics dependent on antecedent, quantitative MR data from patients with histologically proven MTS was reviewed. Patients with a history of PFC have asymmetrical hippocampal volume (HCV) and T2 relaxation time (T2) when compared to patients with no history of PFC and controls. This may suggest that severity and extent of MTS may be, in part, determined by the cause. The assessment of whether SE results in acute brain abnormalities was carried out by prospectively investigating children using MR techniques. Within 48 hours of PFC there is an increase in HCV and T2 relaxation time when compared to controls. Patients with SE and no fever have an increased T2 relaxation time but normal hippocampal volume. PFC appears to result in acute hippocampal swelling, consistent with animal model data. The effect of non-febrile SE on limbic structures is less certain. Buccal midazolam was assessed as an effective, socially acceptable acute treatment for seizures. A pharmacokinetic/pharmacodynamic study confirmed rapid absorption into venous blood and brain. Buccal midazolam was shown to be an effective treatment for acute repetitive seizures and at least as effective as rectal diazepam in the treatment of seizures which have persisted for longer than 5 minutes