Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions

Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles

STATISTICAL METHODS FOR AUTOMATIC BRAIN SEGMENTATION

Background: Automatic segmentation of the brain into cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) has been of interest for over twenty years. As magnetic resonance imaging (MRI) has improved and new sequences have been developed, more and more data can be utilized to improve and accelerate segmentation algorithms. Objective: To segment the brain into CSF, GM, and WM using multichannel MRI data (T1, T2, PD, FLAIR, water image, and MTC) with a multinomial logistic regression (MLR) model and to compare the results to software segmentations from FSL, FreeSurfer, and TOADS-CRUISE. Methods: Within each subject, the different MRI sequences are co-registered to the water image within subject. Bias field correction and intensity normalization is then applied. The aligned T1 images are used as inputs for existing automatic segmentation software. FreeSurfer and TOADS anatomical segmentations are combined into CSF, GM, and WM. Our method uses MLR applied to normalized brain images. Models are further refined by adding spline terms to model possible non-linear associations. Results: Measures of similarity—the Jaccard index, the dice index, and the confusion matrix—are presented to compare the results of existing software with those obtained from the new MLR method. Segmentations are also compared and rated by a radiology resident. Conclusions: Results based on MLR are comparable to software egmentations. In some areas, they outperform existing software

Metabolic engineering by acetate : monitoring effects at a molecular level

Finding effective life style interventions is paramount in the control of obesity and its comorbidities such as NAFLD. In this thesis I investigate the effects of a short chain fatty acid (SCFA) on obesity and liver fat, using firstly two different carbohydrates, inulin (an indigestible but fermentable carbohydrate) and isomaltulose (a digestible carbohydrate). I go on to investigate the effects of acetate, through the development and implementation of a novel nanoparticle carrier to study the effects of this SCFA on hepatic metabolism. To study the effects of isomaltulose and inulin on adiposity, magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques were used on mice fed on a high fat diet supplemented with these two carbohydrates. Manganese enhanced MRI (MEMRI), was used to monitor hypothalamic brain activation. A significant decrease in hypothalamic activation was detected in both inulin and isomaltulose supplemented animals in the absence of detectable phenotypic changes, including body adiposity and liver fat. As some of the effects of fermentable carbohydrates are thought to occur through the increased production of SCFAs, the potential physiological effects of one of the SCFA, acetate, were further explored. PET imaging was employed to study whole body 11C-acetate biodistribution on a murine model. The highest uptake of 11C-acetate was observed in the heart followed by liver, colon, brown adipose tissue (BAT), brain, fat and muscle (20, 10, 4, 3.6, 3, 2.9 and 2 peak %ID/g respectively). Colonic administration caused significant difference in uptake pattern of heart, liver, brain and BAT (p=0.001), muscle (p=0.0001) and colon (p=0.004) compared with i.v. No difference was observed in fed vs fasted animals. Pre-administration of “cold” acetate prior to systemic administration of 11C-acetate increased uptake of the latter in the liver, heart, brain and BAT suggesting that priming with CA saturates either the GPR43/41 receptors or the transport system of acetate In order to assess the potential beneficial effects of acetate, it was necessary to administer this SCFA in a chronic and consistent manner. Administration of large concentrations of pure SCFA to mice or human is known to have significant detrimental effects, so an alternative nanoparticle based strategy was developed for this purpose. Acetate was encapsulated in liposomal nanoparticles, capable of carrying millimolar concentrations of SCFA. In a murine model fed on normal fat diet, liposomal-encapsulated acetate significantly decreased liver adiposity, but not total body fat, while serum markers of obesity were reduced although they did not reach significance. In a murine model fed on high fat diet, liposomal-encapsulated acetate decreased whole body adiposity, liver fat content and serum free fatty acid (FFA) concentrations and serum markers of liver disease were significantly reduced whereas ketone concentrations in serum were significantly increased. This thesis shows that alterations in dietary carbohydrate composition can lead to significant effects on appetite, probably through the increase production of SCFAs. Furthermore, the use of liposomal-nanoparticles for direct SCFA delivery appears a potentially fast and effective way to treat some of the physiological and metabolic abnormalities associated with obesity

Measurement of subtle blood-brain barrier disruption in cerebral small vessel disease using dynamic contrast-enhanced magnetic resonance imaging

Cerebral small vessel disease (SVD) is a common cause of strokes and dementia. The pathogenesis of SVD is poorly understood, but imaging and biochemical investigations suggest that subtle blood-brain barrier (BBB) leakage may contribute to tissue damage. The most widely-used imaging method for assessing BBB integrity and other microvascular properties is dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI has primarily been applied in situations where contrast uptake in tissue is typically large and rapid (e.g. neuro-oncology); the optimal approach for quantifying BBB integrity in diseases where the BBB remains largely intact and the reliability of resulting measurements is unclear. The main purpose of this thesis was to assess and improve the reliability of quantitative assessment of subtle BBB disruption, in order to illuminate its potential role in cerebral SVD. Firstly, a systematic literature review was performed in order to provide an overview of DCE-MRI methods in the brain. This review found large variations in MRI procedures and data analysis methods, resulting in widely varying estimates of tracer kinetic parameters. Secondly, this thesis focused on the analysis of DCE-MRI data acquired in an on-site clinical study of mild stroke patients. After performing basic DCE-MRI processing (e.g. selection of a vascular input function), this work aimed to determine the tracer kinetic modelling approach most suitable for assessing subtle BBB disruption in this cohort. Using data-driven model selection and computer simulations, the Patlak model was found to provide accurate estimates of blood plasma volume and low-level BBB leakage. Thirdly, this thesis aimed to investigate two potential pitfalls in the quantification of subtle BBB disruption. Contrast-free measurements in healthy volunteers revealed that a signal drift of approximately 0.1 %/min occurs during the DCE-MRI acquisition; computer simulations showed that this drift introduces significant systematic errors when estimating low-level tracer kinetic parameters. Furthermore, tracer kinetic analysis was performed in an external patient cohort in order to investigate the inter-study comparability of DCE-MRI measurements. Due to the nature of the acquisition protocol it proved difficult to obtain reliable estimates of BBB leakage, highlighting the importance of study design. Lastly, this thesis examined the relationship between quantitative MRI parameters and clinical measurements in cerebral SVD, with a focus on the estimates of blood volume and BBB leakage obtained in the internal SVD patient cohort. This work did not provide evidence that BBB leakage in normal-appearing tissue increases with SVD burden or predicts disease progression; however, increased BBB leakage was found in white matter hyperintensities. Furthermore, this work raises the possibility of a role for blood plasma volume and dietary salt intake in cerebral SVD. The work described in this thesis has demonstrated that it is possible to estimate subtle BBB disruption using DCE-MRI, provided that the measurement and data analysis strategies are carefully optimised. However, absolute values of tracer kinetic parameters should be interpreted with caution, particularly when making comparisons between studies, and sources of error and their influence should be estimated where possible. The exact roles of BBB breakdown and other microvascular changes in SVD pathology remain to be defined; however, the work presented in this thesis contributes further insights and, together with technical advances, will facilitate improved study design in the future