br Quantitative analysis The quantitative
Quantitative analysis The quantitative analysis is based on modeling the concentration change of the contrast agent using pharmacokinetic modeling techniques. An initial conversion of SI to concentration values is needed. Concentration vs time curves are then fitted using a bi-compartmental PK model (vessels and EES) with single vascular input (usually cyclobenzaprine hcl or other). The following parameters can be derived from a mathematical model: (1) Ktrans (volume transfer constant): determines the influx of the contrast agent from the intravascular space to the EES. It predominantly represents the vascular permeability in a permeability-limited (high flow) situation, but represents the blood flow into the tissue in a flow-limited (high permeability) situation; (2) Kep (reverse reflux rate constant): expresses the return process of the contrast agent from the EES to the intravascular space; and (3) Ve (volume fraction of EES): an indirect measure representing the cellular density of the tissue. In the micro-environment of bone marrow, the above mentioned parameters are more related to low flow status, because the blood perfusion rate of marrow is rather slow as compared with other organ/systems. Those data represents the blood flow into the tissue in a flow-limited situation. These parameters require additional calculations to generate parametric maps obtained after a pixel-by-pixel curve fitting process of the region under analysis. Thus, they are more computationally technical to obtain than the semi-quantitative ones. After generating parametric maps, the mean or median values within region of interests (ROI) are usually calculated to represent microvasculature, but histogram analysis or heterogeneity in parametric maps may also provide additional information. For optimum parameter quantification, a moderate temporal resolution is required to record initial rapid uprising of the SI curve immediately after the contrast agent administration. The accuracy of these parameters is influenced by curve fitting algorithms and magnitude of motion artifacts. Luckily, the environment of BM would not count on motion artifact.
Model selection Kety first described the flow-limited tracer uptake in tissue, and since then several pharmacokinetic models have been proposed by Tofts et al., Brix et al. All these models used single source of arterial input function. However, for BM parenchymal disease, which are supplied by small arteriole such as segmental artery directly arising from aorta, a single-input, bi-compartment PK model by Brix and Tofts is often used to obtain. The choice of contrast agent molecular properties and the temporal resolution of the acquisition have a clear influence on the parameters. To standardize calculations, the acquisition should have proper temporal resolution (about 5 s each image set, during at least 5 min or 300 s), and voxel-wise statistical analysis is suggested.
Clinical application of DCE-MRI Increased bone marrow MVD was reported in patients with AML, but its association with patients\' survival is unclear and inconclusive, probably because the MVD detected by conventional immunocytochemical technique is unable to assess the global and dynamic angiogenesis of the bone marrow. Vascularity within a tumor can be spatially or temporally heterogeneous; and tumor vessels are much more permeable than are normal blood vessels. Thus, assessment of angiogenesis in the BM by traditional MVD poses special challenges. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can provide global and functional imaging of tumor angiogenesis. A previous research, demonstrated that the rate of vertebral marrow perfusion decreased significantly in subjects older than 50 years, but it decreased in a relatively slow speed. However, the differences of BM perfusion between patients with AML and age- and sex-matched healthy subjects were drastic. The patients with AML had much higher Peak values (as the key parameter) than did the age- and sex-matched controls (almost 4.8-fold difference), and the distribution had no overlap between these 2 groups. Thus, we can postulate that, although the age factor has influence on the Peak of the bone marrow, it is minor and can be overridden by the great effect of tumor angiogenesis.