Characterizing Tissue Graft Angiogenesis via Multimodal Optical Imaging

Tissue engineered scaffolds are a powerful means of healing craniofacial bone defects arising from trauma or disease. Murine models of critical-sized bone defects are especially useful in understanding the role of microenvironmental factors such as vascularization on bone regeneration. In this thesis, we review the previously employed bone graft methods used to treat orthopedic tissue defects, the transition of therapeutic approaches to tissue engineering based regimes, and the various imaging modalities which may be used to characterize osteogenesis and angiogenesis within defect sites. Additionally, we demonstrate the capability of a novel multimodality imaging platform capable of acquiring in vivo images of microvascular architecture, microvascular blood flow and tracer/cell tracking via intrinsic optical signaling (IOS), laser speckle contrast (LSC) and fluorescence (FL) imaging, respectively in a critical-sized calvarial defect model. Defects that were 4 mm in diameter were made in the calvarial regions of mice followed by the implantation of osteoconductive scaffolds loaded with human adipose-derived stem cells (ASCs) embedded in fibrin gel. Using IOS imaging, we were able to visualize microvascular angiogenesis at the graft site and extracted morphological information such as vessel radius, length, and tortuosity two weeks after scaffold implantation. FL imaging allowed us to assess functional characteristics of the angiogenic vessel bed such as time-to-peak of a fluorescent tracer, and also allowed us to track the distribution of fluorescently tagged human umbilical vein endothelial cells (HUVECs). Finally, we employed LSC to characterize the in vivo hemodynamic response and maturity of the remodeled microvessels in the scaffold microenvironment. In this thesis, we provide a methodical framework for imaging tissue engineered scaffolds, processing the images in order to extract key microenvironmental parameters, and visualizing this data in a manner that enables the characterization of the vascular phenotype and its effect on bone regeneration. Such multimodality imaging platforms can inform optimization and design of tissue engineered scaffolds and elucidate the factors that promote enhanced vascularization and bone formation

A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI.

DSC-MRI analysis is based on tracer kinetic theory and typically involves the deconvolution of the MRI signal in tissue with an arterial input function (AIF), which is an ill-posed inverse problem. The current standard singular value decomposition (SVD) method typically underestimates perfusion and introduces non-physiological oscillations in the resulting residue function. An alternative vascular model (VM) based approach permits only a restricted family of shapes for the residue function, which might not be appropriate in pathologies like stroke. In this work a novel deconvolution algorithm is presented that can estimate both perfusion and residue function shape accurately without requiring the latter to belong to a specific class of functional shapes. A control point interpolation (CPI) method is proposed that represents the residue function by a number of control points (CPs), each having two degrees of freedom (in amplitude and time). A complete residue function shape is then generated from the CPs using a cubic spline interpolation. The CPI method is shown in simulation to be able to estimate cerebral blood flow (CBF) with greater accuracy giving a regression coefficient between true and estimated CBF of 0.96 compared to 0.83 for VM and 0.71 for the circular SVD (oSVD) method. The CPI method was able to accurately estimate the residue function over a wide range of simulated conditions. The CPI method has also been demonstrated on clinical data where a marked difference was observed between the residue function of normally appearing brain parenchyma and infarcted tissue. The CPI method could serve as a viable means to examine the residue function shape under pathological variations

A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI.

DSC-MRI analysis is based on tracer kinetic theory and typically involves the deconvolution of the MRI signal in tissue with an arterial input function (AIF), which is an ill-posed inverse problem. The current standard singular value decomposition (SVD) method typically underestimates perfusion and introduces non-physiological oscillations in the resulting residue function. An alternative vascular model (VM) based approach permits only a restricted family of shapes for the residue function, which might not be appropriate in pathologies like stroke. In this work a novel deconvolution algorithm is presented that can estimate both perfusion and residue function shape accurately without requiring the latter to belong to a specific class of functional shapes. A control point interpolation (CPI) method is proposed that represents the residue function by a number of control points (CPs), each having two degrees of freedom (in amplitude and time). A complete residue function shape is then generated from the CPs using a cubic spline interpolation. The CPI method is shown in simulation to be able to estimate cerebral blood flow (CBF) with greater accuracy giving a regression coefficient between true and estimated CBF of 0.96 compared to 0.83 for VM and 0.71 for the circular SVD (oSVD) method. The CPI method was able to accurately estimate the residue function over a wide range of simulated conditions. The CPI method has also been demonstrated on clinical data where a marked difference was observed between the residue function of normally appearing brain parenchyma and infarcted tissue. The CPI method could serve as a viable means to examine the residue function shape under pathological variations

Acute ischaemic stroke-multimodal imaging and stroke outcomes

Introduction: Acute stroke Imaging plays a crucial role in understanding the cerebral tissue states and multimodal imaging with perfusion, collaterals and vessel occlusion provides more information on tissue dynamics in individual patient which can be useful for tailored treatments and prognosis. Perfusion parameters and their validation are important in achieving clinical practicality. A novel tissue parameter, Capillary transit time heterogeneity has been suggested to identify micro vascular flow patterns. The clinical utility of this is not yet established. Stroke outcome is dependant not only on the imaging parameters but also on patient demographics, co morbidities and probably on complex socio-economic and other unidentified patient factors. Methods: Using a database of single centre multi modal imaging I derived perfusion metrics in commercial software (MIstar). I conducted a few different analyses on: penumbra relationship with time, collaterals; haemorrhage, oedema relationship with recanalisation; Safety in Stroke Thrombolysis (SITS) registry Stroke outcomes; Deriving a new perfusion parameter called ‘Capillary transit time heterogeneity (CTTH)’ and comparing the values in different tissue compartments. Results: In a cross-sectional sample imaged within 6h, neither the proportions of penumbral tissue nor “target mismatch” varied by time from onset. A trend for reducing penumbra proportion only among those with poor collaterals may have pathophysiological and therapeutic importance. Among patients treated with IV thrombolysis, 24h recanalisation was not independently associated with significant early (24h) vasogenic oedema or significant haemorrhage, although incidence of HI/HI2 ICH was higher. Large ischaemic core was associated with both significant brain oedema and poor outcome. There was no interaction of recanalisation and large core lesions for any imaging outcomes. Early major clinical improvement as a marker of probable early reperfusion was associated with lower incidence of both significant haemorrhage and oedema. In SITS registry study, poorer 90 day outcomes after IV thrombolysis occurred frequently at a hospital (Southern General Hospital) that accepted secondary transfer patients compared to a hospital in the same city (Western Infirmary Glasgow) that did not routinely take such patients. Capillary transit time heterogeneity (CTTH) voxel wise maps were derived successfully using “vascular model” in Brain Lab, Arhus, Denmark. The CTTH values are closely related with MTT. There is no significant difference of CTTH between Core, penumbra. Conclusion: Multimodal imaging can provide us with valuable information on understanding ischemic brain tissue, predict patient outcomes in stroke. Patient imaging and clinical outcomes depend on recanalisation, and patient factors. A novel perfusion parameter, CTTH has been successfully derived and its utility and validity is yet to be evaluated