This Article Full Text Full Text (PDF) Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Jackson, A Search for Related Content PubMed PubMed Citation Articles by Jackson, A

Analysis of dynamic contrast enhanced MRI

Imaging Science and Biomedical Engineering, The Medical School, University of Manchester, Oxford Road, Manchester M13 9PT, UK

View larger version (17K): [in a new window]   Figure 1. Graph showing the change in contrast concentration against time in a single voxel of normal grey matter of a normal brain. Crosses represent the original measurement points and a straight line shows the optimal curve fit result. Notice that the curve fitting process effectively removes the noise present in the original data. Since the curve fitting procedure uses only data during the early part of the first passage of the contrast bolus it also removes the effect of re-circulation of contrast which is responsible for the elevation of measurements in the later part of the curve.

View larger version (80K): [in a new window]   Figure 2. Calculated parametric images from a normal brain. Images represent (a) cerebral blood volume (CBV); (b) cerebral blood flow (CBF); (c) mean transit time (MTT); (d) time of arrival (T0); (e) time to peak concentration (TTP) and (f) standard fitting error (SFE). Note that maps of CBV show high levels within the blood vessels, much lower levels in grey matter and the lowest measurements of all in white matter. Maps of CBF show a similar distribution of values. MTT is relatively uniform across the entire brain except for a slight prolongation is in the anterior and posterior cerebral watershed areas, particularly on the right (left of the image). Both T0 and TTP maps show contrast delay in the central white matter as with early arrival in peripheral cortex. The map of SFE shows very low values (red) in areas of high signal to noise ratio corresponding to blood vessels. High values are seen in cortex and the highest of all in the white matter reflecting measurement uncertainty due to be creasing temporal signal to noise ratio in these tissues.

View larger version (101K): [in a new window]   Figure 3. A series of calculated images of time to peak concentration (TTP) in a patient with a severe right-sided carotid stenosis. There is severe prolongation of contrast arrival throughout the right hemisphere indicated by a loss of the very early arterial arrival (red) seen on the left and increased areas of delayed contrast arrival seen in blue.

View larger version (13K): [in a new window]   Figure 4. A graph showing the change in contrast concentration against time in a single voxel of normal grey matter of a normal brain. The data collection has been performed with a deliberately degraded acquisition sequence in order to reduce contrast to noise ratio in the data. A direct comparison with Figure 1 shows significantly lower signal to noise ratio in these data. Despite this the curve fitting programme has derived an optimal fit which will be used the calculation of vascular parameters. However, if fitting error is calculated as described in the text then this curve fit result will be associated with very broad confidence intervals.

View larger version (11K): [in a new window]   Figure 5. Curves showing changes in signal intensity on T2* weighted gradient echo images at different concentrations of standard paramagnetic contrast material. Note that the use of a low flip angle (bottom graph) results in almost no sensitivity to contrast media so that this strategy can be used in areas of marked contrast leakage. However, note that overall signal is far lower than is obtained with higher flip angles. At higher flip angles there is a significant contrast effect resulting in elevation of signal intensity as contrast concentration rises. However, this rise reaches a plateau after which the response to contrast concentration remains linear and eventually drops. This acts as the basis for a second strategy to remove the effect of contrast leakage by pre-enhancing tissues with a preliminary dose of contrast in order to reach a plateau phase. If this is achieved then the second injection of contrast will not be affected by further contrast leakage.

View larger version (12K): [in a new window]   Figure 6. Signal intensity data from a single voxel in an enhancing tumour imaged using a gradient echo sequence with a flip angle of 35°. (a) The data in the graph show an initial signal drop followed by rapid signal rise due to enhancement. (b) The data in the graph was obtained from the same region of interest in the same tumour using the same sequence. However, these data were collected following pre-enhancement of the tumour and shows no sign of relaxivity related enhancement.

