Quantitative predictions of cerebral arterial labeling employing neural network ensemble orchestrate precise investigation in brain frailty of cerebrovascular disease

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 협동과정 뇌과학전공, 2023. 2. 김상윤서우근(공동지도교수).Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the segmentation-stacking method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each images 90–99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99–1.00 [0.97–1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91–100%; middle cerebral arteries, 82–98%; anterior cerebral arteries, 88–100%; posterior cerebral arteries, 87–100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90–99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Machine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding cerebrovascular disease.CHAPTER 1. AUTOMATED IN-DEPTH CEREBRAL ARTERIAL LABELING USING CEREBROVASCULAR VASCULATURE REFRAMING AND DEEP NEURAL NETWORKS 8 1.1. INTRODUCTION 8 1.2.1. Study design and subjects 9 1.2.2. Imaging preparation 11 1.2.2.1. Magnetic resonance machine 11 1.2.2.2. Magnetic resonance sequence 11 1.2.2.3. Region growing 11 1.2.2.4. Feature extraction 11 1.2.3. Reframing hierarchical cerebrovasculature 12 1.2.4. Classification method development 14 1.2.4.1. Two-step modeling 14 1.2.4.2. Validation 16 1.2.4.3. Statistics 16 1.2.4.4. Data availability 16 1.3. RESULTS 16 1.3.1. Subject characteristics 16 1.3.2. Vascular component characteristics 21 1.3.3. Testing the appropriateness of the reframed vascular structure 24 1.3.4. Step 1 modeling: chunk 24 1.3.5. Step 2 modeling: branch 26 1.3.6. Vascular morphological features according to the vascular risk factors 31 1.3.7. The profiles of geometric feature vectors weighted on deep neural networks 31 1.4. DISCUSSION 35 1.4.1. The role of neural networks in this study 36 1.4.2. Paradigm-shifting vascular unit reframing 36 1.4.3. Limitations and future directions 37 1.5. CONCLUSIONS 38 1.6. ACKNOWLEDGEMENTS 38 1.7. FUNDING 39 BIBLIOGRAPHY 40석

Circle of Willis variants and cerebrovascular health: Representations, prevalences, functions and related consequences. Incomplete anatomy and changes to flow appear to induce more unfavourable health outcomes

Background: The Circle of Willis (CoW) is a circular structure of arteries in which most of the blood flowing to our brains pass through. The structure has primarily been regarded as important for its ability to redistribute blood flow in case of acute arterial occlusion, but may also have a role in dampening the pressure gradient in cerebral blood flow. The CoW anatomy also varies considerably, where its segments can be missing or thinner than normal, and therefore appears as a risk factor for cerebrovascular health. Objectives: To describe and report (I) the observed CoW variants and anatomy, and also examine the incomplete CoW variants’ associations to (II) white matter hyperintensities (WMH) and (III) saccular intracranial aneurysms (IA) compared to the complete CoW variant. Methods: Participants were invited from The Seventh Tromsø Study of which 1878 underwent magnetic resonance imaging. From the scans, CoW variants were semiautomatically classified. Likewise, WMH was automatically segmented and IAs were manually ascertained by radiologists. Results: The complete CoW is not very prevalent in participants older than 40 years old, and our findings suggest that the CoW becomes more incomplete with older age. Furthermore, incomplete CoW variants were not associated with increased WMH volume compared to the complete CoW variant. Incomplete CoW variants were associated increased odds of IA presence compared to the complete CoW variant. Conclusion: The results indicate that a complete CoW variant is not common in adults and elderly, which may have unfortunate consequences when incomplete CoW variants are associated with increased prevalence of IAs. Fortunately, not all results imply unfavourable outcomes, but further study of the CoW changes and possible effects of the variants over time are required.Bakgrunn: Willis Sirkel (CoW) er en sirkulær struktur av arterier i bunnen av hjernen som det meste av blodet går igjennom på tur til hjernen. Strukturen har vært antatt viktig for dens evner til å omdisponere blod i tilfellet arterier går tett, men i nyere tid har det også blitt foreslått at strukturen kan være viktig for å dempe pulstrykket i hjernen fra hjertet. Anatomien til CoW varierer mye, der segmenter mangler eller er tynnere enn normalt, og framstår dermed som et mulig risikomoment for hjernehelsen. Mål: Å beskrive (I) CoW varianter og anatomi. Analysere ufullstendige CoW varianters assosiasjoner til (II) vevsskader i hjernens indre som kalles hvit materie hyperintensiteter (WMH) og (III) sakkulære intrakranielle aneurismer (IA) sammenliknet med den fullstendige CoW varianten. Metoder: Deltakere ble invitert fra den Syvende Tromsøundersøkelsen hvorav 1878 ble tatt hjernebilder av med magnetresonans. Fra disse bildene ble CoW anatomi klassifisert. Likeså ble WMH automatisk segmentert og IA påvist av radiologer. Resultater: Den fullstendige CoW var ikke vanlig blant deltakerne eldre enn 40 år, og vi observerte også at CoW anatomien ble mer og mer ufullstendig hos eldre. Videre var ufullstendige CoW varianter ikke assosiert med høyere forekomst av WMH sammenliknet med den fullstendige CoW. Videre var ufullstendige CoW varianter assosiert med forhøyet odds for å ha IA sammenliknet med den fullstendige CoW. Konklusjon: Resultatene antyder at en fullstendig CoW ikke er spesielt vanlig hos voksne og eldre, noe som kan få uheldige følger når ufullstendige CoW er assosiert med økt forekomst av IA. Heldigvis antyder ikke alle resultatene negative følger, men mer forskning på CoW endringer og mulige effekter av anatomien over tid behøves for å stadfeste resultatene

