MRI-based parameter inference for cerebral perfusion modelling in health and ischaemic stroke.

Cerebral perfusion modelling is a promising tool to predict the impact of acute ischaemic stroke treatments on the spatial distribution of cerebral blood flow (CBF) in the human brain. To estimate treatment efficacy based on CBF, perfusion simulations need to become suitable for group-level investigations and thus account for physiological variability between individuals. However, computational perfusion modelling to date has been restricted to a few patient-specific cases. This study set out to establish automated parameter inference for perfusion modelling based on neuroimaging data and thus enable CBF simulations of groups. Magnetic resonance imaging (MRI) data from 75 healthy senior adults were utilised. Brain geometries were computed from healthy reference subjects' T1-weighted MRI. Haemodynamic model parameters were determined from spatial CBF maps measured by arterial spin labelling (ASL) perfusion MRI. Thereafter, perfusion simulations were conducted in 75 healthy cases followed by 150 acute ischaemic stroke cases representing an occlusion and CBF cessation in the left and right middle cerebral arteries. The anatomical fitness of the brain geometries was evaluated by comparing the simulated grey (GM) and white matter (WM) volumes to measurements in healthy reference subjects. Strong positive correlations were found in both tissue types (GM: Pearson's r 0.74, P<0.001; WM: Pearson's r 0.84, P<0.001). Haemodynamic parameter tuning was verified by comparing the total volumetric blood flow rate to the brain in healthy reference subjects and simulations (Pearson's r 0.89, P<0.001). In acute ischaemic stroke cases, the simulated infarct volume using a perfusion-based estimate was 197±25 ml. Computational predictions were in agreement with anatomical and haemodynamic values from the literature concerning T1-weighted, T2-weighted, and phase-contrast MRI measurements in healthy scenarios and acute ischaemic stroke cases. The acute stroke simulations did not capture small infarcts (left tail of the distribution), which could be explained by neglected compensatory mechanisms, e.g. collaterals. The proposed parameter inference method provides a foundation for group-level CBF simulations and for in silico clinical stroke trials which could assist in medical device and drug development.

Simulation-based optimisation to quantify heterogeneity of specific ventilation and perfusion in the lung by the Inspired Sinewave Test.

The degree of specific ventilatory heterogeneity (spatial unevenness of ventilation) of the lung is a useful marker of early structural lung changes which has the potential to detect early-onset disease. The Inspired Sinewave Test (IST) is an established noninvasive 'gas-distribution' type of respiratory test capable of measuring the cardiopulmonary parameters. We developed a simulation-based optimisation for the IST, with a simulation of a realistic heterogeneous lung, namely a lognormal distribution of spatial ventilation and perfusion. We tested this method in datasets from 13 anaesthetised pigs (pre and post-lung injury) and 104 human subjects (32 healthy and 72 COPD subjects). The 72 COPD subjects were classified into four COPD phenotypes based on 'GOLD' classification. This method allowed IST to identify and quantify heterogeneity of both ventilation and perfusion, permitting diagnostic distinction between health and disease states. In healthy volunteers, we show a linear relationship between the ventilatory heterogeneity versus age ([Formula: see text]). In a mechanically ventilated pig, IST ventilatory heterogeneity in noninjured and injured lungs was significantly different (p < 0.0001). Additionally, measured indices could accurately identify patients with COPD (area under the receiver operating characteristic curve is 0.76, p < 0.0001). The IST also could distinguish different phenotypes of COPD with 73% agreement with spirometry.

On the Sensitivity Analysis of Porous Finite Element Models for Cerebral Perfusion Estimation.

