Dynamic measurements of <i>T</i><sub>1</sub> shortening (dynamic contrast enhanced—DCE) as well as of <i>T</i><sub>2</sub><sup style="margin-left:-6px;">*</sup> shortening (dynamic susceptibility contrast—DSC) as two separate measurement strategies are widely used to quantitatively describe tumor perfusion and vascularity. Dual-echo approaches allow for the simultaneous assessment of both effects. The extension to multi-echo sequences should inhere the advantage of improved signal-to-noise ratios and more precise sampling of the <i>T</i><sub>2</sub><sup style="margin-left:-6px;">*</sup> decay. The aim of our study is to investigate, if an extension of the dual-echo approach to the multi-echo approach allows for more stable quantitative determination of pharmacokinetic parameters in brain tumors. This study applies a multi-echo approach to obtain different estimations of a vascular input function and analyzes various combinations of vascular input functions and pharmacokinetic models. Perfusion measurements were performed with 52 consecutive patients with different brain tumors using a 10-echo gradient echo sequence. Our findings show that the extension to multi-echo sequences leads to an 11%-improvement of the Contrast-to-Noise ratio. Compared to other combinations, an application of Extended Tofts model using the <i>T</i><sub>2</sub><sup style="margin-left:-6px;">*</sup>-related venous output function or an output function estimated in the tumor tissue enables the most reliable determination of perfusion parameters, reducing the reproducibility range by a factor of 1.2 to 10 for K<sup>trans</sup> and of 1.2 to 5.5 in the case of rBV calculation. Determination of K<sup>trans</sup> within repeated measurements within about 3 days results as most stable, if AIF from tumor pixels is used as vascular input function, meaning that the scatter is reduced by a factor of 1.2 compared to the next best VIF and by a factor of 10 compared to the worst of the tested approaches. In addition, this study shows that signal decomposition into two components with different Larmor frequencies might provide additional information concerning tissue composition of brain tumors.
Dynamic contrast enhanced (DCE) magnetic resonance imaging and dynamic susceptibility contrast (DSC) magnetic resonance imaging are widely used techniques for the assessment of tumor perfusion and tumor vascularity. In DCE- MRI, the measured T1 shortening is mainly caused by contrast medium (CM) distributed in the interstitial space, but partly also by intravascular CM [
Since the diffusion of CM into the interstitial space is much slower compared to the passage of a CM bolus through the capillaries, T1-related dynamic measurements can be performed with a temporal resolution of about 45 seconds to 3 minutes [
Using a dual-echo approach, temporal distribution of CM in both interstitial and capillary compartments can be assessed during only one CM administration [
However, to achieve a sufficient temporal resolution, this dual-echo approach is restricted to a few slices only. This major drawback might be solved by the application of more sophisticated sequence designs (e.g., parallel imaging, keyhole, segmented EPI etc.).
Another disadvantage of the dual-echo approach is the compromise between temporal resolution and signal to noise ratio (SNR) of the calculated images, which does not always produce sufficient accuracy of results.
In addition, the determination of a vascular input function (VIF) still is a critical step in MR perfusion measurements. In vessels apart from the tumor, the time course of the CM concentration is known to be different from that in the intratumoral vasculature in terms of temporal position (i.e., delay) as well as peak broadening (i.e., dispersion). This might result in systematic errors of estimated perfusion parameters [
These problems can be diminished when applying the concept of simultaneous dynamic registration of T1 and
・ The multi-echo approach results in a significant better contrast-to-noise ratio (CNR) of the calculated
・ Simultaneous dynamic T1 and
Moreover, there exists still some hidden potential of the multi-echo approach. Especially when we express the TE dependent signal intensity as a complex sum of two components with different Larmor frequencies (e.g., water and fat), we can model the signal dependence on TE more correctly with the multi-echo approach.
Therefore, the aim of this paper is to reveal the potential benefits of the multi-echo approach in dynamic imaging of brain tumors.
More precisely, we aim to assess alterations of CNR using higher numbers of echoes within a given TR. For this purpose, different correction methods for the calculation of S0(t) and
Additionally, different estimations of a vascular input function are compared quantitatively and various combinations of vascular input functions and pharmacokinetic models are tested.
