Automated Measurement of Midline Shift in Brain CT Images and its Application in Computer-Aided Medical Decision Making

The severity of traumatic brain injury (TBI) is known to be characterized by the shift of the middle line in brain as the ventricular system often changes in size and position, depending on the location of the original injury. In this thesis, the focus is given to processing of the CT (Computer Tomography) brain images to automatically calculate midline shift in pathological cases and use it to predict Intracranial Pressure (ICP). The midline shift measurement can be divided into three steps. First the ideal midline of the brain, i.e., the midline before injury, is found via a hierarchical search based on skull symmetry and tissue features. Second, the ventricular system is segmented from the brain CT slices. Third, the actual midline is estimated from the deformed ventricles by shape matching method. The horizontal shift in the ventricles is then calculated based on the ideal midline and the actual midline in TBI CT images. The proposed method presents accurate detection of the ideal midline using anatomical features in the skull, accurate segmentation of ventricles for actual midline estimation using the information of anatomical features with a spatial template derived from a magnetic resonance imaging (MRI) scan, and an accurate estimation of the actual midline based on the robust proposed multiple regions shape matching algorithm. After the midline shift is successively measured, features including midline shift, texture information of CT images, as well as other demographic information are used to predict ICP. Machine learning algorithms are used to model the relation between the ICP and the extracted features. By using systematic feature selection and parameter selection of the learning model, promising results on ICP prediction are achieved. The prediction results also indicate the reliability of the proposed midline shift estimation

INTEGRATION OF BIOMEDICAL IMAGING AND TRANSLATIONAL APPROACHES FOR MANAGEMENT OF HEAD AND NECK CANCER

The aim of the clinical component of this work was to determine whether the currently available clinical imaging tools can be integrated with radiotherapy (RT) platforms for monitoring and adaptation of radiation dose, prediction of tumor response and disease outcomes, and characterization of patterns of failure and normal tissue toxicity in head and neck cancer (HNC) patients with potentially curable tumors. In Aim 1, we showed that the currently available clinical imaging modalities can be successfully used to adapt RT dose based-on dynamic tumor response, predict oncologic disease outcomes, characterize RT-induced toxicity, and identify the patterns of disease failure. We used anatomical MRIs for the RT dose adaptation purpose. Our findings showed that after proper standardization of the immobilization and image acquisition techniques, we can achieve high geometric accuracy. These images can then be used to monitor the shrinkage of tumors during RT and optimize the clinical target volumes accordingly. Our results also showed that this MR-guided dose adaptation technique has a dosimetric advantage over the standard of care and was associated with a reduction in normal tissue doses that translated into a reduction of the odds of long-term RT-induced toxicity. In the second aim, we used quantitative MRIs to determine its benefit for prediction of oncologic outcomes and characterization of RT-induced normal tissue toxicity. Our findings showed that delta changes of apparent diffusion coefficient parameters derived from diffusion-weighted images at mid-RT can be used to predict local recurrence and recurrence free-survival. We also showed that Ktrans and Ve vascular parameters derived from dynamic contrast-enhanced MRIs can characterize the mandibular areas of osteoradionecrosis. In the final clinical aim, we used CT images of recurrence and baseline CT planning images to develop a methodology and workflow that involves the application of deformable image registration software as a tool to standardize image co-registration in addition to granular combined geometric- and dosimetric-based failure characterization to correctly attribute sites and causes of locoregional failure. We then successfully applied this methodology to identify the patterns of failure following postoperative and definitive IMRT in HNC patients. Using this methodology, we showed that most recurrences occurred in the central high dose regions for patients treated with definitive IMRT compared with mainly non-central high dose recurrences after postoperative IMRT. We also correlated recurrences with pretreatment FDG-PET and identified that most of the central high dose recurrences originated in an area that would be covered by a 10-mm margin on the volume of 50% of the maximum FDG uptake. In the translational component of this work, we integrated radiomic features derived from pre-RT CT images with whole-genome measurements using TCGA and TCIA data. Our results demonstrated a statistically significant associations between radiomic features characterizing different tumor phenotypes and different genomic features. These findings represent a promising potential towards non-invasively tract genomic changes in the tumor during treatment and use this information to adapt treatment accordingly. In the final project of this dissertation, we developed a high-throughput approach to identify effective systemic agents against aggressive head and neck tumors with poor prognosis like anaplastic thyroid cancer. We successfully identified three candidate drugs and performed extensive in vitro and in vivo validation using orthotopic and PDX models. Among these drugs, HDAC inhibitor and LBH-589 showed the most effective tumor growth inhibition that can be used in future clinical trials