Measuring blood flow and pulsatility with MRI: optimisation, validation and application in cerebral small vessel disease

Cerebral small vessel disease (SVD) is the breakdown of the small blood vessels of the brain, leading to many cases of stroke and dementia. The pathophysiology of SVD is largely unknown, although several mechanisms have been suggested. One such mechanism is the role of increased blood flow pulsatility into the brain, caused by vessel stiffening, leading to damage of the microvasculature. Magnetic resonance imaging (MRI) allows us to non-invasively measure blood flow and velocity using a technique called phase contrast-MRI – traditionally used with 2D slices across the vessel(s) of interest. An advanced form of phase-contrast MRI, known as 4D flow, has emerged in recent years that allows for a volume of data to be acquired, containing velocity information in all directions. However, to keep scan times practical when collecting this amount of data, spatiotemporal resolution has to be sacrificed. The main aim of this thesis was to assess 4D flow’s capabilities, including comparing it to the more well-established 2D method in healthy volunteers, patients, and phantom experiments, so as to better understand its role in investigating SVD. Another aim was to learn more about the role of flow and pulsatility in SVD development in patients using data acquired in the longitudinal Mild Stroke Study 3 (MSS3). Firstly, I systematically reviewed studies that have assessed the human brain using 4D flow. Across 61 relevant studies, I found a general consensus for the current use of the technique in this context. I then optimised the Siemens prototype 4D flow sequence (N = 11 healthy volunteers), testing different parameters to find the combination that best balanced scan quality and duration. I then assessed the test-retest repeatability and intra-rater reliability of both 2D and 4D methods (N = 11 healthy volunteers), as well as differences between them. Following this, I performed the same 4D-2D comparison on SVD patients (N = 10). Absolute flow measurements using 4D flow were shown to have moderate repeatability and reliability, while flow pulsatility measurements showed acceptable repeatability and reliability. Furthermore, 2D arterial pulsatility was measured higher than with 4D, while 4D often measured higher flow rates than 2D. 4D flow was shown to be feasible when used on SVD patients, with no noticeable issues caused by potential patient movement. Flow data analysis from the longitudinal SVD study MSS3 showed that intracranial pulsatility is associated with cross-sectional SVD lesion volume but not longitudinal lesion growth, with stronger associations seen in the arteries of the neck compared to the venous sinuses

A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries

Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64-68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling