Advanced signal processing methods in dynamic contrast enhanced magnetic resonance imaging

Tato dizertační práce představuje metodu zobrazování perfúze magnetickou rezonancí, jež je výkonným nástrojem v diagnostice, především v onkologii. Po ukončení sběru časové sekvence T1-váhovaných obrazů zaznamenávajících distribuci kontrastní látky v těle začíná fáze zpracování dat, která je předmětem této dizertace. Je zde představen teoretický základ fyziologických modelů a modelů akvizice pomocí magnetické rezonance a celý řetězec potřebný k vytvoření obrazů odhadu parametrů perfúze a mikrocirkulace v tkáni. Tato dizertační práce je souborem uveřejněných prací autora přispívajícím k rozvoji metodologie perfúzního zobrazování a zmíněného potřebného teoretického rozboru.This dissertation describes quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), which is a powerful tool in diagnostics, mainly in oncology. After a time series of T1-weighted images recording contrast-agent distribution in the body has been acquired, data processing phase follows. It is presented step by step in this dissertation. The theoretical background in physiological and MRI-acquisition modeling is described together with the estimation process leading to parametric maps describing perfusion and microcirculation properties of the investigated tissue on a voxel-by-voxel basis. The dissertation is divided into this theoretical analysis and a set of publications representing particular contributions of the author to DCE-MRI.

Scaling behavior of drug transport and absorption in in silico cerebral capillary networks

Drug delivery to the brain is challenging due to the presence of the blood-brain barrier. Mathematical modeling and simulation are essential tools for the deeper understanding of transport processes in the blood, across the blood-brain barrier and within the tissue. Here we present a mathematical model for drug delivery through capillary networks with increasingly complex topologies with the goal to understand the scaling behavior of model predictions on a coarse-to-fine sequence of grids. We apply our model to the delivery of L-Dopa, the primary drug used in the therapy of Parkinson\u27s Disease. Our model replicates observed blood flow rates and ratios between plasma and tissue concentrations. We propose an optimal network grain size for the simulation of tissue volumes of 1 cm3 that allows to make reliable predictions with reasonable computational costs

Supporting measurements or more averages? How to quantify cerebral blood flow most reliably in 5 minutes by arterial spin labeling

Purpose To determine whether sacrificing part of the scan time of pseudo-continuous arterial spin labeling (PCASL) for measurement of the labeling efficiency and blood T1 is beneficial in terms of CBF quantification reliability. Methods In a simulation framework, 5-minute scan protocols with different scan time divisions between PCASL data acquisition and supporting measurements were evaluated in terms of CBF estimation variability across both noise and ground truth parameter realizations taken from the general population distribution. The entire simulation experiment was repeated for a single-post-labeling delay (PLD), multi-PLD, and free-lunch time-encoded (te-FL) PCASL acquisition strategy. Furthermore, a real data study was designed for preliminary validation. Results For the considered population statistics, measuring the labeling efficiency and the blood T1 proved beneficial in terms of CBF estimation variability for any distribution of the 5-minute scan time compared to only acquiring ASL data. Compared to single-PLD PCASL without support measurements as recommended in the consensus statement, a 26%, 33%, and 42% reduction in relative CBF estimation variability was found for optimal combinations of supporting measurements with single-PLD, free-lunch, and multi-PLD PCASL data acquisition, respectively. The benefit of taking the individual variation of blood T1 into account was also demonstrated in the real data experiment. Conclusions Spending time to measure the labeling efficiency and the blood T1 instead of acquiring more averages of the PCASL data proves to be advisable for robust CBF quantification in the general population

Mathematical methods for modeling the microcirculation

The microcirculation plays a major role in maintaining homeostasis in the body. Alterations or dysfunctions of the microcirculation can lead to several types of serious diseases. It is not surprising, then, that the microcirculation has been an object of intense theoretical and experimental study over the past few decades. Mathematical approaches offer a valuable method for quantifying the relationships between various mechanical, hemodynamic, and regulatory factors of the microcirculation and the pathophysiology of numerous diseases. This work provides an overview of several mathematical models that describe and investigate the many different aspects of the microcirculation, including geometry of the vascular bed, blood flow in the vascular networks, solute transport and delivery to the surrounding tissue, and vessel wall mechanics under passive and active stimuli. Representing relevant phenomena across multiple spatial scales remains a major challenge in modeling the microcirculation. Nevertheless, the depth and breadth of mathematical modeling with applications in the microcirculation is demonstrated in this work. A special emphasis is placed on models of the retinal circulation, including models that predict the influence of ocular hemodynamic alterations with the progression of ocular diseases such as glaucoma