Computational physiological models are promising tools to enhance the design of clinical trials and to assist in decision making. Organ-scale haemodynamic models are gaining popularity to evaluate perfusion in a virtual environment both in healthy and diseased patients. Recently, the principles of verification, validation, and uncertainty quantification of such physiological models have been laid down to ensure safe applications of engineering software in the medical device industry. The present study sets out to establish guidelines for the usage of a three-dimensional steady state porous cerebral perfusion model of the human brain following principles detailed in the verification and validation (V&V 40) standard of the American Society of Mechanical Engineers. The model relies on the finite element method and has been developed specifically to estimate how brain perfusion is altered in ischaemic stroke patients before, during, and after treatments. Simulations are compared with exact analytical solutions and a thorough sensitivity analysis is presented covering every numerical and physiological model parameter. The results suggest that such porous models can approximate blood pressure and perfusion distributions reliably even on a coarse grid with first order elements. On the other hand, higher order elements are essential to mitigate errors in volumetric blood flow rate estimation through cortical surface regions. Matching the volumetric flow rate corresponding to major cerebral arteries is identified as a validation milestone. It is found that inlet velocity boundary conditions are hard to obtain and that constant pressure inlet boundary conditions are feasible alternatives. A one-dimensional model is presented which can serve as a computationally inexpensive replacement of the three-dimensional brain model to ease parameter optimisation, sensitivity analyses and uncertainty quantification. The findings of the present study can be generalised to organ-scale porous perfusion models. The results increase the applicability of computational tools regarding treatment development for stroke and other cerebrovascular conditions.

A porous circulation model of the human brain for in silico clinical trials in ischaemic stroke.

The advancement of ischaemic stroke treatment relies on resource-intensive experiments and clinical trials. In order to improve ischaemic stroke treatments, such as thrombolysis and thrombectomy, we target the development of computational tools for in silico trials which can partially replace these animal and human experiments with fast simulations. This study proposes a model that will serve as part of a predictive unit within an in silico clinical trial estimating patient outcome as a function of treatment. In particular, the present work aims at the development and evaluation of an organ-scale microcirculation model of the human brain for perfusion prediction. The model relies on a three-compartment porous continuum approach. Firstly, a fast and robust method is established to compute the anisotropic permeability tensors representing arterioles and venules. Secondly, vessel encoded arterial spin labelling magnetic resonance imaging and clustering are employed to create an anatomically accurate mapping between the microcirculation and large arteries by identifying superficial perfusion territories. Thirdly, the parameter space of the problem is reduced by analysing the governing equations and experimental data. Fourthly, a parameter optimization is conducted. Finally, simulations are performed with the tuned model to obtain perfusion maps corresponding to an open and an occluded (ischaemic stroke) scenario. The perfusion map in the occluded vessel scenario shows promising qualitative agreement with computed tomography images of a patient with ischaemic stroke caused by large vessel occlusion. The results highlight that in the case of vessel occlusion (i) identifying perfusion territories is essential to capture the location and extent of underperfused regions and (ii) anisotropic permeability tensors are required to give quantitatively realistic estimation of perfusion change. In the future, the model will be thoroughly validated against experiments.

Identifying the myogenic and metabolic components of cerebral autoregulation.

Cerebral autoregulation is the term used to describe a number of mechanisms that act together to maintain a near constant cerebral blood flow in response to changes in arterial blood pressure. These mechanisms are complex and known to be affected in a range of cerebrovascular diseases. However, it can be difficult to assign an alteration in cerebral autoregulation to one of the underlying physiological mechanisms without the use of a complex mathematical model. In this paper, we thus set out a new approach that enables these mechanisms to be related to the autoregulation behaviour and hence inferred from experimental measurements. We show that the arteriolar response is a function of just three parameters, which we term the elastic, the myogenic and the metabolic sensitivity coefficients, and that the full vascular response is dependent upon only seven parameters. The ratio of the strengths of the myogenic and the metabolic responses is found to be in the range 2.5 to 5 over a wide range of pressure, indicating that the balance between the two appears to lie within this range. We validate the model with existing experimental data both at the level of an individual vessel and across the whole vasculature, and show that the results are consistent with findings from the literature. We then conduct a sensitivity analysis of the model to demonstrate which parameters are most important in determining the strength of static autoregulation, showing that autoregulation strength is predominantly set by the arteriolar sensitivity coefficients. This new approach could be used in future studies to help to interpret the components of the autoregulation response and how they are affected under different conditions, providing a greater insight into the fundamental processes that govern autoregulation.

Modelling dynamic changes in blood flow and volume in the cerebral vasculature.

