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| THE EDITORIAL DEBATE |
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| Year : 2015 | Volume
: 63
| Issue : 4 | Page : 480 |
Are we ready to replace dynamic contrast-enhanced perfusion with tissue similarity measures derived perfusion magnetic resonance imaging in glioma grading?
Rakesh K Gupta
Fortis Memorial Research Institute, Gurgaon, Haryana, India
| Date of Web Publication | 4-Aug-2015 |
Correspondence Address:
Rakesh K Gupta
Fortis Memorial Research Institute, Gurgaon, Haryana
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0028-3886.161980
How to cite this article:
Gupta RK. Are we ready to replace dynamic contrast-enhanced perfusion with tissue similarity measures derived perfusion magnetic resonance imaging in glioma grading?. Neurol India 2015;63:480 |
How to cite this URL:
Gupta RK. Are we ready to replace dynamic contrast-enhanced perfusion with tissue similarity measures derived perfusion magnetic resonance imaging in glioma grading?. Neurol India [serial online] 2015 [cited 2023 Dec 7];63:480. Available from: https://www.neurologyindia.com/text.asp?2015/63/4/480/161980 |
Perfusion magnetic resonance imaging (MRI) is currently being used in the evaluation of brain tumors. A number of papers are available in the literature utilizing this technique to assess the glioma grading and therapeutic response assessment. Currently, three techniques are available in the literature - dynamic susceptibility weighted contrast (DSC), [1],[2] dynamic contrast enhanced (DCE) [3] and arterial spin level (ASL) [2] perfusion imaging. While DSC and DCE techniques utilize a paramagnetic contrast agent, ASL exploits the endogenous contrast properties available in the blood. Among these three techniques, DSC is the most widely used technique for evaluation of brain tumors because of its easy availability at most of the mid and high field commercial MRI systems. However, DSC has a number of technical limitations like presumed linear relationship between magnetic resonance signals and the contrast agent concentration, delay and dispersion of bolus, partial volume effect, leakage correction etc., that are known to influence the quantitative results. Strong local magnetic field inhomogeneities due to calcium and/or blood products within the lesion may also produce erroneous DSC results. [1] On the other hand, DCE generates hemodynamic as well as permeability information about the tumor tissue; however, it has limited excess at majority of the commercial MRI systems. [1],[3] While DCE has the ability to address a number of limitations of DSC, it generally suffers from relatively lower temporal and spatial resolution which may influence the analysis. [1]
Hu et al. have proposed tissue similarity measures (TSM) in the DSC perfusion data and compared their DSC based regional cerebral blood volume (rCBV) results in 35 patients for glioma grading with the quantification of rCBV derived from TSM. [4] They have also claimed similar results for glioma grading with improvement in a signal-to-noise ratio which needs detailed evaluation. There are very limited studies using TSM in brain perfusion MRI till date. [5] Interestingly, Hu et al. believe that they can exclude conventional quantification of arterial input function and concentration time curve and can still produce similar results using the TSM method. Another interesting aspect of this technique exploited by the authors is by using the white matter as the reference for the contra-lateral region; however, it has been widely observed that the tumor may involve the white matter or grey matter or both. Moreover, CBV is known to be higher in grey matter as compared to white matter. In Hu et al.'s work, use of white matter as contra-lateral reference region assumption is required to quantify rCBV of the tumor for the purpose of grading since, most likely, the tumors in their study were more localized to the white matter regions.
I believe that we should verify the results of this study in a larger group of patients before we may justify its use in clinical practice.
| » References | |  |
| 1. | Jahng GH, Li KL, Ostergaard L, Calamante F. Perfusion magnetic resonance imaging: A comprehensive update on principles and techniques. Korean J Radiol 2014;15:554-77.  |
| 2. | Svolos P, Kousi E, Kapsalaki E, Theodorou K, Fezoulidis I, Kappas C, et al. The role of diffusion and perfusion weighted imaging in the differential diagnosis of cerebral tumors: A review and future perspectives. Cancer Imaging 2014;14:20. [ PUBMED] |
| 3. | Sahoo P, Rathore RK, Awasthi R, Roy B, Verma S, Rathore D, et al. Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high- and low-grade glioma using dynamic contrast-enhanced (DCE) MRI. J Magn Reson Imaging 2013;38:677-88. |
| 4. | Hu CH, Hu S, Gao X, Sun CM, Gan WJ, Liu YL, et al. Feasibility of tissue similarity map-based relative cerebral blood volume in the evaluation of gliomas. Neurol India 2015;63:525-31. |
| 5. | Haacke EM, Li M, Juvvigunta F. Tissue similarity maps (TSMs): A new means of mapping vascular behavior and calculating relative blood volume in perfusion weighted imaging. Magn Reson Imaging 2013;31:481-9. |
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