Role of the advanced MRI sequences in predicting the outcome of preterm neonates

AIM The aim of the project is to evaluate the role of advanced MRI sequences (susceptibility weight imaging (SWI), diffusion tensor imaging (DTI), and arterial spin labeling (ASL) perfusion) in detecting early changes that affect preterm neonatal brain, especially in those patients without lesions at conventional MRI or with small brain injuries (i.e. low grade germinal matrix-intraventricular hemorrhage (GMHIVH)), and to correlate these subtle brain abnormalities with neurodevelopmental outcome at 24 months. METHODS Since November 2015 until June 2017, 287 preterm neonates and 108 term neonates underwent a 3T or 1.5T MRI study at term corrected age (40\ub11 weeks). SWI, DTI and ASL sequences were performed in all neonates. SWI sequences were evaluated using both a qualitative (SWI venography) and quantitative (Quantitative Susceptibility Map analysis (SWI-QSM)) approach. DTI data were analyzed using a Tract-Based Spatial Statistics analysis (TBSS). ASL studies were processed to estimate Cerebral Blood Flow (CBF) maps. Perinatal clinical data were collected for all neonates. Neurodevelopmental data were evaluated at 24 months in 175 neonates using 0-2 Griffiths Developmental Scales. RESULTS The analysis performed on SWI-venography revealed differences in subependymal veins morphology between preterm and term neonates with normal brain MRI, with a higher variability from the typical anatomical pattern in preterm neonates. The same analysis performed in preterm neonates with GMH-IVH revealed that the anatomical features of subependymal veins may play a potential role as predisposing factor for GMH-IVH. Moreover, the SWI-QSM analysis revealed a greater paramagnetic susceptibility in several periventricular white matter (WM) regions in preterm neonates with GMH-IVH than in healthy controls. This finding is likely related to the accumulation of hemosiderin/ferritin following the diffusion of large amounts of intraventricular blood products into the WM, and it is also supposed to trigger the cascade of lipid peroxidation and free radical formation that promote oxidative and inflammatory injury of the WM in neonatal brain after GMH-IVH. The TBSS analysis confirmed that microstructural WM injury can occur in preterm neonates with low grade GMH-IVH even in the absence of overt signal changes on conventional MRI, with different patterns of WM involvement depending on gestational age. Moreover, the distribution of these WM microstructural alterations after GMH-IVH correlates with specific neurodevelopmental impairments at 24 months of age. Finally, the analysis of brain perfusion at term-corrected age revealed lower CBF in preterms with sub-optimal neuromotor development, reinforcing the hypothesis that impaired autoregulation of CBF may contribute to the development of brain damage in preterm neonates. CONCLUSION Advanced MRI sequences can assist the standard perinatal brain imaging in the early diagnosis of preterm neonatal brain lesions and can provide new insights for predicting the neurodevelopmental trajectory. However, detailed and serial imaging of carefully chosen cohorts of neonates coupled with longer clinical follow-up are essential to ensure the clinical significance of these novel findings

Noninvasive Multi-Modality Studies of Cardiac Electrophysiology, Mechanics, and Anatomical Substrate in Healthy Adults, Arrhythmogenic Cardiomyopathy, and Heart Failure

Heart disease is a leading cause of death and disability and is a major contributor to healthcare costs. Many forms of heart disease are caused by abnormalities in the electrical function of heart muscle cells or the cardiac conduction system. Electrocardiographic Imaging (ECGI) is a noninvasive modality for imaging cardiac electrophysiology. By combining recordings of the voltage distribution on the torso surface with anatomical images of the heart-torso geometry, ECGI reconstructs voltages on the epicardium. This thesis applies ECGI to novel studies of human heart function and disease and explores new combinations of ECGI with additional imaging modalities. ECGI was applied in combination with late gadolinium enhancement (LGE) scar imaging MRI in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). ARVC carries a high risk of sudden cardiac death, and the hallmark feature of ARVC is the progressive replacement of healthy myocardium with fibrous and fatty tissue. By combining ECGI and LGE in ARVC patients we found that there are signs of conduction abnormalities before structural abnormalities can be detected in ARVC patients. Electrical and structural abnormalities in ARVC patients co-localized. We also found that PVCs, potential triggers for arrhythmia, originated in regions of structural and electrical abnormalities. ECGI was applied in combination with speckle tracking echocardiography (STE) to longitudinally image heart failure patients undergoing cardiac resynchronization therapy (CRT). STE is an echocardiographic technique for measuring strain (contraction) in the heart. CRT is a highly effective treatment for heart failure, however, around 30% of patients do not respond to the treatment. ECGI was more effective for predicting response to CRT than the current standard ECG criteria or STE indices. The timing of peak contraction in STE did not accurately reflect the electrical activation sequence. CRT caused improvements in contraction that persisted even when pacing was disabled. CRT prolonged repolarization at the site of the LV pacing lead, which may increase the risk of arrhythmia in CRT patients. The above studies contribute novel observations of human disease physiology and demonstrate the clinical feasibility and effectiveness of ECGI for noninvasive assessment of ARVC and CRT