The cerebral microvasculature plays a key role in the transport of blood and the delivery of nutrients to the cells that perform brain function. Although recent advances in experimental imaging techniques mean that its structure and function can be interrogated to very small length scales, allowing individual vessels to be mapped to a fraction of 1 µm, these techniques currently remain confined to animal models. In-vivo human data can only be obtained at a much coarser length scale, of order 1 mm, meaning that mathematical models of the microvasculature play a key role in interpreting flow and metabolism data. However, there are close to 10,000 vessels even within a single voxel of size 1 mm3. Given the number of vessels present within a typical voxel and the complexity of the governing equations for flow and volume changes, it is computationally challenging to solve these in full, particularly when considering dynamic changes, such as those found in response to neural activation. We thus consider here the governing equations and some of the simplifications that have been proposed in order more rigorously to justify in what generations of blood vessels these approximations are valid. We show that two approximations (neglecting the advection term and assuming a quasi-steady state solution for blood volume) can be applied throughout the cerebral vasculature and that two further approximations (a simple first order differential relationship between inlet and outlet flows and inlet and outlet pressures, and matching of static pressure at nodes) can be applied in vessels smaller than approximately 1 mm in diameter. We then show how these results can be applied in solving flow fields within cerebral vascular networks providing a simplified yet rigorous approach to solving dynamic flow fields and compare the results to those obtained with alternative approaches. We thus provide a framework to model cerebral blood flow and volume within the cerebral vasculature that can be used, particularly at sub human imaging length scales, to provide greater insight into the behaviour of blood flow and volume in the cerebral vasculature.

A model for generating synthetic arterial blood pressure.

A new model capable of simulating many important aspects of human arterial blood pressure (ABP) is proposed. Both data-driven approach and physiological principles have been applied to describe the time series of diastolic, systolic, dicrotic notch and dicrotic peak pressure points. Major static and dynamic features of the model can be prescribed by the user, including heart rate, mean systolic and diastolic pressure, and the corresponding physiological control quantities, such as baroreflex sensitivity coefficient and Windkessel time constant. A realistic ABP generator can be used to compile a virtual database of signals reflecting individuals with different clinical conditions and signals containing common artefacts. The ABP model permits to create a platform to assess a wide range of biomedical signal processing approaches and be used in conjunction with, e.g. Kalman filters to improve the quality of ABP signals.

Mathematical model of the effect of ischemia-reperfusion on brain capillary collapse and tissue swelling.

Restoration of an adequate cerebral blood supply after an ischemic attack is a primary clinical goal. However, the blood-brain barrier may break down after a prolonged ischemia causing the fluid in the blood plasma to filtrate and accumulate into the cerebral tissue interstitial space. Accumulation of this filtration fluid causes the cerebral tissue to swell, a condition known as vasogenic oedema. Tissue swelling causes the cerebral microvessels to be compressed, which may further obstruct the blood flow into the tissue, thus leading to the no-reflow phenomenon or a secondary ischemic stroke. The actual mechanism of this however is still not fully understood. A new model is developed here to study the effect of reperfusion on the formation of vasogenic oedema and cerebral microvessel collapse. The formation of vasogenic oedema is modelled using the capillary filtration equation while vessel collapse is modelled using the tube law of microvessel. Tissue swelling is quantified in terms of displacement, which is modelled using poroelastic theory. The results show that there is an increase in tissue displacement and interstitial pressure after reperfusion. In addition, the results also show that vessel collapse can occur at high value of reperfusion pressure, low blood osmotic pressure, high cerebral capillary permeability and low cerebral capillary stiffness. This model provides insight on the formation of ischemia-reperfusion injury by tissue swelling and vessel collapse.

Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients.

