Scanner Invariant Representations for Diffusion MRI Harmonization

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer\u27s disease

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD. Keywords: Inflammation, White matter damage, Diffusion basis spectrum imaging, Neuro-inflammation imaging, Cerebrospinal fluid, Preclinical Alzheimer disease, Early symptomatic Alzheimer disease, Magnetic resonance imagin

Functional Imaging of Autonomic Regulation: Methods and Key Findings.

Central nervous system processing of autonomic function involves a network of regions throughout the brain which can be visualized and measured with neuroimaging techniques, notably functional magnetic resonance imaging (fMRI). The development of fMRI procedures has both confirmed and extended earlier findings from animal models, and human stroke and lesion studies. Assessments with fMRI can elucidate interactions between different central sites in regulating normal autonomic patterning, and demonstrate how disturbed systems can interact to produce aberrant regulation during autonomic challenges. Understanding autonomic dysfunction in various illnesses reveals mechanisms that potentially lead to interventions in the impairments. The objectives here are to: (1) describe the fMRI neuroimaging methodology for assessment of autonomic neural control, (2) outline the widespread, lateralized distribution of function in autonomic sites in the normal brain which includes structures from the neocortex through the medulla and cerebellum, (3) illustrate the importance of the time course of neural changes when coordinating responses, and how those patterns are impacted in conditions of sleep-disordered breathing, and (4) highlight opportunities for future research studies with emerging methodologies. Methodological considerations specific to autonomic testing include timing of challenges relative to the underlying fMRI signal, spatial resolution sufficient to identify autonomic brainstem nuclei, blood pressure, and blood oxygenation influences on the fMRI signal, and the sustained timing, often measured in minutes of challenge periods and recovery. Key findings include the lateralized nature of autonomic organization, which is reminiscent of asymmetric motor, sensory, and language pathways. Testing brain function during autonomic challenges demonstrate closely-integrated timing of responses in connected brain areas during autonomic challenges, and the involvement with brain regions mediating postural and motoric actions, including respiration, and cardiac output. The study of pathological processes associated with autonomic disruption shows susceptibilities of different brain structures to altered timing of neural function, notably in sleep disordered breathing, such as obstructive sleep apnea and congenital central hypoventilation syndrome. The cerebellum, in particular, serves coordination roles for vestibular stimuli and blood pressure changes, and shows both injury and substantially altered timing of responses to pressor challenges in sleep-disordered breathing conditions. The insights into central autonomic processing provided by neuroimaging have assisted understanding of such regulation, and may lead to new treatment options for conditions with disrupted autonomic function

Methods for Data Management in Multi-Centre MRI Studies and Applications to Traumatic Brain Injury

Neuroimaging studies are becoming increasingly bigger, and multi-centre collaborations to collect data under similar protocols, but different scanning sites, are now commonplace.However, with increasing sample size the complexity of databases and the entailed data management as well as computational burden are growing. This thesis aims to highlight and address challenges faced by large multi-centre magnetic resonance imaging(MRI) studies. The methods implemented are then applied to traumatic brain injury (TBI) data.Firstly, a pre-processing pipeline for both anatomical and diffusion MRI was proposed, that allows for a high throughput of MRI scans. After describing the choices for processing tools,the performance of the integrated quality assurance was assessed based on the results from a large multi-centre dataset for TBI. Secondly, the applicability of the pipelines for processing mild TBI (mTBI) data from three sites was shown in a case study. For this, volumetric and diffusion metrics in the acute phase are analysed for their prognostic potential. Further-more, the cohort was examined for longitudinal changes. Thirdly, independent scan-rescan datasets are examined to gain a better understanding of the degree of reproducibility which can be achieved in imaging studies. This involves analysing the robustness of brain parcellations based on structural or diffusion imaging. The effect of using different MRI scanners or imaging protocols was also assessed and discussed. Fourthly, sources of diffusion MRI variability and different approaches to cope with these are reviewed. Using this foundation,state-of-the art methods for diffusion MRI harmonisation were compared against each other using both a benchmark dataset and mTBI cohort. Lastly, a solution to localise brain lesions was proposed. Its implications for lesion analysis, are assessed in the light of an application to a more severe TBI patient cohort, imaged on two different scanners. Furthermore, a lesion matching algorithm was introduced to automatically examine lesion evolution with time post-injury. In summary, this thesis explored different options for MRI data analysis in the context of large multi-centre studies. Different approaches are studied and compared using a number of different MRI datasets, including scan-rescan data across different MRI scanners and imaging protocols. The potential of the optimised solutions was illustrated through applications to TBI data.CENTER-TB

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker

Using PET/MRI to Assess Hepatic Radioembolization of Yttrium-90 Microspheres

Radioembolization of yttrium-90 (Y-90) microspheres is used to treat primary and secondary cancers in the liver. Though this therapy has existed for decades, the treatment is not well optimized from treatment planning to post-procedural assessment. Recently, there has been a surge to utilize the small positron yield from the radioactive decay of Y-90 for post-radioembolization positron emission tomography (PET) imaging of the microsphere activity distribution. These images provide promise for dosimetry assessment, identifying extrahepatic uptake and possible under-dosed lesions that may benefit from subsequent therapy. However, due to the low positron statistics and high flux of Bremsstrahlung radiation, PET imaging of Y-90 presents with its own unique set of challenges. In this work, we optimized the PET imaging acquisition and reconstruction parameters when imaging with a hybrid PET/MRI scanner to offer the most accurate images for quantitative dosimetric applications. We then tested the variability of imaging Y-90 with PET across multiple institutions in a world-wide phantom study in preparation for a multi-institutional phase I/II clinical trial. Lastly, we determined the clinical utility of using Y-90 PET-based dosimetry to predict clinical outcomes and assess how well it correlates with pre-treatment imaging

A process algebra based simulation model of a Diagnostic Radiology Department

In the last years, it's possible to observe a growing interest in applying in the health care sector tools and methods, which have been successfully applied in other service and industry sectors and have helped to improve planning and efficient use of resources, while maintaining high quality of the delivered service or product. Discrete event simulation represents a powerful and proven tool, which enables the experimentation of several possible solutions at relatively low cost. This paper is focused on the modeling and analysis of a Diagnostic Radiology Department (DRD) in an important Southern Italy hospital and adopts a discrete-event simulation approach based on a process algebra dialect, called $. Some "scenario" results are then illustrated in order to derive basic performance indicators of the system, which could be useful to hospital decision-makers and constitute a starting point for a deeper cost-benefit evaluation