Finally, assuming tumor tissue to consist of two components with different Larmor frequencies, we aim to obtain more information about the tumor structure by differentiating those substances. We think that at least rough spectroscopic differences are identifiable with multi-echo measurements. Therefore, we analyze the potential clinical value of the signal decomposition into two parts with different Larmor frequencies in a population of brain tumor patients.
52 consecutive patients with different, newly diagnosed brain tumors were included in the study (25 glioblastoma, 12 meningioma, 12 metastasis, 3 lymphoma patients). For 21 patients, MRI examination was performed twice with a time interval of 1 to 4 days. 18 patients received preoperatively dexamethasone (10 glioblastoma, 2 meningeoma, 5 metastasis, 1 lymphoma patients). We excluded five patients who received an MRI examination twice and eight patients with single-time MRI examinations from further analysis due to non-plausible vascular input functions or movement artifacts.
MRI was performed on a 3 Tesla MR scanner (Siemens Magnetom Verio). In addition to the standard tumor protocol, a dynamic 10 echo FLASH sequence (TR = 44 ms, α = 70˚, TE = 1.2, 2.2, 3.1, 4.1, 5.5, 6.4, 8.0, 10.0, 12.0 and 13.0 ms, matrix 256 × 208 (acquisition matrix: 128 × 73), GRAPPA acceleration factor = 3) with a temporal resolution of 2 seconds was run during the application of CM (0.1 ml/kg body weight of Gadovist, (Bayer Schering) at a flow of 4 ml/s followed by 10 ml of saline). The sequence includes 3 slices of each 5 mm thickness. One slice was positioned in the neck region for measurement of an Arterial Input Function (AIF), two others were placed in the tumor region, identified with native T1 and T2 weighted images. After 10 of 60 dynamic scans, the CM was administered.
For calculation of
The quality of the multi-echo corrections (i.e., the determination of S0 and
Sum of CNRs | Euklidean distance | |
---|---|---|
1st + 2nd echo | 163.92 | 26.6 |
1st + 10th echo | 319.21 | 24.9 |
10 echoes loglinear | 357.04 | 33.4 |
10 echoes exponential | 342.41 | 33.3 |
4 echoes exponential | 330.16 | 27.4 |
water fat 10 echoes exponential | 347.42 | 30.4 |
water fat 4 echoes exponential | 344.51 | 27.4 |
For estimation of the vascular input function, we implemented several time courses: S0(t) and
The degree of nonlinearity in S0(CM concentration) was qualitatively eva- luated by comparing the curve shape of S0(t) with that of
The following models were combined with the above-described vascular input functions:
For S0,Tumor(t) we applied the Patlak and Extended Tofts models [
For
Additionally, we evaluated simple empiric parameters like maximum relative and maximum absolute enhancement in S0,Tumor(t) and maximum temporal increase of
Based on the perfusion parametric images, lacunarity parameters were calculated as published elsewhere [
To identify model/VIF-combinations allowing the most stable perfusion parameter estimation, the reliability and reproducibility of the calculated parameters were evaluated visually and quantitatively. For the visual evaluation of each parameter, the values of the first and second investigation were drawn on a patient-by-patient basis (see
(n = number of patients, x1,i or x2,i = parameter x for patient i at first or second measurement resp.). This parameter normalizes the mean of difference of measures between the two time points to the median of the corresponding parameter and defines in this way a relative measure of stability of that parameter. For plausibility reasons,
Statistical analysis was done using Excel with user functions written in VBA (Visual Basic for Applications, Microsoft Corporation). The image analysis was performed with a program written in IDL (“Interactive Data Language”, Exelis Visual Information Solutions Inc.).
The CNR values averaged over all measurements for
Most reliable pharmacokinetic parameters occurred to be Ktrans and rBV. In
In general, the overall signal intensity in the slice aimed for the AIF estimation was significantly lower than in the tumor covering slice(s).