Amide proton transfer (APT) imaging is a pH mapping method based on the chemical exchange saturation transfer phenomenon that has potential for penumbra identification following stroke. The majority of the literature thus far has focused on generating pH-weighted contrast using magnetization transfer ratio asymmetry analysis instead of quantitative pH mapping. In this study, the widely used asymmetry analysis and a model-based analysis were both assessed on APT data collected from healthy subjects (n = 2) and hyperacute stroke patients (n = 6, median imaging time after onset = 2 hours 59 minutes). It was found that the model-based approach was able to quantify the APT effect with the lowest variation in grey and white matter (≤ 13.8 %) and the smallest average contrast between these two tissue types (3.48 %) in the healthy volunteers. The model-based approach also performed quantitatively better than the other measures in the hyperacute stroke patient APT data, where the quantified APT effect in the infarct core was consistently lower than in the contralateral normal appearing tissue for all the patients recruited, with the group average of the quantified APT effect being 1.5 ± 0.3 % (infarct core) and 1.9 ± 0.4 % (contralateral). Based on the fitted parameters from the model-based analysis and a previously published pH and amide proton exchange rate relationship, quantitative pH maps for hyperacute stroke patients were generated, for the first time, using APT imaging.

Phase-rectified signal averaging for intrapartum electronic fetal heart rate monitoring is related to acidaemia at birth.

OBJECTIVE: Recent studies suggest that phase-rectified signal averaging (PRSA), measured in antepartum fetal heart rate (FHR) traces, may sensitively indicate fetal status; however, its value has not been assessed during labour. We determined whether PRSA relates to acidaemia in labour, and compare its performance to short-term variation (STV), a related computerised FHR feature. DESIGN: Historical cohort. SETTING: Large UK teaching hospital. POPULATION: All 7568 Oxford deliveries that met the study criteria from April 1993 to February 2008. METHODS: We analysed the last 30 minutes of the FHR and associated outcomes of infants. We used computerised analysis to calculate PRSA decelerative capacity (DC(PRSA)), and its ability to predict umbilical arterial blood pH ≤ 7.05 using receiver operator characteristic (ROC) curves and event rate estimates (EveREst). We compared DC(PRSA) with STV calculated on the same traces. MAIN OUTCOME MEASURE: Umbilical arterial blood pH ≤ 7.05. RESULTS: We found that PRSA could be measured in all cases. DC(PRSA) predicted acidaemia significantly better than STV: the area under the ROC curve was 0.665 (95% CI 0.632-0.699) for DC(PRSA), and 0.606 (0.573-0.639) for STV (P = 0.007). EveREst plots showed that in the worst fifth centile of cases, the incidence of low pH was 17.75% for DC(PRSA) but 11.00% for STV (P < 0.001). DC(PRSA) was not highly correlated with STV. CONCLUSIONS: DC(PRSA) of the FHR can be measured in labour, and appears to predict acidaemia more accurately than STV. Further prospective evaluation is warranted to assess whether this could be clinically useful. The weak correlation between DC(PRSA) and STV suggests that they could be combined in multivariate FHR analyses.

Removal of acidic pharmaceuticals within a nitrifying recirculating biofilter.

The fate of pharmaceutically active compounds (PhACs) in wastewater treatment systems is an area of increasing concern. Little research has been done to understand this issue in rural or decentralized communities. The objective of this research was to examine the ability of a bench scale nitrifying recirculating biofilter (RBF) to remove four acidic PhACs: gemfibrozil, naproxen, ibuprofen and diclofenac from secondary treated municipal wastewater at concentrations of 20 and 200µg/L. The average removals in this study were between 92 and 99% for ibuprofen, 89 and 99% for naproxen, 62 and 92% for gemfibrozil and 40 and 76% for diclofenac, which is consistent with literature. Ibuprofen and naproxen were largely removed through biological transformation; whereas gemfibrozil and diclofenac showed more variable removal, likely due to both biological transformation and sorption processes. PhAC removal in the RBFs was repeatable between trials, robust and responsive to system upsets, and the presence of PhACs as a single compound versus mixtures had no impact on PhAC removal efficiency. In summary, this study indicates that RBFs as a nitrifying stage of a multi-stage filtration process could be a viable technology for removal of some acidic pharmaceuticals in small onsite wastewater treatment facilities.

Genetic testing in inherited polyposis syndromes - how and why?

There have been recent advances in genetic testing enabling accurate diagnosis of polyposis syndromes by identifying causative gene mutations, which is essential in the management of individuals with polyposis syndrome and predictive genetic testing of their extended families. There are some similarities in clinical presentation of various polyposis syndromes, which may pose a challenge to diagnosis. In this review, we discuss the clinical presentation of the main polyposis syndromes and the process of genetic testing, including the latest advancement and future of genetic testing. We aim to reiterate the importance of genetic testing in the management of polyposis syndromes, potential pitfalls associated with genetic testing and recommendations for healthcare professionals involved with the care of polyposis patients.