Characterization of Carotid Plaques with Ultrasound Non-Invasive Vascular Elastography (NIVE) : Feasibility and Correlation with High-Resolution Magnetic Resonance Imaging

L’accident vasculaire cérébral (AVC) est une cause principale de décès et de morbidité dans le monde; une bonne partie des AVC est causée par la plaque d’athérosclérose carotidienne. La prévention de l’AVC chez les patients ayant une plaque carotidienne demeure controversée, vu les risques et bénéfices ambigus associés au traitement chirurgical ou médical. Plusieurs méthodes d’imagerie ont été développées afin d’étudier la plaque vulnérable (dont le risque est élevé), mais aucune n’est suffisamment validée ou accessible pour permettre une utilisation comme outil de dépistage. L’élastographie non-invasive vasculaire (NIVE) est une technique nouvelle qui cartographie les déformations (élasticité) de la plaque afin de détecter les plaque vulnérables; cette technique n’est pas encore validée cliniquement. Le but de ce projet est d’évaluer la capacité de NIVE de caractériser la composition de la plaque et sa vulnérabilité in vivo chez des patients ayant des plaques sévères carotidiennes, en utilisant comme étalon de référence, l’imagerie par résonance magnétique (IRM) à haute-résolution. Afin de poursuivre cette étude, une connaissance accrue de l’AVC, l’athérosclérose, la plaque vulnérable, ainsi que des techniques actuelles d’imagerie de la plaque carotidienne, est requise. Trente-et-un sujets ont été examinés par NIVE par ultrasonographie et IRM à haute-résolution. Sur 31 plaques, 9 étaient symptomatiques, 17 contenaient des lipides, et 7 étaient vulnérables selon l’IRM. Les déformations étaient significativement plus petites chez les plaques contenant des lipides, avec une sensibilité élevée et une spécificité modérée. Une association quadratique entre la déformation et la quantité de lipide a été trouvée. Les déformations ne pouvaient pas distinguer les plaques vulnérables ou symptomatiques. En conclusion, NIVE par ultrasonographie est faisable chez des patients ayant des sténoses carotidiennes significatives et peut détecter la présence d’un coeur lipidique. Des études supplémentaires de progression de la plaque avec NIVE sont requises afin d’identifier les plaques vulnérables.Stroke is a leading cause of death and morbidity worldwide, and a significant proportion of strokes are caused by carotid atherosclerotic plaque rupture. Prevention of stroke in patients with carotid plaque poses a significant challenge to physicians, as risks and benefits of surgical or medical treatments remain equivocal. Many imaging techniques have been developed to identify and study vulnerable (high-risk) atherosclerotic plaques, but none is sufficiently validated or accessible for population screening. Non-invasive vascular elastography (NIVE) is a novel ultrasonic technique that maps carotid plaque strain (elasticity) characteristics to detect its vulnerability; it has not been clinically validated yet. The goal of this project is to evaluate the ability of ultrasound NIVE strain analysis to characterize carotid plaque composition and vulnerability in vivo in patients with significant plaque burden, as determined by the reference standard, high resolution MRI. To undertake this study, a thorough understanding of stroke, atherosclerosis, vulnerable plaque, and current non-invasive carotid plaque imaging techniques is required. Thirty-one subjects underwent NIVE and high-resolution MRI of internal carotid arteries. Of 31 plaques, 9 were symptomatic, 17 contained lipid and 7 were vulnerable on MRI. Strains were significantly lower in plaques containing a lipid core compared to those without lipid, with high sensitivity and moderate specificity. A quadratic fit was found between strain and lipid content. Strains did not discriminate symptomatic patients or vulnerable plaques. In conclusion, ultrasound NIVE is feasible in patients with significant carotid stenosis and can detect the presence of a lipid core. Further studies of plaque progression with NIVE are required to identify vulnerable plaques