For all kinds of multi-echo correction, the CNR of the VOF was better than that of the AIF by a factor of 1.75 to 3.5, where the superior sagittal sinus allowed most plausible curves. For the preferred log-linear correction these factors amount to 2.96 for S0 and 2.10 for
In addition, high reproducibility of perfusion parameters was achieved by using vascular input function from
The frequency of occurrence of the different models and vascular input functions in the list of the 30 best model-VIF combinations is summarized in
Ktrans model | rBV in function | VIF source | VIF ROI | rBV model | VIF source | VIF ROI | ||
---|---|---|---|---|---|---|---|---|
Tofts | fitted | tumor | 0.47 | Patlak | S0 | vene | 0.61 | |
Tofts | fix | tumor | 0.51 | Gauss area | vene | 0.71 | ||
Tofts | fitted | vene | 0.58 | Gauss peak | vene | 0.80 | ||
Tofts | fix | vene | 0.76 | Patlak | tumor | 0.82 | ||
Tofts | fitted | artery | 0.81 | Tofts | tumor | 0.85 | ||
Tofts | fitted | S0 | vene | 1.16 | Tofts | vene | 0.93 | |
Tofts | fix | artery | 1.17 | Patlak | artery | 0.97 | ||
Patlak | fitted | S0 | vene | 1.17 | Gauss area | vene | 1.20 | |
Patlak | fix | tumor | 1.47 | Tofts | S0 | vene | 1.28 | |
Tofts | fix | S0 | vene | 1.69 | Tofts | artery | 1.43 | |
Tofts | fix | S0 | artery | 1.84 | GVF peak | vene | 1.43 | |
Tofts | fitted | S0 | artery | 2.21 | Patlak | vene | 1.47 | |
Patlak | fix | artery | 2.30 | Tofts | S0 | artery | 2.97 | |
Patlak | fitted | vene | 2.60 | Patlak | S0 | artery | 3.36 | |
Patlak | fitted | tumor | 2.68 | |||||
Patlak | fitted | artery | 2.87 | |||||
Patlak | fix | vene | 3.93 | |||||
Patlak | fix | S0 | vene | 4.02 | ||||
Patlak | fitted | S0 | artery | 4.41 | ||||
Patlak | fix | S0 | artery | 4.67 |
Model | Non normalized | Normalized |
---|---|---|
Tofts | 7.1% | 17.1% |
Patlak | 0.0% | 13.3% |
IDT | 0.0% | 0.0% |
Gaussian fit | 0.0% | 50.0% |
GVF fit | 0.0% | 0.0% |
--- | 25.0% | 50.0% |
Vascular input function | Non normalized | Normalized |
S0 artery | 0.0% | 20.0% |
S0 vein | 0.0% | 11.1% |
0.0% | 0.0% | |
4.3% | 21.7% | |
15.0% | 30.0% | |
--- | 14.3% | 21.4% |
properties). The most reliable calculations are based on the Extended Toftsmodel, Gaussian fit and model-free calculations (like maximum enhancement e.g., which showed minimum
The results of IDT calculations are rather dissatisfactory. None of the IDT- based parameters was found either in the 30 best normalized or the best non- normalized parameters.
The lowest mean “fat-to-water” signal ratio was found for malignant glioma and the highest mean for meningioma. Besides this, higher “fat-to-water” signal ratios were detected in gliomas after dexamethasone application, compared to those without such medication. But due to the small sample size, these differences were not statistically significant.
Dual-echo perfusion measurements have already been proposed in the 1990s. They allow calculation of the time course of
Compared to the dual-echo approach, the multi-echo approach that is proposed in this paper allows improvements concerning signal-to-noise ratio and estimation of vascular input functions. Furthermore, it allows at least rough estimations of the proportion of components with different Larmor frequencies.
In our study, the loglinear curve fit was found to suit best among all multi-echo correction algorithms tested. Remarkable, loglinear correction showed better results than the exponential one. From the physical point of view, however, the exponential fits correspond to the data more exactly than fits on logarithmized signal intensities (by taking the logarithms, the statistical uncertainty of higher values is compressed. Thus, linear fitting of logarithms overweighs the low-in- tensity data points corresponding to the late echoes). Otherwise, for numerical reasons, they are less stable compared to the linear fit on the logarithmized data. In other words, if the weighting of data points influences the curve fit to an important way, then the exponential fits should give better results than the loglinear ones. But, if numerical robustness is the essential objective of the multi- echo correction method, then the loglinear model should work better than the exponential ones. Therefore, in our data the stability aspect underwent more influence than the biased weighting of the data points.
We also found the biexponential fat-water models resulting in higher Euclidean distances between tumor entities and CNR―at least compared to the corresponding monoexponential models. But we want to stress, that models based on loglinear and monoexponential calculations are actually identical, the only differences are the different weighting of data points and the different numeric stability.
An extended discussion of the biexponential fat-water model can be found in the “signal decomposition” paragraph.