Modeling and simulation of blood circulation and perfusion

Numeriske simuleringer har hatt vesentlig betydning for vår forståelse av perfusjon og blodsirkulasjon, og simuleringer gir også viktig innsikt under utviklingen av medisinske anvendelser. Teknologiske fremskritt har muliggjort bruken av mer realistiske modeller, ikke bare i form av mer kompleks fysikk, men også ved at en kan studere sirkulasjonen i hele organer. Disse kjennetegnene er ofte av interesse da fysiologiske egenskaper er forskjellige på tvers av romlige størrelsesordener. Denne avhandlingen fokuserer på modellering og simulering av blodstrøm, sporstofftransport og perfusjon i organvev. De fysiske prosessene er uttrykt i en flerskala strømningsmodell der segmenterte arterier og vener danner en nettverksmodell for vaskulær strømning, og som er knyttet til en mikrosirkulasjonsmodell. Den ikkeobserverbare vaskulaturen beskrevet av modellen simuleres både med en kontinuerlig og en diskretisert tilnærming. Vi presenterer et flerskala rammeverk for å studere blodsirkulasjon. Det nytenkende aspektet ved rammeverket består i å kombinere en eksisterende hybrid strømningsmodell for flerskala sirkulasjon med vaskulærfremkalte ikke-lineariteter som har opphav i bl.a. veggelastisiteten og kurvaturen til blodkarene. Anvendelsen av en passende betingelse fra lineær algebra gjør at vi effektivt kan løse det tilknyttede ikke-lineære systemet ved bruk av en iterativ Newtons metode, og det relativt enkle rammeverket beskriver slik blodsirkulasjon i et komplekst fysisk domene med en lav beregningsmessig kostnad. Modellene og deres tilhørende implementeringer presenteres i artiklene som utgjør Del II i avhandlingen. Her foreslår vi et rammeverk for å generere digitale fantomer for avbildning av perfusjon, og ved å evaluere kinetikkmodeller for sporstoff demonstrerer vi de betydelige verdiene som finnes i etterbehandling av medisinske data. I tillegg undersøker vi optimale vaskulære nettverk som avslører en kompleks gjensidig avhengighet mellom geometrien til det vaskulære nettverket, kapillærene og organene. Resultatene fra denne avhandlingen bidrar til en bedre forståelse av blodperfusjonsmodeller i vev og potensialet til disse, samt potensialet som vitenskapelig databehandling har i medisinske anvendelser utover perfusjonsavbildning.Numerical simulations have become essential for understanding blood circulation and perfusion, as well as providing important insights for medical applications. More realistic models have become possible with technological advances, not only in the form of more complex physics, but also in the flow detail of an entire organ circulation. These characteristics are frequently of interest because blood vessels at different spatial scales have different physiological properties. This thesis focuses on the modeling and simulations of blood flow, tracer transport, and perfusion in an organ tissue. The physical processes are expressed in a multiscale flow model with segmented arteries and veins forming a vascular network flow model that is connected to a microcirculation model. The unobservable vasculature, including small vessels and capillaries, represented by the connection model, is simulated by using a continuum and discrete approach. A multiscale framework for solving blood circulation is presented. The novelty of this framework comes from combining an existing hybrid flow model for a multiscale circulation with vasculature-induced nonlinearities such as vessel wall elasticity and vessel curvature. By using an appropriate linear algebra precondition, the corresponding nonlinear system can be efficiently solved by using an iterative Newton method. This allows us to formulate more realistic blood circulation in a complex physical domain by employing a relatively simple framework with a low computational cost. The models and their implementation are presented in the papers that constitute Part II of this thesis. In the paper section, we propose a framework to generate a digital phantom for perfusion imaging. Moreover, we evaluate tracer kinetic models demonstrating the significant value of post-processing of medical data. We also investigate optimal vascular networks revealing a complex interdependence between the geometry of the vascular network, the capillary bed and organ shape. The results of this thesis contribute to a better understanding of blood perfusion models in tissue and their potential, as well as the potential of scientific computing, for medical applications not limited to perfusion imaging.Doktorgradsavhandlin

Kinetic modeling in the context of cerebral blood flow quantification by H215O positron emission tomography: The meaning of the permeability coefficient in Renkin–Crone׳s model revisited at capillary scale.

One the one hand, capillary permeability to water is a well-defined concept in microvascular physiology, and linearly relates the net convective or diffusive mass fluxes (by unit area) to the differences in pressure or concentration, respectively, that drive them through the vessel wall. On the other hand, the permeability coefficient is a central parameter introduced when modeling diffusible tracers transfer from blood vessels to tissue in the framework of compartmental models, in such a way that it is implicitly considered as being identical to the capillary permeability. Despite their simplifying assumptions, such models are at the basis of blood flow quantification by H215O Positron Emission Tomography. In the present paper, we use fluid dynamic modeling to compute the transfers of H215O between the blood and brain parenchyma at capillary scale. The analysis of the so-obtained kinetic data by the Renkin-Crone model, the archetypal compartmental model, demonstrates that, in this framework, the permeability coefficient is highly dependent on both flow rate and capillary radius, contrarily to the central hypothesis of the model which states that it is a physiological constant. Thus, the permeability coefficient in Renkin-Crone's model is not conceptually identical to the physiologic permeability as implicitly stated in the model. If a permeability coefficient is nevertheless arbitrarily chosen in the computed range, the flow rate determined by the Renkin-Crone model can take highly inaccurate quantitative values. The reasons for this failure of compartmental approaches in the framework of brain blood flow quantification are discussed, highlighting the need for a novel approach enabling to fully exploit the wealth of information available from PET data