Diagnosis and prognosis of cardiovascular diseases by means of texture analysis in magnetic resonance imaging

Cardiovascular diseases constitute the leading global cause of morbidity and mortality. Magnetic resonance imaging (MRI) has become the gold standard technique for the assessment of patients with myocardial infarction. However, limitations still exist thus new alternatives are open to investigation. Texture analysis is a technique that aims to quantify the texture of the images that are not always perceptible by the human eye. It has been successfully applied in medical imaging but applications to cardiac MRI (CMR) are still scarce. Therefore, the purpose of this thesis was to apply texture analysis in conventional CMR images for the assessment of patients with myocardial infarction, as an alternative to current methods. Three applications of texture analysis and machine learning techniques were studied: i) Detection of infarcted myocardium in late gadolinium enhancement (LGE) CMR. Segmentation of the infarcted myocardium is routinely performed using image intensity thresholds. The inclusion of texture features to aid the segmentation was analyzed obtaining overall good results. The method was developed using 10 LGE CMR datasets and tested on a separate dataset comprising 5 cases that were acquired with a completely different scanner than that used for training. Therefore, this preliminary study showed the transferability of texture analysis which is important for clinical applicability. ii) Differentiation of acute and chronic myocardial infarction using LGE CMR and standard pre-contrast cine CMR. In this study, two different feature selection techniques and six different machine learning classifiers were studied and compared. The best classification was achieved using a polynomial SVM obtaining an overall AUC of 0.87 ± 0.06 in LGE CMR. Interestingly, results on cine CMR in which infarctions are visually imperceptible in most cases were also good (AUC = 0.83 ± 0.08). iii) Detection of infarcted non-viable segments in cine CMR. This study was motivated by the findings of the previous one. It demonstrated that texture analysis can be used to distinguish non-viable, viable and remote segments using standard pre-contrast cine CMR solely. This was the most relevant contribution of this thesis as it can be used as hypothesis for future work aiming to accurately delineate the infarcted myocardium as a gadolinium-free alternative that will have potential advantages. The three proposed applications were successfully performed obtaining promising results. In conclusion, texture analysis can be successfully applied to conventional CMR images and provides a potential quantitative alternative to existing methods

Developing and Applying CAD-generated Image Markers to Assist Disease Diagnosis and Prognosis Prediction