The efficacy of tacrolimus and sirolimus in heavily pre-treated unresectable thymic malignancies.

BACKGROUND: Thymomas and thymic carcinomas, although uncommon, constitute a significant proportion of anterior mediastinal tumours. Systemic chemotherapy is the mainstay of treatment for inoperable or recurrent disease, but immunosuppressive therapy may provide an alternative treatment strategy. PATIENTS AND METHODS: We present a series of 18 patients diagnosed with unresectable thymic tumours, of which eight received immunosuppressive therapy following relapse after chemotherapy. RESULTS: Eight individuals were treated with primary immunotherapy after a median of 3.5 lines of chemotherapy (range 2-6 lines), of which 3 had confirmed myasthenia gravis (MG). After 3 months, 2 patients achieved a radiological partial response and 4 had stable disease. The median time to progression measured 6.8 months (CI 1.4-19.3 months). Two of the 4 patients who progressed on tacrolimus and prednisolone received sirolimus. One of these patients has stable disease (SD) at 21 months, and the other has SD at 3 months. CONCLUSIONS: Although previous case reports have related tacrolimus therapy with tumour shrinkage in patients with MG-associated invasive thymomas, these data are the first to demonstrate the efficacy of such immunosuppressive agents in a larger cohort of heavily pre-treated patients with thymic tumours. Our experience adds to the limited anecdotal evidence in the literature, and suggests that immunosuppressive agents represent a valuable additional treatment for thymic tumours.

Optimal sampling schedule for chemical exchange saturation transfer.

The sampling schedule for chemical exchange saturation transfer imaging is normally uniformly distributed across the saturation frequency offsets. When this kind of evenly distributed sampling schedule is used to quantify the chemical exchange saturation transfer effect using model-based analysis, some of the collected data are minimally informative to the parameters of interest. For example, changes in labile proton exchange rate and concentration mainly affect the magnetization near the resonance frequency of the labile pool. In this study, an optimal sampling schedule was designed for a more accurate quantification of amine proton exchange rate and concentration, and water center frequency shift based on an algorithm previously applied to magnetization transfer and arterial spin labeling. The resulting optimal sampling schedule samples repeatedly around the resonance frequency of the amine pool and also near to the water resonance to maximize the information present within the data for quantitative model-based analysis. Simulation and experimental results on tissue-like phantoms showed that greater accuracy and precision (>30% and >46%, respectively, for some cases) were achieved in the parameters of interest when using optimal sampling schedule compared with evenly distributed sampling schedule. Hence, the proposed optimal sampling schedule could replace evenly distributed sampling schedule in chemical exchange saturation transfer imaging to improve the quantification of the chemical exchange saturation transfer effect and parameter estimation.

Evaluating the use of a continuous approximation for model-based quantification of pulsed chemical exchange saturation transfer (CEST).

Many potential clinical applications of chemical exchange saturation transfer (CEST) have been studied in recent years. However, due to various limitations such as specific absorption rate guidelines and scanner hardware constraints, most of the proposed applications have yet to be translated into routine diagnostic tools. Currently, pulsed CEST which uses multiple short pulses to perform the saturation is the only viable irradiation scheme for clinical translation. However, performing quantitative model-based analysis on pulsed CEST is time consuming because it is necessary to account for the time dependent amplitude of the saturation pulses. As a result, pulsed CEST is generally treated as continuous CEST by finding its equivalent average field or power. Nevertheless, theoretical analysis and simulations reveal that the resulting magnetization is different when the different irradiation schemes are applied. In this study, the quantification of important model parameters such as the amine proton exchange rate from a pulsed CEST experiment using quantitative model-based analyses were examined. Two model-based approaches were considered - discretized and continuous approximation to the time dependent RF irradiation pulses. The results showed that the discretized method was able to fit the experimental data substantially better than its continuous counterpart, but the smaller fitted error of the former did not translate to significantly better fit for the important model parameters. For quantification of the endogenous CEST effect, such as in amide proton transfer imaging, a model-based approach using the average power equivalent saturation can thus be used in place of the discretized approximation.

