Dinil Sasi S1, Rakesh K Gupta2, Rana Patir2, Suneeta Ahlawat2, and Anup Singh1,3
1Indian Institute of Technology Delhi, New Delhi, India, 2Fortis Memorial Research Institute, Gurugram, India, 3All India Institute of Medical Science, New Delhi, India
Synopsis
Arterial-Input-Function(AIF)
is a pre-requisite in fitting generalized-tracer-kinetic-model(GTKM) to dynamic-contrast-enhanced(DCE)
MRI data for computing tracer-kinetic-parameters(TKP). TKP are highly sensitive
to peak and shape of AIF, and this results in variation in computed parameter
values across studies. These variations can reduce accuracy of TKP in glioma grading.
We hypothesize that propagation of these AIF related errors to TKP can be
mitigated using normalization w.r.t. corresponding average TKP values of healthy
tissue and normalized TKP might improve glioma grading. The proposed
normalization w.r.t. healthy gray-matter tissue has significantly reduced
variations of TKP and improved accuracy of glioma grading particularly using Ktrans.
Introduction
Dynamic-contrast-enhanced
MRI(DCE-MRI) derived trace- kinetic-parameters(TKP) computed from generalized-tracer-kinetic-model(GTKM)
has shown its potential in diagnosis, treatment planning and grading of glioma1–3. Arterial-input-function(AIF)
is a prerequisite in fitting GTKM to the voxel-wise concentration-time curve(C(t)).
Different manual, semi-automatic and automatic segmentation of arterial voxels
have been reported previously4–6. Depending on
different parameters like contrast injection dose, partial volume effect, heart
output rate, etc., AIF shows high inter-subject and intra-subject variability(during
follow-up) in its peak and shape6. Since TKP are
highly sensitive to these variations, parameters reported across different
studies show variation in their magnitude scales6,7. It was
hypothesized that normalization can play a key role to minimize these
variations. The objective of this study was to compare four different
normalization approaches to minimize the variations of TKP and evaluate effect
of normalization on differentiating high-grade-glioma(HGG) vs. low-grade-glioma(LGG). Methods
This
retrospective study included 31-HGG, 22-LGG and 33 follow-up data from 10
subjects (pre and post-operative) histologically confirmed with glioma(WHO 2016
classification). MRI data included conventional MRI images(2D T1-W, dual
PD-T2-W, 3D FLAIR) and DCE-MRI data acquired at 3T(Ingenia, Philips Healthcare,
The Netherlands). DCE-MRI data protocol: 20 slices,6mm thickness,TR/TE=6.15/3ms,
10o FA, 230×230mm2 FOV, 32 time-points with 3.8s temporal-resolution.
Contrast(Gd-BOPTA) of dose 0.1 mmol/kg body weight was injected at 4th
time point at 3.0ml/sec injection rate
Voxel-wise
conversion of signal-intensity time curve to C(t) was perfomed using a T1 map
computed from 3-point TSE method1. A piece-wise
linear model was fitted to each C(t) to estimate features like bolus arrival
time(BAT), wash-out slope etc7. and these
parameters were used as features to segment AIF voxels. A mean AIF across all
slices were used in this study to compute TKP. In this study, we have proposed TKP
normalization based on the perfusion in contralesional mean gray-matter(GM) and
mean white-matter(WM) curves(Type-1 and Type-3) and compared this with the previously
reported technique to normalize hemodynamic parameters(rCBV, rCBF)8 on TKP(Type-2 and
Type-4). The steps for Type-1,2,3 and 4 normalizations are given below:
i.Segment the contralesional GM and WM
tissues of multiple slices(In this study, we have considered the central 25% of
total spatial coverage).
ii.Compute the mean C(t) of the segmented GM
and WM mask to obtain GM-C(t) and WM- C(t) (to compute the mean curves, C(t)
were co-registered with respect to BAT to avoid dispersion effect).
iii.Fit the GTKM to the mean GM-C(t) and
WM-C(t) to compute the parameters corresponding to mean C(t)(α1,β1,γ1,α2,β2,γ2). α1,β1,γ1 corresponds to Ktrans, ve, and vp of GM-C(t), and α2,β2,γ2 corresponds to Ktrans, ve, and vp of WM-C(t)
iv.For Type-1 and Type-3 normalization, divide the Ktrans, ve,
and vp maps(estimated from equation(1)) with the corresponding
parameter value obtained from step(iii)
v. Compute the mean value corresponding to GM and WM masks (step(i)) of Ktrans (αmean1,αmean2), ve (βmean1,βmean2) and vp (γmean1,γmean2).
vi. For Type-2 and Type-4 normalization, divide the Ktrans, ve, and vp maps(estimated from equation(1)) with the corresponding parameter value obtained from step(v)
In
this study, Co-efficient of variation(CoV)(ratio of standard deviation to mean)
and relative-percentage-error(RPE) were used to evaluate variation in TKP
w.r.t. AIF. A t-test and ROC
curve analysis were performed for evaluating differentiation between LGG and HGG.Results and Discussion
Figure-1
shows a representative FLAIR image, segmented GM and WM tissues, and their
corresponding mean curves fitted to GTKM. These reference TKP were used to for Type-1
and Type-3 normalizations. While, mean values of the TKP in segmented GM and WM
tissues in are used for Type-2 and Type-4 normalizations. Figure-2 show results
of simulation study conducted on 6 subjects to evaluate change in TKP w.r.t AIF. At -30% variation, RPE in Ktrans and vp
were -41±5 % and -44±2%, while 30% increase amplitude resulted in 23±0.22% and
23±0.14% respectively. All four normalization techniques have significantly
reduced the variations in vp to less than ±1%. For Ktrans,
Type-1 and Type-2 normalizations were found to relatively more stable to
variations in AIF(RE<±1%). vp exhibited significant difference
between grades before normalization(p=0.0135). After normalization, Type-1
normalization produced significant difference for stratification for Ktrans
and ve(p=0.0175 and p=0.0417). Type-2 and Type-4 normalization
performed better for vp(p=0.006 and p=0.004). Type-2 normalization
performed better in grading using ve(p=0.023). Figure-3 and table-1
shows the Box-Whisker plot and results of ROC analysis. Around 9% improvement was observed in AUC with
improved specificity for Ktrans after Type-1 normalization. Type-4
normalization has also provided substantial improvement in AUC(~6%). Even though Type-1 and Type-2 variations have minimized the intra-class
and inter-class variations after normalizing ve, it is not accounted
in improving the differentiation of glioma grades. However, Type-1
normalization has improved the sensitivity and specificity(table-1).More than
100% change in amplitude was observed across single subject in multiple
follow-up data(figure-4). CoV(%) in Ktrans, ve and vp
without normalization were 37.1±18.1,24.6±10 and 24.3±14.3 and, after Type-1 normalization, CoV reduced to
20.2±10.2,15.6±5.8 and 16.9±8.6 respectively.
Limitations:
This study included single centre data and same protocol. A test of proposed
normalization techniques should be conducted on large mutli-center data set
with different acquisition parameters such as acquisition time and temporal
resolution.Conclusion
Type-1
normalization has outperformed all other normalization techniques for Ktrans
and ve and Type-4 normalization was found to be optimum for
normalizing vp in quantitatively differentiating HGG and LGGAcknowledgements
The authors thank Dr. Mamta Gupta for data handling,
Neha Vats, Manish Awasti and Virender Yadav for assistance in data processing.References
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