Quantitative Magnetic Resonance Imaging of Tissue Microvasculature and Microstructure in Selected Clinical Applications

This thesis is based on four papers and aims to establish perfusion and diffusion measurements with magnetic resonance imaging (MRI) in selected clinical applications. While structural imaging provides invaluable geometric and anatomical information, new disease relevant information can be obtained from measures of physiological processes inferred from advanced modelling. This study is motivated by clinical questions pertaining to diagnosis and treatment effects in particular patient groups where inflammatory processes are involved in the disease. Paper 1 investigates acquisition parameters in dynamic contrast enhanced (DCE)-MRI of the temporomandibular joint (TMJ) with possible involvement of juvenile idiopathic arthritis. High level elastic motion correction should be applied to DCE data from the TMJ, and the DCE data should be acquired with a sample rate of at least 4 s. Paper 2 investigates choices of arterial input functions (AIFs) in dynamic susceptibility contrast (DSC)-MRI in brain metastases. AIF shapes differed across patients. Relative cerebral blood volume estimates differentiated better between perfusion in white matter and grey matter when scan-specific AIFs were used than when patient-specific AIFs and population-based AIFs were used. Paper 3 investigates DSC-MRI perfusion parameters in relation to outcome after stereotactic radiosurgery (SRS) in brain metastases. Low perfusion prior to SRS may be related to unfavourable outcome. Paper 4 applies free water (FW) corrected diffusion MRI to characterise glioma. Fractional anisotropy maps of the tumour region were significantly impacted by FW correction. The estimated FW maps may also contribute to a better description of the tumour. Although there are challenges related to post-processing of MRI data, it was shown that the advanced MRI methods applied can add to a more accurate description of the TMJ and of brain lesions.Doktorgradsavhandlin

The costs and benefits of estimating T-1 of tissue alongside cerebral blood flow and arterial transit time in pseudo-continuous arterial spin labeling

Multi-post-labeling-delay pseudo-continuous arterial spin labeling (multi-PLD PCASL) allows for absolute quantification of the cerebral blood flow (CBF) as well as the arterial transit time (ATT). Estimating these perfusion parameters from multi-PLD PCASL data is a non-linear inverse problem, which is commonly tackled by fitting the single-compartment model (SCM) for PCASL, with CBF and ATT as free parameters. The longitudinal relaxation time of tissue T-1t is an important parameter in this model, as it governs the decay of the perfusion signal entirely upon entry in the imaging voxel. Conventionally, T-1t is fixed to a population average. This approach can cause CBF quantification errors, as T-1t can vary significantly inter- and intra-subject. This study compares the impact on CBF quantification, in terms of accuracy and precision, of either fixing T-1t, the conventional approach, or estimating it alongside CBF and ATT. It is shown that the conventional approach can cause a significant bias in CBF. Indeed, simulation experiments reveal that if T-1t is fixed to a value that is 10% off its true value, this may already result in a bias of 15% in CBF. On the other hand, as is shown by both simulation and real data experiments, estimating T-1t along with CBF and ATT results in a loss of CBF precision of the same order, even if the experiment design is optimized for the latter estimation problem. Simulation experiments suggest that an optimal balance between accuracy and precision of CBF estimation from multi-PLD PCASL data can be expected when using the two-parameter estimator with a fixed T-1t value between population averages of T-1t and the longitudinal relaxation time of blood T-1b

The roles of cerebral blood flow, capillary transit time heterogeneity, and oxygen tension in brain oxygenation and metabolism

Normal brain function depends critically on moment-to-moment regulation of oxygen supply by the bloodstream to meet changing metabolic needs. Neurovascular coupling, a range of mechanisms that converge on arterioles to adjust local cerebral blood flow (CBF), represents our current framework for understanding this regulation. We modeled the combined effects of CBF and capillary transit time heterogeneity (CTTH) on the maximum oxygen extraction fraction (OEFmax) and metabolic rate of oxygen that can biophysically be supported, for a given tissue oxygen tension. Red blood cell velocity recordings in rat brain support close hemodynamic–metabolic coupling by means of CBF and CTTH across a range of physiological conditions. The CTTH reduction improves tissue oxygenation by counteracting inherent reductions in OEFmax as CBF increases, and seemingly secures sufficient oxygenation during episodes of hyperemia resulting from cortical activation or hypoxemia. In hypoperfusion and states of blocked CBF, both lower oxygen tension and CTTH may secure tissue oxygenation. Our model predicts that disturbed capillary flows may cause a condition of malignant CTTH, in which states of higher CBF display lower oxygen availability. We propose that conditions with altered capillary morphology, such as amyloid, diabetic or hypertensive microangiopathy, and ischemia–reperfusion, may disturb CTTH and thereby flow-metabolism coupling and cerebral oxygen metabolism