Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies

Development of Deep Learning Methods for Magnetic Resonance Phase Imaging of Neurological Disease

Magnetic resonance imaging (MRI) is a high-resolution, non-invasive medical imaging modality that is widely used in human brain. In recent years, susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) have been proposed to utilize MR phase signal to generate contrast from tissue magnetic susceptibility and even quantify the property. On the other hand, deep learning, especially deep convolutional neural networks (DCNNs), have achieved state-of-the-art performances in numerous computer vision tasks and gained significant attention in the field of medical imaging in the recent years. This dissertation combined the idea of deep learning with the two MR phase imaging methods. To combined deep learning with SWI, we designed and trained a 3D deep residual network that can distinguish false positive detected candidates from cerebral microbleeds (CMBs) and built an automatic CMB detection pipeline with high performance. We further confirmed the generalizability of this deep learning-based pipeline using multiple dataset with different scan parameters and pathologies and provided lessons for application and generalization of generic deep learning based medical imaging methods.To combine deep learning with QSM, we developed a 3D U-Net based network that learns to perform dipole inversion from gold standard QSM acquired from data with multiple orientation. The model was further improved with adversarial training strategy and achieved significantly lower reconstruction error than traditional QSM algorithms. In addition, we also performed various background removal and dipole inversion algorithms on both brain tumor patients and healthy volunteers to study and compare their performances. The results could provide guidance on future application of QSM in different scenarios

Detection of Cerebral Microbleeds With Venous Connection at 7-Tesla MRI

Objective: Cerebral microbleeds (MBs) are a common finding in patients with cerebral small vessel disease (CSVD) and Alzheimer disease as well as in healthy elderly people, but their pathophysiology remains unclear. To investigate a possible role of veins in the development of MBs, we performed an exploratory study, assessing in vivo presence of MBs with a direct connection to a vein. Methods: 7-Tesla (7T) MRI was conducted and MBs were counted on quantitative susceptibility mapping (QSM). A submillimeter resolution QSM-based venogram allowed identification of MBs with a direct spatial connection to a vein. Results: A total of 51 people (mean age [SD] 70.5 [8.6] years, 37% female) participated in the study: 20 had CSVD (cerebral amyloid angiopathy [CAA] with strictly lobar MBs [n = 8], hypertensive arteriopathy [HA] with strictly deep MBs [n = 5], or mixed lobar and deep MBs [n = 7], 72.4 [6.1] years, 30% female) and 31 were healthy controls (69.4 [9.9] years, 42% female). In our cohort, we counted a total of 96 MBs with a venous connection, representing 14% of all detected MBs on 7T QSM. Most venous MBs (86%, n = 83) were observed in lobar locations and all of these were cortical. Patients with CAA showed the highest ratio of venous to total MBs (19%) (HA = 9%, mixed = 18%, controls = 5%). Conclusion: Our findings establish a link between cerebral MBs and the venous vasculature, pointing towards a possible contribution of veins to CSVD in general and to CAA in particular. Pathologic studies are needed to confirm our observations

Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

Deteção e Quantificação de Microhemorragias Cerebrais com Base em Imagens de Ressonância Magnética Ponderadas por Suscetibilidade Magnética

As microhemorragias cerebrais (CMBs) têm uma função importante no desenvolvimento de hemorragias intracerebrais (ICH) e doenças cerebrovasculares. Estas microestruturas surgem devido ao sangramento perivascular dos pequenos vasos, principalmente afetados por vasculopatia hipertensiva e angiopatia amilóide cerebral, que consistem na forma esporádica da doença dos pequenos vasos (SVD) cerebrais. Esta patologia consiste na segunda maior causa de demência, que por sua vez constituí uma das preocupações a nível global que afeta sobretudo a população idosa. Para que seja possível o diagnóstico precoce, bem como a monitorização da progressão da SVD existe a necessidade do desenvolvimento de um protocolo na prática clínica que agilize o processo de deteção e quantificação de forma automática, rápida e eficiente destes biomarcadores imagiológicos (CMBs) em pacientes com SVD. O processo de inspeção visual de CMBs é na maioria das vezes impraticável em exames de rotina, dado que é bastante demorado. Uma das modalidades de ressonância magnética (RM) com grande potencial para a deteção de CMBs é a imagem ponderada por suscetibilidade magnética (SWI), cuja influência de diversos fatores na quantificação de CMBs ainda necessitam de ser explorados. Assim sendo, nesta tese propôs-se averiguar o potencial de técnicas avançadas de RM, a fim de detetar as CMBs presentes na SVD, incluindo o estudo sistemático de várias opções de préprocessamento das imagens SWI, através da manipulação das máscaras de fase positiva, negativa e sigmóide. Para além da apreciação visual das máscaras de fase procedeu-se à avaliação de algoritmos de aprendizagem automática para a deteção das CMBs. Deste estudo, conclui-se que as imagens SWI pertencentes ao conjunto de dados previamente adquirido podem surgir devido à multiplicação da máscara positiva com a imagem de magnitude quatro vezes. A máscara de fase que proporciona o aumento da sensibilidade na deteção de CMBs é a máscara positiva através de oito multiplicações

