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Müller SJ, Khadhraoui E, Ernst M, Rohde V, Schatlo B, Malinova V. Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria. BMC Med Imaging 2024; 24:3. [PMID: 38166651 PMCID: PMC10759655 DOI: 10.1186/s12880-023-01183-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVE Glioblastoma with multiple foci (mGBM) and multiple brain metastases share several common features on magnetic resonance imaging (MRI). A reliable preoperative diagnosis would be of clinical relevance. The aim of this study was to explore the differences and similarities between mGBM and multiple brain metastases on MRI. METHODS We performed a retrospective analysis of 50 patients with mGBM and compared them with a cohort of 50 patients with multiple brain metastases (2-10 lesions) histologically confirmed and treated at our department between 2015 and 2020. The following imaging characteristics were analyzed: lesion location, distribution, morphology, (T2-/FLAIR-weighted) connections between the lesions, patterns of contrast agent uptake, apparent diffusion coefficient (ADC)-values within the lesion, the surrounding T2-hyperintensity, and edema distribution. RESULTS A total of 210 brain metastases and 181 mGBM lesions were analyzed. An infratentorial localization was found significantly more often in patients with multiple brain metastases compared to mGBM patients (28 vs. 1.5%, p < 0.001). A T2-connection between the lesions was detected in 63% of mGBM lesions compared to 1% of brain metastases. Cortical edema was only present in mGBM. Perifocal edema with larger areas of diffusion restriction was detected in 31% of mGBM patients, but not in patients with metastases. CONCLUSION We identified a set of imaging features which improve preoperative diagnosis. The presence of T2-weighted imaging hyperintensity connection between the lesions and cortical edema with varying ADC-values was typical for mGBM.
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Affiliation(s)
- Sebastian Johannes Müller
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
- Neuroradiologische Klinik, Klinikum Stuttgart, Stuttgart, Germany
| | - Eya Khadhraoui
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
- Neuroradiologische Klinik, Klinikum Stuttgart, Stuttgart, Germany
| | - Marielle Ernst
- Department of Neuroradiology, University Medical Center, Göttingen, Germany
| | - Veit Rohde
- Department of Neurosurgery, University Medical Center, Göttingen, Germany
| | - Bawarjan Schatlo
- Department of Neurosurgery, University Medical Center, Göttingen, Germany
| | - Vesna Malinova
- Department of Neurosurgery, University Medical Center, Göttingen, Germany.
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Uher D, Drenthen GS, Schijns OEMG, Colon AJ, Hofman PAM, van Lanen RHGJ, Hoeberigs CM, Jansen JFA, Backes WH. Advances in Image Processing for Epileptogenic Zone Detection with MRI. Radiology 2023; 307:e220927. [PMID: 37129491 DOI: 10.1148/radiol.220927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.
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Affiliation(s)
- Daniel Uher
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Gerhard S Drenthen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Olaf E M G Schijns
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Albert J Colon
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Paul A M Hofman
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Rick H G J van Lanen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Christianne M Hoeberigs
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Jacobus F A Jansen
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
| | - Walter H Backes
- From the Department of Radiology and Nuclear Medicine (D.U., G.S.D., P.A.M.H., C.M.H., J.F.A.J., W.H.B.) and Department of Neurosurgery (O.E.M.G.S., R.H.G.J.v.L.), Maastricht University Medical Centre, P. Debyelaan 25, NL-6229 HX Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands (D.U., G.S.D., O.E.M.G.S., R.H.G.J.v.L., J.F.A.J., W.H.B.); Academic Center for Epileptology, Kempenhaeghe and Maastricht University Medical Centre, Heeze/Maastricht, the Netherlands (O.E.M.G.S., A.J.C., P.A.M.H., C.M.H., J.F.A.J.); and Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands (J.F.A.J.)
