Arterial Spin Labeling Perfusion of the Brain: Emerging Clinical Applications

Arterial spin labeling (ASL) is a magnetic resonance (MR) imaging technique used to assess cerebral blood flow noninvasively by magnetically labeling inflowing blood. In this article, the main labeling techniques, notably pulsed and pseudocontinuous ASL, as well as emerging clinical applications will be reviewed. In dementia, the pattern of hypoperfusion on ASL images closely matches the established patterns of hypometabolism on fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) images due to the close coupling of perfusion and metabolism in the brain. This suggests that ASL might be considered as an alternative for FDG, reserving PET to be used for the molecular disease-specific amyloid and tau tracers. In stroke, ASL can be used to assess perfusion alterations both in the acute and the chronic phase. In arteriovenous malformations and dural arteriovenous fistulas, ASL is very sensitive to detect even small degrees of shunting. In epilepsy, ASL can be used to assess the epileptogenic focus, both in peri- and interictal period. In neoplasms, ASL is of particular interest in cases in which gadolinium-based perfusion is contraindicated (eg, allergy, renal impairment) and holds promise in differentiating tumor progression from benign causes of enhancement. Finally, various neurologic and psychiatric diseases including mild traumatic brain injury or posttraumatic stress disorder display alterations on ASL images in the absence of visualized structural changes. In the final part, current limitations and future developments of ASL techniques to improve clinical applicability, such as multiple inversion time ASL sequences to assess alterations of transit time, reproducibility and quantification of cerebral blood flow, and to measure cerebrovascular reserve, will be reviewed

Intelligent Imaging of Perfusion Using Arterial Spin Labelling

Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage

Learning-based Algorithms for Inverse Problems in MR Image Reconstruction and Quantitative Perfusion Imaging

Medical imaging has become an integral part of the clinical pipeline through its widespread use in the diagnosis, prognosis and treatment planning of several diseases. Magnetic Resonance Imaging (MRI) is particularly useful because it is free from ionizing radiation and is able to provide excellent soft tissue contrast. However, MRI suffers from drawbacks like long scanning durations that increase the cost of imaging and render the acquired images vulnerable to artifacts like motion. In modalities like Arterial Spin Labeling (ASL), which is used for non-invasive and quantitative perfusion imaging, low signal-to-noise ratio and lack of precision in parameter estimates also present significant problems. In this thesis, we develop and present algorithms whose focus can be divided into two broad categories. First, we investigate the reconstruction of MR images from fewer measurements, using data-driven machine learning to fill in the gaps in acquisition, thereby reducing the scan duration. Specifically, we first combine a supervised and an unsupervised (blind) learned dictionary in a residual fashion as a spatial prior in MR image reconstruction, and then extend this framework to include deep supervised learning. The latter, called blind primed supervised (BLIPS) learning, proposes that there exists synergy between features learned using shallower dictionary-based methods or traditional prior-based image reconstruction and those learned using newer deep supervised learning-based approaches. We show that this synergy can be exploited to yield reconstructions that are approx. 0.5-1 dB better in PSNR (in avg. across undersampling patterns). We also observe that the BLIPS algorithm is more robust to a scarcity of available training data, yielding reconstructions that are approx. 0.8 dB better (in terms of avg. PSNR) compared to strict supervised learning reconstruction when training data is very limited. Secondly, we aim to provide more precise estimates for multiple physiological parameters and tissue properties from ASL scans by estimation-theory-based optimization of ASL scan design, and combination with MR Fingerprinting. For this purpose, we use the Cramer-Rao Lower Bound (CRLB) for optimizing the scan design, and deep learning for regression-based estimation. We also show that regardless of the estimator used, optimization improves the precision in parameter estimates, and enables us to increase the available ‘useful’ information obtained in a fixed scanning duration. Specifically, we successfully improve the theoretical precision of perfusion estimates by 4.6% compared to a scan design where the repetition times are randomly chosen (a popular choice in literature) thereby yielding a 35.2% improvement in the corresponding RMSE in our in-silico experiments. This improvement is also visually evident in our in-vivo studies on healthy human subjects.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169819/1/anishl_1.pd

