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Imaging microvascular structure with contrast enhanced MRI

Imaging Science and Biomedical Engineering, Department of Medicine, Stopford Building, University of Manchester, Oxford Road, Manchester M13 9PT, UK

View larger version (23K): [in a new window]   Figure 1. Diagramatic representation of the distribution of contrast media within a voxel. Contrast molecules (black dots) will enter within the plasma and their delivery will be controlled by the plasma concentration (Cp) and by the flow of blood through the voxel (F). Leakage will occur into the extravascular extracellular space whose fractional value is expressed as the variable ve. Current models assume that contrast leakage does not occur into the intracellular space (vi). The leakage of contrast will be governed by the concentration difference between the plasma and the extracellular extravascular space and by the permeability and surface area of the capillary endothelia which is expressed as permeability–surface area product (PS).

View larger version (36K): [in a new window]   Figure 2. (a) Dynamic time course series through the brain using a T2* weighted acquisition during passage of bolus of contrast agent. Note the decrease in signal intensity within the brain during the passage of the contrast bolus. (b) Signal intensity changes in a large blood vessel (A), grey matter (B) and white matter (C). (c) Calculated changes in contrast concentration derived from the vascular and grey matter curves. Open circles and crosses represent the original data measurements. The solid lines show the result of a gamma variate curve fit to each of the data sets. Note that the curve fitting procedure eliminates the effects of contrast re-circulation in the later phases of the images. (Courtesy of Dr F Calamante.)

View larger version (51K): [in a new window]   Figure 3. Calculated parametric images of cerebral blood volume (CBV), cerebral blood flow and mean transit time (MTT) from a normal healthy volunteer. The images were acquired using T2* weighted dynamic imaging techniques. Data were analysed using a gamma variate curve fit analysis to allow calculation of each parameter. Note the clear distinction between grey matter, white matter and blood vessels in both the CBV and flow maps. In the MTT map slight prolongation of the MTT can be seen in the watershed areas.

View larger version (63K): [in a new window]   Figure 4. (a) Time course data from a region of interest in an enhancing meningioma following injection of contrast agent. Images were acquired using a T2* weighted acquisition protocol. Note the initial dip in signal intensity as a contrast agent enters blood vessels within the voxel followed by a rapid enhancement effect as contrast leaks into surrounding tissues. The preliminary drop is due largely to T2* (susceptibility) effects in the image whereas the rise is due to residual T1 weighted (relaxivity) effect. (b) Signal change from the same region of interest from a second contrast injection performed several minutes after the first. The pre-enhancement with the initial dose of contrast has saturated the T1 sensitivity of the tissues and only the susceptibility effects are now seen. Note the significant signal drop due to intravascular contrast and the failure of the signal to return to the baseline during the re-circulation phase. (c) A calculated regional relative cerebral blood volume (rCBV) map of a large glioma. Note the high CBV in the large feeding vessels and draining veins. Central areas of poor perfusion are seen as negative values. (d) The relationship between median rCBV and tumour grade in a series of gliomas. Note the clear grade specific relationship of CBV calculated in this way.

View larger version (18K): [in a new window]   Figure 5. Demonstrates the concept of the relative re-circulation parameter. The dotted line shows the expected changes in contrast concentration within a voxel which contains no contrast leakage. The initial peak is due to the passage of the contrast agent bolus and the second peak represents the second passage and subsequent re-circulation. The black line shows typical data observed from enhancing tumours. The elevation of the re-circulation phase data reflects slow perfusion and/or irregular flow through areas of low perfusion pressure and is equivalent to the tumour blush seen on conventional angiography. Since the expected shape of the first pass curve can be estimated by gamma variate fitting the difference between this and the actual measured value can be estimated (hatched area). This measure can then be used as an indicator of flow irregularity.

View larger version (65K): [in a new window]   Figure 6. Calculated images from dynamic T2* weighted perfusion data in (a) a large meningioma and (b) a glioblastoma. The black and white scales illustrate the relative cerebral blood volume (rCBV) which can be seen to be elevated throughout the meningioma and also in the peripheral component of the glioma. Red areas illustrate abnormally high values of relative recirculation. None are seen in the meningioma, which is classically characterized by well-organized and well-developed vascular structures. In the glioblastoma a number of areas are seen in the deep part of the enhancing portion of the tumour adjacent to the non-enhancing components. This corresponds to areas where tumour staining would be seen on angiography and where flow can be anticipated to be irregular and slow.

