NEURORADIOLOGY / ORIGINAL PAPER
Synthetic magnetic resonance-based relaxometry in differentiating
central nervous system tuberculoma and glioblastoma
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Department of Radiodiagnosis and Imaging, Division of Neuroimaging and Interventional Neuroradiology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
Submission date: 2025-01-01
Final revision date: 2025-02-21
Acceptance date: 2025-02-21
Publication date: 2025-04-29
Corresponding author
Sanket Dash
Department of Radiodiagnosis and Imaging, Division of Neuroimaging and Interventional Neuroradiology, PGIMER, Chandigarh,
India
Pol J Radiol, 2025; 90: 198-206
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ABSTRACT
Purpose:
Synthetic magnetic resonance imaging (MRI) allows reconstruction of multiple contrast-weighted images from a single acquisition of multiple delay multiple echo (MDME) sequence with quantitative relaxometry (longitudinal relaxation rate [R1], transverse relaxation rate [R2], and proton density [PD]) in a shorter acquisition time. We tried to explore synthetic MR-based relaxometry to differentiate central nervous system (CNS) tuberculomas from primary CNS neoplasm like glioblastoma.
Material and methods:
Ten cases of CNS tuberculoma and 14 cases of glioblastoma underwent pre- and post-contrast synthetic MRI. R1, R2, and PD values were calculated from lesion core, wall, and perilesional oedema using free-hand region of interest and compared across the 2 groups.
Results:
Both pre- and post-contrast R1 and R2 relaxation rates from core were significantly higher in tuberculoma (mean pre-contrast R1 – 0.93, R2 – 15.02; post-contrast R1 – 1.51, R2 – 15.48) from glioblastoma (mean pre-contrast R1 – 0.36, R2 – 4.58; post-contrast R1 – 0.43, R2 – 4.78). The same values were higher in perilesional oedema of glioblastoma (mean pre-contrast R1 – 0.75, R2 – 9.9; post-contrast R1 – 0.78, R2 – 10.48) compared to tuberculoma (mean pre-contrast R1 – 0.68, R2 – 8.57; post-contrast R1 – 0.72, R2 – 8.67). No significant difference was seen between relaxometry parameters from the walls of lesions.
Conclusions:
Synthetic MR-based relaxometry can be useful in distinguishing CNS tuberculomas from glioblastoma. R1 and R2 relaxation rates from core of the lesions are most important in differentiating the two with R1 value > 0.852 and R2 value > 11.565 from core strongly suggests tuberculoma over glioblastoma.
REFERENCES (17)
1.
Krauss W, Gunnarsson M, Andersson T, Thunberg P. Accuracy and reproducibility of a quantitative magnetic resonance imaging method for concurrent measurements of tissue relaxation times and proton density. Magn Reson Imaging 2015; 33: 584-591.
2.
Warntjes JB, Leinhard OD, West J, Lundberg P. Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magn Reson Med 2008; 60: 320-329.
3.
Warntjes JB, Dahlqvist O, Lundberg P. Novel method for rapid, simultaneous T1, T*2, and proton density quantification. Magn Reson Med 2007; 57: 528-537.
4.
Khatri GD, Krishnan V, Antil N, Saigal G. Magnetic resonance imaging spectrum of intracranial tubercular lesions: one disease, many faces. Pol J Radiol 2018; 83: e54-65. DOI: 10.5114/pjr.2018.81408.
5.
Kang KM, Choi SH, Hwang M, Yoo RE, Yun TJ, Kim JH, Sohn CH. Application of synthetic MRI for direct measurement of magnetic resonance relaxation time and tumor volume at multiple time points after contrast administration: preliminary results in patients with brain metastasis. Korean J Radiol 2018; 19: 783-791.
6.
Gupta RK, Kumar S. Central nervous system tuberculosis. Neuroimaging Clin N Am 2011; 21: 795-814.
7.
Jayakumar PN, Srikanth SG, Chandrashekar HS, Subbakrishna DK. T2 relaxometry of ring lesions of the brain. Clin Radiol 2007; 62: 370-375.
8.
Barajas Jr RF, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 2012; 14: 942-954.
9.
Petrecca K, Guiot MC, Panet-Raymond V, Souhami L. Failure pattern following complete resection plus radiotherapy and temozolomide is at the resection margin in patients with glioblastoma. J Neurooncol 2013; 111: 19-23.
10.
Tsougos I, Svolos P, Kousi E, Fountas K, Theodorou K, Fezoulidis I, Kapsalaki E. Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T. Cancer Imaging 2012; 12: 423-436.
11.
Lemercier P, Paz Maya S, Patrie JT, Flors L, Leiva-Salinas C. Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions. American J Roentgenol 2014; 203: 163-169.
12.
Price SJ, Young AMH, Scotton WJ, Ching J, Mohsen LA, Boon-zaier NR, et al. Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016; 43: 487-494.
13.
Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Peña A, et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. Am J Neuroradiol 2006; 27: 1969-1974.
14.
Oh J, Cha S, Aiken AH, Han ET, Crane JC, Stainsby JA, et al. Quantitative apparent diffusion coefficients and T2 relaxation times in characterizing contrast enhancing brain tumors and regions of peritumoral edema. J Magn Reson Imaging 2005; 21: 701-708.
15.
Blystad I, Warntjes JB, Smedby Ö, Lundberg P, Larsson EM, Tisell A. Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema. Sci Rep 2020; 10: 17986. DOI: 10.1038/s41598-020-75105-6.
16.
Müller A, Jurcoane A, Kebir S, Ditter P, Schrader F, Herrlinger U, et al. Quantitative T1-mapping detects cloudy-enhancing tumor compartments predicting outcome of patients with glioblastoma. Cancer Med 2017; 6: 89-99.
17.
Dash S, Vyas S, Bhardwaj N, Ahuja CK, Modi M, Chhabra R, et al. Synthetic MRI derived relaxometry parameters: a new insight into characterization of ring enhancing lesions of brain. Neuroradiology 2024. DOI: 10.1007/s00234-024-03533-6.