Cerebrovascular dysfunction in cerebral small vessel disease

INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

Investigating the effects of microstructure and magnetic susceptibility in MRI

Over the last decade, phase measurements derived from gradient echo MRI have increasingly been used as a source of quantitative information, allowing tissue composition and microstructure to be probed in vivo and opening up many new avenues of research. However, the non-local nature of phase contrast and the complexity of the underlying sources of phase variation mean that care must be taken in the interpretation and exploitation of phase information. The work described in this thesis explores the application of phase-based quantitative susceptibility measurements in vivo, and uses theory, experiment, and simulation to investigate the contribution of local structural effects to measurements of MRI signal phase. In initial work, the use of phase imaging and quantitative susceptibility mapping (QSM) is compared in the analysis of white matter lesions in multiple sclerosis, demonstrating in vivo the dipolar distortions inherent in phase images, and the correction of such artefacts through the application of QSM, based on a thresholded k-space division method . Visual analysis of the lesions with a focus on the presence of the peripheral rings that occur in some white matter lesions allows comparison of our data with previous studies. A theoretical description of effects of magnetic susceptibility anisotropy using a susceptibility tensor model is then presented, and its predictions tested using macroscopic phantoms composed of pyrolytic graphite sheet, a highly anisotropic and diamagnetic material. The results of these experiments confirm that the full tensor model must be used to predict the effects of structures composed of such materials on the magnetic field. Finally, Monte Carlo simulation is used to demonstrate the effects of perturber shape and diffusion on the MRI signal phase measured from a volume containing oriented, NMR-invisible, spheroidal perturbers with constant bulk magnetic susceptibility. The rate of phase accumulation over time is shown to be highly dependent on perturber shape and diffusion, and the possible implication of these results on real MRI measurements are discussed

Investigating the effects of microstructure and magnetic susceptibility in MRI

Over the last decade, phase measurements derived from gradient echo MRI have increasingly been used as a source of quantitative information, allowing tissue composition and microstructure to be probed in vivo and opening up many new avenues of research. However, the non-local nature of phase contrast and the complexity of the underlying sources of phase variation mean that care must be taken in the interpretation and exploitation of phase information. The work described in this thesis explores the application of phase-based quantitative susceptibility measurements in vivo, and uses theory, experiment, and simulation to investigate the contribution of local structural effects to measurements of MRI signal phase. In initial work, the use of phase imaging and quantitative susceptibility mapping (QSM) is compared in the analysis of white matter lesions in multiple sclerosis, demonstrating in vivo the dipolar distortions inherent in phase images, and the correction of such artefacts through the application of QSM, based on a thresholded k-space division method . Visual analysis of the lesions with a focus on the presence of the peripheral rings that occur in some white matter lesions allows comparison of our data with previous studies. A theoretical description of effects of magnetic susceptibility anisotropy using a susceptibility tensor model is then presented, and its predictions tested using macroscopic phantoms composed of pyrolytic graphite sheet, a highly anisotropic and diamagnetic material. The results of these experiments confirm that the full tensor model must be used to predict the effects of structures composed of such materials on the magnetic field. Finally, Monte Carlo simulation is used to demonstrate the effects of perturber shape and diffusion on the MRI signal phase measured from a volume containing oriented, NMR-invisible, spheroidal perturbers with constant bulk magnetic susceptibility. The rate of phase accumulation over time is shown to be highly dependent on perturber shape and diffusion, and the possible implication of these results on real MRI measurements are discussed