Developing computer-aided detection and/or diagnosis (CAD) schemes has been an active research topic in medical imaging informatics (MII) with promising results in assisting clinicians in making better diagnostic and/or clinical decisions in the last two decades. To build robust CAD schemes, we need to develop state-of-the-art image processing and machine learning (ML) algorithms to optimize each step in the CAD pipeline, including detection and segmentation of the region of interest, optimal feature generation, followed by integration to ML classifiers. In my dissertation, I conducted multiple studies investigating the feasibility of developing several novel CAD schemes in the field of medicine concerning different purposes. The first study aims to investigate how to optimally develop a CAD scheme of contrast-enhanced digital mammography (CEDM) images to classify breast masses. CEDM includes both low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron-based ML classifiers integrated with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. The study demonstrated that DES images eliminated the overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. By mapping mass regions segmented from DES images to LE images, CAD yields significantly improved performance. The second study aims to develop a new quantitative image marker computed from the pre-intervention computed tomography perfusion (CTP) images and evaluate its feasibility to predict clinical outcome among acute ischemic stroke (AIS) patients undergoing endovascular mechanical thrombectomy after diagnosis of large vessel occlusion. A CAD scheme is first developed to pre-process CTP images of different scanning series for each study case, perform image segmentation, quantify contrast-enhanced blood volumes in bilateral cerebral hemispheres, and compute image features related to asymmetrical cerebral blood flow patterns based on the cumulative cerebral blood flow curves of two hemispheres. Next, image markers based on a single optimal feature and ML models fused with multi-features are developed and tested to classify AIS cases into two classes of good and poor prognosis based on the Modified Rankin Scale. The study results show that ML model trained using multiple features yields significantly higher classification performance than the image marker using the best single feature (p<0.01). This study demonstrates the feasibility of developing a new CAD scheme to predict the prognosis of AIS patients in the hyperacute stage, which has the potential to assist clinicians in optimally treating and managing AIS patients. The third study aims to develop and test a new CAD scheme to predict prognosis in aneurysmal subarachnoid hemorrhage (aSAH) patients using brain CT images. Each patient had two sets of CT images acquired at admission and prior to discharge. CAD scheme was applied to segment intracranial brain regions into four subregions, namely, cerebrospinal fluid (CSF), white matter (WM), gray matter (GM), and extraparenchymal blood (EPB), respectively. CAD then computed nine image features related to 5 volumes of the segmented sulci, EPB, CSF, WM, GM, and four volumetrical ratios to sulci. Subsequently, 16 ML models were built using multiple features computed either from CT images acquired at admission or prior to discharge to predict eight prognosis related parameters. The results show that ML models trained using CT images acquired at admission yielded higher accuracy to predict short-term clinical outcomes, while ML models trained using CT images acquired prior to discharge had higher accuracy in predicting long-term clinical outcomes. Thus, this study demonstrated the feasibility of predicting the prognosis of aSAH patients using new ML model-generated quantitative image markers. The fourth study aims to develop and test a new interactive computer-aided detection (ICAD) tool to quantitatively assess hemorrhage volumes. After loading each case, the ICAD tool first segments intracranial brain volume, performs CT labeling of each voxel. Next, contour-guided image-thresholding techniques based on CT Hounsfield Unit are used to estimate and segment hemorrhage-associated voxels (ICH). Next, two experienced neurology residents examine and correct the markings of ICH categorized into either intraparenchymal hemorrhage (IPH) or intraventricular hemorrhage (IVH) to obtain the true markings. Additionally, volumes and maximum two-dimensional diameter of each sub-type of hemorrhage are also computed for understanding ICH prognosis. The performance to segment hemorrhage regions between semi-automated ICAD and the verified neurology residents’ true markings is evaluated using dice similarity coefficient (DSC). The data analysis results in the study demonstrate that the new ICAD tool enables to segment and quantify ICH and other hemorrhage volumes with higher DSC. Finally, the fifth study aims to bridge the gap between traditional radiomics and deep learning systems by comparing and assessing these two technologies in classifying breast lesions. First, one CAD scheme is applied to segment lesions and compute radiomics features. In contrast, another scheme applies a pre-trained residual net architecture (ResNet50) as a transfer learning model to extract automated features. Next, the principal component algorithm processes both initially computed radiomics and automated features to create optimal feature vectors. Then, several support vector machine (SVM) classifiers are built using the optimized radiomics or automated features. This study indicates that (1) CAD built using only deep transfer learning yields higher classification performance than the traditional radiomic-based model, (2) SVM trained using the fused radiomics and automated features does not yield significantly higher AUC, and (3) radiomics and automated features contain highly correlated information in lesion classification. In summary, in all these studies, I developed and investigated several key concepts of CAD pipeline, including (i) pre-processing algorithms, (ii) automatic detection and segmentation schemes, (iii) feature extraction and optimization methods, and (iv) ML and data analysis models. All developed CAD models are embedded with interactive and visually aided graphical user interfaces (GUIs) to provide user functionality. These techniques present innovative approaches for building quantitative image markers to build optimal ML models. The study results indicate the underlying CAD scheme's potential application to assist radiologists in clinical settings for their assessments in diagnosing disease and improving their overall performance

Using vision transformer to synthesize computed tomography perfusion images in ischemic stroke patients

Computed tomography perfusion (CTP) imaging is crucial for diagnosing and determining the extent of damage in cerebral stroke patients [1]. Automatic segmentation of ischemic core and penumbra regions in CTP images is desired, given the limitations of manual examination. Self-supervised segmentation has gained attention [2], but it requires a large training set that can be obtained by synthesizing CTP images. Deep convolutional generative adversarial networks (DCGANs) have been used for this purpose [3], but high-resolution image synthesis remains a challenge. To address this, we propose to tailor the high-resolution transformer-based generative adversarial network (HiT-GAN) model, proposed by Zhao et al. [4], which utilizes vision transformers and self-attention mechanisms for the purposes of generating high-quality CTP data. Our proposed model was trained using CTP images from 157 patients, categorized based on vessel occlusion. The dataset consisted of 70,050 raw data images, which were normalized and downsampled. Comparative evaluation with DCGAN showed that HiT-GAN achieved a significantly lower fréchet inception distance (FID) score of 77.4, compared to 143.0 for the DCGAN, indicating superior image generation performance. The generated images were visually compared with real samples, demonstrating promising results. While the current focus is on generating 2D images, future work aims to extend the model to generate 3D CTP data conditioned on labeled brain slices. Overall, our study highlights the potential of HiT-GAN for synthesizing high-resolution CTP images, although its significance in advancing automatic segmentation techniques for ischemic stroke analysis is yet to be examined

Brain Injury

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