Advances in MRI-Based Detection of Cerebrovascular Changes after Experimental Traumatic Brain Injury

Traumatic brain injury is a heterogeneous and multifaceted neurological disorder that involves diverse pathophysiological pathways and mechanisms. Thorough characterization and monitoring of the brain’s status after neurotrauma is therefore highly complicated. Magnetic resonance imaging (MRI) provides a versatile tool for in vivo spatiotemporal assessment of various aspects of central nervous system injury, such as edema formation, perfusion disturbances and structural tissue damage. Moreover, recent advances in MRI methods that make use of contrast agents have opened up additional opportunities for measurement of events at the level of the cerebrovasculature, such as blood–brain barrier permeability, leukocyte infiltration, cell adhesion molecule upregulation and vascular remodeling. It is becoming increasingly clear that these cerebrovascular alterations play a significant role in the progression of post-traumatic brain injury as well as in the process of post-traumatic brain repair. Application of advanced multiparametric MRI strategies in experimental, preclinical studies may significantly aid in the elucidation of pathomechanisms, monitoring of treatment effects, and identification of predictive markers after traumatic brain injury

A survey of deep learning models in medical therapeutic areas.

pre-print1407 K

Mapping track density changes in nigrostriatal and extranigral pathways in Parkinson's disease

peer reviewedHighlights First whole-brain probabilistic tractography study in Parkinson's disease High quality diffusion-weighted images (120 gradient directions, b = 2500 s/mm2) Voxel-based group analysis comparing early-stage patients and controls Abnormal reconstructed track density in the nigrostriatal pathway and brainstem Track density also increased in limbic and cognitive circuits

Integrated Structural And Functional Biomarkers For Neurodegeneration

Alzheimer\u27s Disease consists of a complex cascade of pathological processes, leading to the death of cortical neurons and development of dementia. Because it is impossible to regenerate neurons that have already died, a thorough understanding of the earlier stages of the disease, before significant neuronal death has occurred, is critical for developing disease-modifying therapies. The various components of Alzheimer\u27s Disease pathophysiology necessitate a variety of measurement techniques. Image-based measurements known as biomarkers can be used to assess cortical thinning and cerebral blood flow, but non-imaging characteristics such as performance on cognitive tests and age are also important determinants of risk of Alzheimer\u27s Disease. Incorporating the various imaging and non-imaging sources of information into a scientifically interpretable and statistically sound model is challenging. In this thesis, I present a method to include imaging data in standard regression analyses in a data-driven and anatomically interpretable manner. I also introduce a technique for disentangling the effect of cortical structure from blood flow, enabling a clearer picture of the signal carried by cerebral blood flow beyond the confounding effects of anatomical structure. In addition to these technical developments in multi-modal image analysis, I show the results of two clinically-oriented studies focusing on the relative importance of various biomarkers for predicting presence of Alzheimer\u27s Disease pathology in the earliest stages of disease. In the first, I present evidence that white matter hyperintensities, a marker of small vessel disease, are more highly associated with Alzheimer\u27s Disease pathology than current mainstream imaging biomarkers in elderly control patients. In the second, I show that once Alzheimer\u27s Disease has progressed to the point of noticeable cognitive decline, cognitive tests are as predictive of presence of Alzheimer\u27s pathology as standard imaging biomarkers. Taken together, these studies demonstrate that the relative importance of biomarkers and imaging modalities changes over the course of disease progression, and sophisticated data-driven methods for combining a variety of modalities is likely to lead to greater biological insight into the disease process than a single modality