Testing simple dual-echo corrections, Euclidean distances between tumor entities based on Ktrans and rBV, appeared to be surprisingly good for the first and second echo only ? in contrast to the CNR evaluation. This, however, seems to be a statistical artifact due to the high data noise and the small sample size. Obtained parameters were not significantly biased compared to the ones from the other correction methods, but they were noisier.
A prerequisite for quantitative perfusion measurements is the determination of VIF. Several approaches of manual, semi-automatic or automatic VIF determinations for brain perfusion measurements are available. There are for example measurements of a special slice caudal to the tumor for deriving of an AIF [
In the present study, we drew the ROIs manually in order to control their influence on the time courses of S0 and
In our data, nonlinearity of S0 (CM concentration) as well as biased curve shapes in the venous ROIs were less noticeable than in the arterial ones. This subjective impression was confirmed by CNR analysis as well as by rating of perfusion parameters with different vascular input functions. According to Yuan [
Earlier studies [
In addition to the correct position on the time axis,
However, due to downscaling by the volume fraction of capillaries in the tumor voxels,
Although models with fewer numbers of free parameters like the Patlak approach are supposed to be more stable, we found better reproducibility with the Tofts model. This is a contradictory finding to previously published data [
Calculations based on the Indicator Dilution Theory lead to quite instable results. This could be caused by the limited influence of smaller, even good vascularized tumors on the Mean Transit Time (MTT) of the CM bolus due to the comparatively short pathway of the blood through the tumor. Another reason could be the less stable rBV estimation by means of the fit of a Gamma Variate Function compared to the well performing Gaussian fit.
Diagnostics based on pharmacological parameters is one way to achieve comparability of results between different sites. Another way to reach this aim is standardization of measurement conditions allowing for comparison of simple model free parameters. In MR mammography, a qualitative description of the time course of contrast enhancement under mildly standardized measurement conditions occurred to meet the clinical needs [
The assumption for brain and tumor tissue to consist of two components with different Larmor frequency appears to improve the quality of curve fits of signal intensities such as a function of TE, compared to monoexponential fits without differentiation between fat and water signals. This is clearly seen in the evaluation of CNRs, but does not transfer to better accuracy of perfusion parameter based differentiation between tumor entities (
Some limitations of this study are to be mentioned:
First of all, our data was not compared to any reference method. To our knowledge, no real “gold standard” for the perfusion measures exists. Hence, for the evaluation of the reliability of our data, we introduced the reproducibility parameter defined in Equation (1).
Second, evaluations were performed with in-house software. Although carefully tested during the development process, there remains a higher risk of incorrect calculations or numeric instabilities compared to commercial and/or widespread software solutions. No reference calculations with standard software were performed.
Finally, in the literature, DCE MR studies of brain tumors usually are performed for about 5 minutes at a temporal resolution of 5.3 s [
The extension of the well-known dual-echo perfusion measurement to multi- echo sequence leads to the advantage of improved signal-to-noise ratios. The most reliable determinations of perfusion parameters (especially Ktrans and rBV) were achieved with Extended Tofts model applying the
Temporal distribution of contrast concentration within the tumor vessel bed from multi-echo data seems to be advantageous for the estimation of a vascular
Without pretreatment | Dexamethasone | |
---|---|---|
Fat to water signal ratio | 0.016 | 0.018 |
Fat to water signal ratio normalized to normal tissue | 2.00 | 2.57 |
input function compared to potentially dispersed and time-shifted functions derived from arterial or venous signals.
As potential added value, multi-echo FLASH MRI allowing signal decomposition into two components with different Larmor frequencies might provide additional information concerning tissue composition of brain tumors. The clinical value of these findings and particularly the exact biochemical composition of the so-called “fat” component are fruitful areas for of future research.
We gratefully thank Nadine Hietschold for her helpful recommendations and linguistic support during the writing of this manuscript.
Hietschold, V., Abramyuk, A., Juratli, T., Sitoci-Ficici, K.H., Laniado, M. and Linn, J. (2017) Magnetic Resonance Perfusion in Brain Tumors: Com- parison of Different Evaluation Approaches in Dual-Echo and Multi-Echo Techniques. International Journal of Medical Physics, Clinical Engineering and Radiation Onco- logy, 6, 174-192. https://doi.org/10.4236/ijmpcero.2017.62016