View larger version (18K): [in a new window]   Figure 7. Scattergrams showing the change in bolus width within a single slice of brain tissue plotted against contrast arrival time (TTM, time to mean contrast concentration). (a) The graph shows values from pixels with blood volume values greater than 5% (i.e. vessels) whereas (b) shows values from all pixels within the image. There is a clear linear relationship between contrast to age and duration and resulting from bolus dispersion of the contrast agent passes through the brain.

View larger version (30K): [in a new window]   Figure 8. Figure illustrating the basic compartments involved in pharmacokinetic modelling of intravenous contrast distribution. Movements of contrast between compartments will be governed by the ratio of the contrast concentrations between those compartments and by the local blood flow and the surface area and permeability of the capillary endothelium to contrast agent (represented by the thickness of the connecting arrows). The composite transfer coefficient for contrast between blood and the tissue of interest is represented as ktrans.

View larger version (27K): [in a new window]   Figure 9. Graphical representation of the contrast distribution occurring within an individual voxel of tissue. Contrast passes from the blood into the interstitial tissues and the figure shows the standard mathematical abbreviations used to describe each of the individual tissue spaces.

View larger version (114K): [in a new window]   Figure 10. A series of dynamic MR images (top) showing contrast enhancement and passage of the contrast agent into the interstitial tissues using a 3D T1 weighted gradient echo acquisition. Illustrated images are spaced approximately 10 s apart. The lower row of images shows the calculated concentration of contrast agent which is derived from the images in the top row and which can be used as the basis for pharmacokinetic analysis of enhancement patterns.

View larger version (16K): [in a new window]   Figure 11. An example of contrast concentration changes occurring in various tissues during contrast enhancement. The concentration in the capillary plasma can be seen to reach an early peak as the bolus of contrast passes through the tissue vasculature. A second re-circulation peak can also be seen and subsequently there is elevation of the contrast concentration within the plasma which gradually decreases due to renal elimination. The change of concentration of contrast material in the interstitial space can be seen to represent a gradual elevation throughout the time course illustrated here. The amplitude and gradient of that rise will reflect capillary endothelial permeability surface area product and blood flow. The third curve shows the concentration of contrast observed in the tissue as a whole. In this case a small initial peak can be seen due to contrast within blood vessels in the voxel as well as a gradual elevation due to contrast leakage.

View larger version (98K): [in a new window]   Figure 12. Transverse contrast enhanced image from (A) dynamic series and (B) maps of T0, (C) kfp and (D) blood volume (BV) in a patient with metastatic colonic carcinoma. Metastatic deposits are seen in the right and left lobes. The T0 map shows early contrast arrival compared with normal liver in both metastases. Maps of kfp (C) and BV (D) show a peripheral rim of high kfp and BV in both metastases with low values in the tumour centre. This tumour rim shows K values that appear lower than those of normal liver parenchyma and BV values that appear lower.

View larger version (15K): [in a new window]   Figure 13. Curve fit of dynamic contrast enhanced data from (a) a low-grade and (b) a high-grade glioma showing curve fit performed with an extended pharmacokinetic model. Illustrated values show separate calculated estimates of flow (F) and permeability surface area product (PS) as well as the proportional blood volume (vb) and the volume of the extravascular extracellular space (ve). The original data (represented as circles) shows some significant spread around the curve fit due to inherent signal to noise characteristics despite the fact that this data was taken from large regions of interest rather than single voxels.

View larger version (73K): [in a new window]   Figure 14. Parametric calculated images of (a) ktrans and (b) cerebral blood volume (CBV) from dynamic T1 weighted contrast enhanced data. Parametric images were calculated using a first-pass analysis algorithm which decomposes its intravascular and extravascular contrast concentrations. ktrans will therefore be affected by both permeability surface area product and flow.

View larger version (19K): [in a new window]   Figure 15. Scattergrams of the results from a Monte Carlo simulation showing the relationship between calculated standard fitting error and the expected variation in mean cerebral blood volume (CBV) from images acquired with variable levels of image noise.