Relation of fetal heart rate signals with unassignable baseline to poor neonatal state at birth.

Electronic fetal heart rates (FHR) are used to monitor fetal health during labour. The paper records are visually assessed by clinicians, but automated alternatives are being developed. Interpretation, visual or computerised, depends on assigning a baseline to identify key features such as accelerations and decelerations. However, when the FHR is unstable the baseline may be unassignable, making conventional analysis unreliable. Such instability may reflect on fetal health. If true, these segments should not be discarded but quantified, for which we have developed a numerical method. In 7,568 labours, the association between unassignable baseline and umbilical arterial blood pH ≤ 7.05 at birth (evidence of poor health) was studied retrospectively. We found a consistent increase of the risk for acidaemia with longer intervals of unassignable baseline. This is detectable at the end of the first stage of labour, but stronger at the end of the second stage: in the last 30 min of labour, the odds ratios (with respect to baseline assignable throughout this period) increased from 1.99 (15 min unassignable) to 4.9 (30 min unassignable). Computerised analysis of the FHR becomes unreliable when the baseline cannot be assigned; however, this pattern is itself a pathological feature associated with acidaemia at birth.

Vasomotion does inhibit mass exchange between axisymmetric blood vessels and tissue.

Vasomotion, the name given to the physiological phenomenon whereby blood vessel walls exhibit rhythmic oscillations in diameter, is a complex process and very poorly understood. It has been proposed as a mechanism for protecting tissue when perfusion levels are reduced, since it has experimentally been shown to occur more frequently under such conditions. However, no quantitative evidence yet exists for whether the oscillation of the wall actually has any effect on mass transport to tissue. In our previous work, it was shown that the presence of non-linearities in the governing equation could result in a significant change in time-averaged mass transport to tissue: however, it was not possible, due to the limitations of the model, to determine whether time-averaged mass transport increased or decreased. This model is extended in this paper through coupling of the one-dimensional axisymmetric mass transport equations in tissue and blood to quantify the effects of vasomotion on mass transport to tissue. The results show that over a wide parameter range, surrounding those values calculated from experimental data, vasomotion does inhibit mass transport to tissue in a one-dimensional axisymmetric blood vessel by an amount that is predominantly dependent upon the amplitude of oscillation and that increases rapidly at larger oscillation amplitudes.

Effects of arterial blood gas levels on cerebral blood flow and oxygen transport.

Near Infra-Red Spectroscopy (NIRS) is a non-invasive technique which can be used to investigate cerebral haemodynamics and oxygenation with high temporal resolution. When combined with measures of Cerebral Blood Flow (CBF), it has the potential to provide information about oxygen delivery, utilization and metabolism. However, the interpretation of experimental results is complex. Measured NIRS signals reflect both scalp and cerebral haemodynamics and are influenced by many factors. The relationship between Arterial Blood Pressure (ABP) and CBF has been widely investigated and it central to cerebral autoregulation. Changes in arterial blood gas levels have a significant effect on ABP and CBF and these relationships have been quantified previously. The relationship between ABP and NIRS signals, however, has not been fully characterized. In this paper, we thus investigate the influence of changes in arterial blood gas levels both experimentally and theoretically, using an extended mathematical model of cerebral blood flow and metabolism, in terms of the phase angle at 0.1 Hz. The autoregulation response is found to be strongly dependent upon the carbon dioxide (CO2) partial pressure but much less so upon changes in arterial oxygen saturation (SaO2). The results for phase angle sensitivity to CO2 show good agreement between experimental and theory, but a poorer agreement is found for the sensitivity to SaO2.

The response of hepatocyte cell volume to hyperthermia and its role in oedema.

A novel mathematical model for hepatocytes and surrounding volume is presented here; in addition to tracking ion transport and diffusion the new model allows for changing cell volume. Using temporally and spatially varying temperature as an input, this paper shows how differences between diffusion coefficients directly influence increases in cell volume. The multiscale nature of the model presents a possible link from established cellular equations to the observed clinical result of oedema present in thermal treatments of cancer.