The interaction between human vision and eye movements in health and disease

Human motor behaviour depends on the successful integration of vision and eye movements. Many studies have investigated neural correlates of visual processing in humans, but typically with the eyes stationary and fixated centrally. Similarly, many studies have sought to characterise which brain areas are responsible for oculomotor control, but generally in the absence of visual stimulation. The few studies to explicitly study the interaction between visual perception and eye movements suggest strong influences of both static and dynamic eye position on visual processing and modulation of oculomotor structures by properties of visual stimuli. However, the neural mechanisms underlying these interactions are poorly understood. This thesis uses a range of fMRI methodologies such as retinotopic mapping, multivariate analsyis techniques, dynamic causal modelling and ultra high resolution imaging to examine the interactions between the oculomotor and visual systems in the normal human brain. The results of the experiments presented in this thesis demonstrate that oculomotor behaviour has complex effects on activity in visual areas, while spatial properites of visual stimuli modify activity in oculomotor areas. Specifically, responses in the lateral geniculate nucleus and early cortical visual areas are modulated by saccadic eye movements (a process potentially mediated by the frontal eye fields) and by changes in static eye position. Additionally, responses in oculomotor structures such as the superior colliculus are biased for visual stimuli presented in the temporal rather than nasal hemifield. These findings reveal that although the visual and oculomotor systems are spatially segregated in the brain, they show a high degree of integration at the neural level. This is consistent with our everyday experience of the visual world where frequent eye movements do not lead to disruption of visual continuity and visual information is seamlessly transformed into motor behaviour

Quantification of vascular perfusion in the spinal cord after injury.

Traumatic injury destroys blood vessels at the injury epicenter and is followed by local angiogenesis and regional inflammation. Healing from injury depends on vascular health because blood supply is directly responsible for the health and function of surrounding tissue. This work establishes a new method for qualitatively and quantitatively measuring the blood supply of spinal cord (SC) tissue. Systemically injecting fluorescent microspheres (FMs) and cryostat sectioning SC tissue reveals a novel and potentially powerful way of assessing blood supply. This method is easily incorporated with existing tissue processing protocols because it does not require chemical digestion of the tissue region of interest. FM blood supply measurements show that after mild contusion injury, the epicenter has less blood flow while the blood flow several millimeters rostral and caudal to the epicenter is elevated compared to uninjured controls. The time course for vascular repair after spinal cord injury (SCI) has been widely studied and this pilot experiment was carried out seven days post-injury, at which point angiogenesis has reached its zenith and vascular pruning is minimal. A custom MATLAB program is used to automatically analyze FM distribution

Entwicklung und Validierung der in vivo zeitharmonischen Ultraschall-Elastografie des menschlichen Gehirns für die klinische Anwendung