Diffusion and Perfusion MRI in Paediatric Posterior Fossa Tumours

Brain tumours in children frequently occur in the posterior fossa. Most undergo surgical resection, after which up to 25% develop cerebellar mutism syndrome (CMS), characterised by mutism, emotional lability and cerebellar motor signs; these typically improve over several months. This thesis examines the application of diffusion (dMRI) and arterial spin labelling (ASL) perfusion MRI in children with posterior fossa tumours. dMRI enables non-invasive in vivo investigation of brain microstructure and connectivity by a computational process known as tractography. The results of a unique survey of British neurosurgeons’ attitudes towards tractography are presented, demonstrating its widespread adoption and numerous limitations. State-of-the-art modelling of dMRI data combined with tractography is used to probe the anatomy of cerebellofrontal tracts in healthy children, revealing the first evidence of a topographic organization of projections to the frontal cortex at the superior cerebellar peduncle. Retrospective review of a large institutional series shows that CMS remains the most common complication of posterior fossa tumour resection, and that surgical approach does not influence surgical morbidity in this cohort. A prospective case-control study of children with posterior fossa tumours treated at Great Ormond Street Hospital is reported, in which children underwent longitudinal MR imaging at three timepoints. A region-of-interest based approach did not reveal any differences in dMRI metrics with respect to CMS status. However, the candidate also conducted an analysis of a separate retrospective cohort of medulloblastoma patients at Stanford University using an automated tractography pipeline. This demonstrated, in unprecedented spatiotemporal detail, a fine-grained evolution of changes in cerebellar white matter tracts in children with CMS. ASL studies in the prospective cohort showed that following tumour resection, increases in cortical cerebral blood flow were seen alongside reductions in blood arrival time, and these effects were modulated by clinical features of hydrocephalus and CMS. The results contained in this thesis are discussed in the context of the current understanding of CMS, and the novel anatomical insights presented provide a foundation for future research into the condition

Magnetic Resonance Imaging Studies of Angiogenesis and Stem Cell Implantations in Rodent Models of Cerebral Lesions

Molecular biology and stem cell research have had an immense impact on our understanding of neurological diseases, for which little or no therapeutic options exist today. Manipulation of the underlying disease-specific molecular and cellular events promises more efficient therapy. Angiogenesis, i.e. the regrowth of new vessels from an existing vascular network, has been identified as a key contributor for the progression of tumor and, more recently, for regeneration after stroke. Donation of stem cells has proved beneficial to treat cerebral lesions. However, before angiogenesis-targeted and stem cell therapies can safely be used in patients, underlying biological processes need to be better understood in animal models. Noninvasive imaging is essential in order to follow biological processes or stem cell fate in both space and time. We optimized steady state contrast enhanced magnetic resonance imaging (SSCE MRI) to monitor vascular changes in rodent models of tumor and stroke. A modification of mathematical modeling of MR signal from the vascular network allowed for the first time simultaneous measurements of relaxation time T2 and SSCE MRI derived blood volume, vessel size, and vessel density. Limitations of SSCE MRI in tissues with high blood volume and non-cylindrically shaped vessels were explored. SSCE MRI detected angiogenesis and response to anti-angiogenic treatment in two rodent tumor models. In both tumor models, reduction of blood volume in small vessels and a shift towards larger vessels was observed upon treatment. After stroke, decreased vessel density and increased vessel size was found, which was most pronounced one week after the infarct. This is in agreement with two initial, recently published clinical studies. Overall, very little signs of angiogenesis were found. Furthermore, superparamagnetic iron oxide (SPIO) labels were used to study neural stem cells (NSCs) in vivo with MRI. SPIO labeling revealed a decrease in volume of intracerebral grafts over 4 months, assessed by T2* weighted MRI. Since SPIO labels are challenging to quantify and their MR contrast can easily be confounded, we explored the potential of in vivo 19F MRI of 19F labeled NSCs. Hardware was developed for in vitro and in vivo 19F MRI. NSCs were labeled with little effect on cell function and in vivo detection limits were determined at ~10,000 cells within 1 h imaging time. A correction for the inhomogeneous magnetic field profile of surface coils was validated in vitro and applied for both sensitive and quantitative in vivo cell imaging. As external MRI labels do not provide information on NSC function we combined 19F MRI with bioluminescence imaging (BLI). The BLI signal allowed quantification of viable cells whereas 19F MRI provided graft location and density in 3D over 4 weeks both in the healthy and stroke brain. A massive decrease in number of viable cells was detected independent of the microenvironment. This indicates that functional recovery reported in many studies of NSC implantation after stroke, is rather due to release of factors by NSCs than direct tissue replacement. In light of these indirect effects, combination of the imaging methods developed in this dissertation with other functional and structural imaging methods is suggested in order to further elucidate interactions of NSCs with the vasculature