BOLD Hemodynamic alteration in Brain Tumors
Lalit Gupta1, Rakesh K Gupta2, Prativa Sahoo1, Pradeep K Gupta2, Rana Patir3, Sandeep Vaishya3, Indrajit Saha4, and Walter Backes5

1Philips India Ltd., Bangalore, India, 2Department of Radiology, Fortis Memorial Research Institute, Gurgaon, India, 3Department of Neurosurgery, Fortis Memorial Research Institute, Gurgaon, India, 4Philips India Ltd., Gurgaon, India, 5Department of Radiology, Maastricht University Medical Center, Maastricht, Netherlands

Synopsis

The objective of the study is to determine the temporal delay in cerebral hemodynamic flow in brain tumors relative to normal brain tissue using rsfMRI and compare this with DCE derived cerebral blood volume(CBV) maps. Time series from all the voxels were cross-correlated with the mean time series from the normal hemisphere. The time point with maximum correlation was used to generate temporal shift map(TSM) for each voxel. We observed early hemodynamic changes in high grade glioma and found significant difference in the mean TSM ratio between Glioblastoma(GBM) and low grade tumors. TSM also appeared similar to rCBV perfusion maps.

Purpose

Dynamic contrast enhanced (DCE) MR perfusion imaging is one of the preferred methods to infer the tumor grade for treatment decisions. In this study, we have explored the utility of blood oxygenation level-dependent fMRI (BOLD-fMRI) in resting state for tumor characterization. We assess the changes in the cerebral hemodynamic flow using resting state fMRI in the tumors region. Given a notion that the major source of the component in BOLD is non-neuronal physiologic fluctuations related to respiration and cardiac pulsation, it was hypothesized that the BOLD might reflects temporal dynamics of underlying vasculature1,2. The objective of the study is to compare the time differences in the cerebral hemodynamic flow in brain tumor regions with the normal side of the brain and compare these delay maps with that of DCE perfusion based cerebral blood volume (CBV) maps.

Method

Data acquisition: We included 18 patient data sets in this retrospective study (9 each high and low grade glioma). Diagnosis in all these cases was confirmed on histopathology. The patients were included in this study in accordance with local ethical committee approval. All the imaging were done at 3.0T scanner (Inginia,Philips Healthcare) . For rs-fMRI, EPI data were acquired TR/TE:3000/35ms, 90° Flip Angle, 96×94 matrix, 230×230 FOV, 4mm slice, no gap, 30 section and 120 frames), DCE-MRI data were acquired with TR/TE:4.4/2.1ms, 12 slice with 6mm thickness, 32 dynamic with 3.9s temporality.

Image processing: Using SPM8 software, the functional images were slice-time and motion corrected, co-registered to the anatomical template and smoothed with a kernel of 8 mm (full-width-at-half-maximum). Any signal drifts were corrected by removing the very low frequency components (<0.01 Hz). Very high frequency components i.e. above 0.1 Hz were also removed.

Temporal shift map: The “mean time series” was computed from the normal side of the brain (including both gray matter and white matter). Time series from each brain voxel was correlated with the time shifted (±6 secs) “mean time series”. The time point with maximum correlation was (from 0 to 12 seconds) used for each voxel to generate “temporal shift map” of the brain from resting-state functional MR imaging1. The ratio of the mean temporal shift map values in the tumor region to that of contralateral of the brain was used for tumor characterization and measured as arbitrary unit (AU).

Results

The ratio of the mean temporal shift in the tumor region to the mean temporal shift in the normal region (contralateral side) was found be below 1.0 in all the patients; however, the ratio in two normal areas was found be greater than 1.0 (figure 1). Significant differences were found between GBM and LGG (p<0.05) using Wilcoxon Rank-Sum Test. Figure 2 shows threshold temporal shift map superimposed on the FLAIR image. It can be observed that bold temporal shift map show similarity with CBV maps in both high grade and low grade glioma.

Discussion

Our preliminary study demonstrates that temporal shift/fluctuations in BOLD signal shows similarity with DCE derived CBV maps. Temporal shift maps showed that hemodynamic alteration come early in the GBM than normal brain. Quantification of the normalized ratio of the signal intensity from glioma shows significant differences between high grade and low grade glioma. Based on this study, it appears that temporal hemodynamic fluctuations quantification may have the potential in clinical grading of glioma and may be developed as the alternate non contrast technique to contrast enhanced perfusion based technique in future.

Acknowledgements

No acknowledgement found.

References

1. Amemiya S, Kunimatsu A, Saito N, et al. Cerebral Hemodynamic Impairment: Assessment with Resting-State Functional MR Imaging. Radiology. 2014;270(2):548-555.

2. Kiviniemi V, Jauhiainen J, Tervonen O, et al. Slow vasomotor fluctuation in fMRI of anesthetized child brain. Magn Reson Med. 2000;44(2):373–378.

Figures

Figure 1: Comparison of mean temporal shift in tumor region to that of in normal size in GBM and low grade gliomas.

Figure 2: Showing Temporal shift map superimposed on FLAIR (A, E); T2 weighted (B, F), rCBV (CBV normalized with normal white matter) (C,G), T1 post contrast (D,H) of a patient diagnosed with GBM and low grade glioma respectively.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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