Motivation: In neurology, the determination of intracranial pressure (ICP) is of central importance for the diagnosis of brain damage. However, reliable ICP measurements are realized by invasive techniques such as lumbar puncture or surgically implanted pressure probes. Cerebral stiffness (CS) measured by elastography could be a parameter sensitive to ICP variations. However, CS is currently measured exclusively by magnetic resonance elastography, which is associated with long examinations and limited availability. Time harmonic shear wave excitation used in magnetic resonance elastography combined with transcranial ultrasound (cerebral THE) can provide reproducible and stable elastograms over a large field-of-view in real-time. Initial applications of cerebral THE in healthy volunteers during performance of the Valsalva maneuver demonstrated sensitivity of CS to blood flow and pressure changes in the brain. The goal of this PhD project was to optimize and validate cerebral THE that I previously developed to quantify CS, identify it as a marker of cerebral perfusion, and provide initial evidence for the potential clinical application of the method as a noninvasive technique for estimating ICP. Methods: To this end, I conducted two studies in healthy volunteers aimed at artificial manipulation of cerebral blood flow: (i) I investigated the effect of hypercapnia during breathing of carbon dioxide-enriched gas and (ii) the effect of dehydration and oral rehydration on CS measured by cerebral THE. Finally, I applied cerebral THE in a pilot clinical study in patients with idiopathic intracranial hypertension (IIH) who underwent lumbar puncture (LP) along with invasive quantification of cerebrospinal fluid (CSF) opening pressure and, if necessary, CSF drainage. Results: Hypercapnia increased CS by 6 ± 4% above baseline. In contrast, dehydration of healthy volunteers resulted in a decrease in CS of 4 ± 2%, whereas CS returned to baseline after oral rehydration. In patients with IIH, CS was 16 ± 5% higher than in healthy volunteers and correlated positively with CSF opening pressure (r = 0:69, p < 0:001). Approximately 30 min after LP, patients’ CS values were within the range of CS values in healthy volunteers. Conclusion: Cerebral THE proved to be a reproducible, stable imaging technique for real-time determination of CS. This project demonstrated that changes in CS are closely associated with changes in cerebral perfusion and ICP. These results suggest that cerebral THE may be a promising noninvasive diagnostic tool for determining ICP in routine clinical practice.Motivation: In der Neurologie ist die Bestimmung des intrakraniellen Drucks (ICP) von zentraler Bedeutung für die Diagnose von Hirnschäden. Zuverlässige ICP-Messungen werden jedoch durch invasive Techniken wie die Lumbalpunktion oder chirurgisch implantierte Drucksonden realisiert. Die mittels Elastografie gemessene zerebrale Steifigkeit (CS) könnte ein Parameter sein, der empfindlich auf ICP-Schwankungen reagiert. Allerdings wird die CS derzeit ausschließlich mit der Magnetresonanz-Elastografie gemessen, die mit langen Untersuchungen und begrenzter Verfügbarkeit verbunden ist. Zeitharmonische Scherwellenanregung, wie sie in der Magnetresonanz-Elastografie verwendet wird, kombiniert mit transkraniellem Ultraschall (zerebrale THE) kann reproduzierbare, stabile Elastogramme über ein großes Sichtfeld in Echtzeit liefern. Erste Anwendungen der zerebralen THE bei gesunden Probanden während der Durchführung des Valsalva-Manövers zeigten, dass die CS empfindlich auf Blutflussund Druckänderungen im Gehirn reagiert. Ziel dieses Promotionsprojekts war die Optimierung und Validierung der zerebralen THE, welche ich zuvor entwickelt habe, um CS zu quantifizieren, als Marker für zerebrale Perfusion zu identifizieren und erste Beweise für die potenzielle klinische Anwendung der Methode als nichtinvasive Technik zur Abschätzung des ICP zu liefern. Methoden: Zu diesem Zweck führte ich zwei Studien an gesunden Probanden durch, welche die künstliche Manipulation des zerebralen Blutflusses zum Ziel hatten: (i) Ich untersuchte die Auswirkung von Hyperkapnie während der Atmung von mit Kohlendioxid angereichertem Gas und (ii) die Auswirkung von Dehydrierung und oraler Rehydrierung auf die durch zerebrale THE gemessene CS. Schließlich habe ich die zerebrale THE in einer klinischen Pilotstudie bei Patienten mit idiopathischer intrakranieller Hypertension (IIH) angewandt, bei denen eine Lumbalpunktion (LP) zusammen mit einer invasiven Quantifizierung des Liquoröffnungsdrucks und, falls erforderlich, einer Liquordrainage durchgeführt wurde. Ergebnisse: Hyperkapnie erhöhte den CS um 6 4% über den Ausgangswert. Im Gegensatz dazu führte die Dehydratation gesunder Probanden zu einem Rückgang des CS um 4 2%, während der CS nach oraler Rehydrierung wieder den Ausgangswert erreichte. Bei Patienten mit IIH war die CS um 16 5% höher als bei gesunden Probanden und korrelierte positiv mit dem Liquoröffnungsdruck (r = 0:69, p < 0:001). Etwa 30 Minuten nach der LP lagen die CS Werte der Patienten im Bereich der CS Werte gesunder Probanden. Schlussfolgerung: Die zerebrale THE erwies sich als reproduzierbares, stabiles bildgebendes Verfahren zur Echtzeit-Bestimmung der CS. Dieses Projekt zeigte, dass Änderungen des CS eng mit Änderungen der zerebralen Perfusion und des ICP verbunden sind. Diese Ergebnisse deuten darauf hin, dass die zerebrale THE ein vielversprechendes nichtinvasives Diagnoseinstrument zur Bestimmung des ICP in der klinischen Routinepraxis sein könnte

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

The Electrophysiology of Resting State fMRI Networks

Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects