Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors

Abstract

A method is presented for the acquisition and analysis of dynamic contrast-enhanced (DCE) MRI data, focused on the characterization of tumors in humans. Gadolinium (Gd) contrast was administered by bolus injection, and its effect was monitored in time by fast T1-weighted MRI. A simple algorithm was developed for automatic extraction of the arterial input function (AIF) from the DCE-MRI data. This AIF was used in the pixelwise pharmacokinetic determination of physiological vascular parameters in normal and tumor tissue. Maps were reconstructed to show the spatial distribution of parameter values. To test the reproducibility of the method 11 patients with different types of tumors were measured twice, and the rate of contrast agent uptake in the tumor was calculated. The results show that normalizing the DCE-MRI data using individual coregistered AIFs, instead of one common AIF for all patients, substantially reduces the variation between successive measurements. It is concluded that the proposed method enables the reproducible assessment of contrast agent uptake rates.

Data Assimilation in Brain Tumor Models

“…Some sources of registration error are individual variance in patient brain geometry and the mass effect. Radiation necrosis resulting from treatment, which may appear similar to active tumor on the image, can further complicate MR image interpretation [22].…”

Comparison of errors associated with single‐ and multi‐bolus injection protocols in low‐temporal‐resolution dynamic contrast‐enhanced tracer kinetic analysis

“…In theory, the use of an individually measured AIF will increase the accuracy with which parameterizations may be obtained relative to the use of an assumed AIF (16,17). It has also been suggested that measuring an AIF aides the reproducibility of parameter estimation by accounting for day-to-day variation in contrast agent (CA) administration and physiological factors that may affect CA circulation (17). However, measurement of an AIF is itself subject to errors, which can potentially lead to degraded parameter accuracy and reproducibility.…”

Heuristic linear mapping of physiological parameters in dynamic contrast‐enhanced MRI without T1 measurement and contrast agent concentration

“…In this case, the use of the proposed linear fitting may lead to fitting deviations. However, according to the reported values in the literature, fortunately very few k ep values over 5/min have been reported for different kinds of tumors and normal tissues (21,(25)(26)(27)(28)(29), indicating the feasibility and practicability of using this mapping method for different DCE-MRI applications. As seen in Table 1, the narrower k ep range to be fit, the better linearity is found between TSF and k ep .…”

“…Simply put, pixels from blood vessels were identified if the maximum enhancement of one pixel was greater than the average plus two times the standard deviation (SD) of the maximum enhancement of all pixels in this slice. The arteries were separated from veins according to the time delay to the maximum signal intensity (21). For convenience, the TICs for arteries were referenced as AIF.…”