View larger version (31K): [in a new window]   Figure 7. (a) The distribution of pixel values of relative re-circulation (rR) in a patient with a grade 3 glioma. Note that the distribution of the values fits normal distribution with a skewness value of 0.45. (b) A similar distribution graph from a patient with a glioblastoma multiforme. Note the marked skew of the data to the right side due to an increase of pixels with high values. This gives rise to a skewed distribution with a skewness of 1.22. (c) The distribution of rR values within a group of gliomas of varying grades. A clear relationship between grade can be identified with far higher values of skewness in grade 4 tumours than in grade 2 and 3. (d) A scattergram showing the distribution of mean values of relative cerebral blood volume (rCBV) on the x-axis and skewness of rR on the y-axis. The points represent grade 2 (triangles), grade 3 (diamonds) and grade 4 (circles) gliomas.

View larger version (34K): [in a new window]   Figure 8. T1 weighted MRI of the distal femur osteosarcoma after injection of contrast medium. The study was performed following initial chemotherapy and prior to tumour resection. Regions of representative tissue types are shown with corresponding dynamic signals for the four areas of the tumour (A) and necrotic central core, (B) viable soft tissue component with increased extracellular space, (C) viable marrow with stable microcirculation and (D) rapidly proliferating tumour. Image courtesy of Professor June Taylor, University of Utah.

View larger version (11K): [in a new window]   Figure 9. Scattergram showing the distribution of the values of ve and Ktrans for a group of gliomas (squares), meningiomas (diamonds) and vestibular schwannomas (triangles). Note that vestibular schwannomas can be distinguished by increase in extravascular extracellular space fraction and that more aggressive forms of meningioma and glioma are characterized by higher values of Ktrans. This analysis was performed using a standard 2-compartment pharmacokinetic model.

View larger version (16K): [in a new window]   Figure 10. Example curve fits from data in (a) a grade 3 and (b) a grade 4 glioma. The fitting has been performed using the adiabatic model described by St Lawrence and Lee [40] and therefore gives individual estimates of flow (F), permeability–surface area product (PS), fractional vascular volume (v(b)) and fractional extravascular extracellular space volume (v(e)). (Cartesy of Dr D Buckley, University of Manchester.)

View larger version (53K): [in a new window]   Figure 11. Maps of (a) Ktrans and (b) kfp on a patient with a glioblastoma multiforme. Note the standard conventional 2-compartment model (a) shows apparent areas of extremely high permeability in the region of feeding and draining blood vessels. Small blood vessels throughout the brain also appear as areas of high permeability. In the modified model (b), contributions from intravascular contrast are explicity modelled. This image of kfp is therefore free of these pseudopermeability effects. Elevated areas of kfp can be seen within the tumour and in the choroid plexus and peripineal organs where the blood–brian barrier is not intact.

View larger version (99K): [in a new window]   Figure 12. Maps of (a) kfp and (b) relative blood volume in a patient with a hypervascular hepatic metastasis. The lower images were acquired 24 h after administration of vascular endothelial growth factor (VEGF) antagonist and show significant decreases in both Ktrans and cerebral blood volume within the tumour.

View larger version (18K): [in a new window]   Figure 13. Data using the first pass pharmacokinetic model described by Li et al [43] showing reproducibility of mean values of (a) kfp and (b) the 97.5th centile value of the same data. These data were taken from five patients scanned on two occasions on subsequent days without intervention or treatment. Note the excellent reproducibility not only of mean values but of the percentile values.

View larger version (22K): [in a new window]   Figure 14. Three-dimensional plots of 40 histograms of (a) Monte Carlo estimated relative cerebral blood volume (rCBV) and (b) mean transit time (MTT) values, normalized to have median values of 0. Values were obtained with signal-to-noise ratio (SNR) ranging from 8.8–8.3. Each histogram contains 104 samples. The widths of the histograms are measures of the variation of rCBV and MTT values which represent the uncertainties in the calculation of rCBV and MTT.

View larger version (36K): [in a new window]   Figure 15. Results from a Monte Carlo modelling experiment to assess the accuracy of pharmacokinetic models in estimating Ktrans. Graphs on the left show data with a very high signal-to-noise ratio (SNR) central graphs show data using a lower SNR and data on the right shows the results where the SNR is low. The upper series of graphs show the accuracy and standard deviations associated with fitting synthetic data using the standard Tofts and Kermode model. It can be seen that at low SNR this technique becomes highly inaccurate and imprecise. The middle row shows the effect of using the first pass pharmacokinetic model described by Li et al [43]. This model systematically underestimates high values of kfp but shows good reproducibility even at relatively low SNRs. The lower series of graphs shows a more complex model which combines the two analysis techniques [43]. This hybrid method shows good accuracy and precision across a wide range of SNR values.

View larger version (13K): [in a new window]   Figure 16. Changes in kfp occurring as a result of administration of a vascular endothelial growth factor (VEGF) antagonist in groups of patients with advanced epithelial cancers. Note the immediate drop in kfp as a result of the drug administration and the gradual recovery over the following months. Also note the dose effect with minimal effects seen at the lowest dose (0.3 mg kg–1) and more marked effects at the higher dose regimens.