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Peng Q, Wu C, Kim J, Li X. Efficient phase-cycling strategy for high-resolution 3D gradient-echo quantitative parameter mapping. NMR IN BIOMEDICINE 2022; 35:e4700. [PMID: 35068007 DOI: 10.1002/nbm.4700] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 06/05/2023]
Abstract
Magnetization-prepared (MP) gradient-echo (GRE) sequences suffer from signal contaminations from T1 recovery during the readout train, which can be eliminated by paired RF phase cycling (PC) at the cost of doubling the scan time. The objective of this study was to develop and validate a novel unpaired PC strategy to eliminate the time penalty for high-resolution quantitative parameter mapping in 3D MP-GRE sequences. Based on the observation that the contaminating T1 recovery signal along the GRE readout train is independent of magnetization preparation, its impact can be eliminated using a novel curve-fitting approach with complex-valued data without needing paired PC acquisitions. Four new unpaired PC schemes were compared with two traditional paired PC schemes in both phantom and in vivo human knee studies at 3 T using a MP angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) T1ρ mapping sequence. In the phantom study, all methods resulted in consistent T1ρ measurements (∆T1ρ < 0.5%) at the center slice when B0 /B1 values were uniform. Results were not consistent when off-center slices with nonideal B0 /B1 were included. Two unpaired PC schemes had comparable or significantly improved quantitative accuracy and scan-rescan reproducibility compared with the paired PC schemes. There was no significant T1ρ quantitative variability increase or spatial fidelity loss using the new unpaired PC schemes. Unpaired PC schemes also had different T1ρ spectral responses at different B0 frequency offsets, which can potentially be exploited to reduce sensitivity to B0 field inhomogeneities. The human knee study results were consistent with the phantom study findings. In conclusion, an unpaired PC strategy potentially allows more accurate quantitative parameter mapping with halved scan time compared with the paired PC approach to eliminate signal contaminations from T1 recovery. It therefore offers additional flexibility in SNR optimization, spatial resolution improvement, and choice of imaging sampling points to obtain more accurate quantitative parameter mapping.
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Affiliation(s)
- Qi Peng
- GRUSS Magnetic Resonance Research Center (MRRC), Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA
| | - Can Wu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jeehun Kim
- Department of Biomedical Engineering, Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA
| | - Xiaojuan Li
- Department of Biomedical Engineering, Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, Ohio, USA
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Zhang M, Huang H, Liu W, Tang L, Li Q, Wang J, Huang X, Lin X, Meng H, Wang J, Zhan S, Li B, Luo J. Combined quantitative T2 mapping and [ 18F]FDG PET could improve lateralization of mesial temporal lobe epilepsy. Eur Radiol 2022; 32:6108-6117. [PMID: 35347363 PMCID: PMC9381472 DOI: 10.1007/s00330-022-08707-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 02/15/2022] [Accepted: 03/01/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES To investigate whether quantitative T2 mapping is complementary to [18F]FDG PET in epileptogenic zone detection, thus improving the lateralization accuracy for drug-resistant mesial temporal lobe epilepsy (MTLE) using hybrid PET/MR. METHODS We acquired routine structural MRI, T2-weighted FLAIR, whole brain T2 mapping, and [18F]FDG PET in 46 MTLE patients and healthy controls on a hybrid PET/MR scanner, followed with computing voxel-based z-score maps of patients in reference to healthy controls. Asymmetry indexes of the hippocampus were calculated for each imaging modality, which then enter logistic regression models as univariate or multivariate for lateralization. Stereoelectroencephalography (SEEG) recordings and clinical decisions were collected as gold standard. RESULTS Routine structural MRI and T2w-FLAIR lateralized 47.8% (22/46) of MTLE patients, and FDG PET lateralized 84.8% (39/46). T2 mapping combined with [18F]FDG PET improved the lateralization accuracy by correctly lateralizing 95.6% (44/46) of MTLE patients. The asymmetry indexes of hippocampal T2 relaxometry and PET exhibit complementary tendency in detecting individual laterality, especially for MR-negative patients. In the quantitative analysis of z-score maps, the ipsilateral hippocampus had significantly lower SUVR (LTLE, p < 0.001; RTLE, p < 0.001) and higher T2 value (LTLE, p < 0.001; RTLE, p = 0.001) compared to the contralateral hippocampus. In logistic regression models, PET/T2 combination resulted in the highest AUC of 0.943 in predicting lateralization for MR-negative patients, followed by PET (AUC = 0.857) and T2 (AUC = 0.843). CONCLUSIONS The combination of quantitative T2 mapping and [18F]FDG PET could improve lateralization for temporal lobe epilepsy. KEY POINTS • Quantitative T2 mapping and18F-FDG PET are complementary in the characterization of hippocampal alterations of MR-negative temporal lobe epilepsy patients. • The combination of quantitative T2 and18F-FDG PET obtained from hybrid PET/MR could improve lateralization for temporal lobe epilepsy.
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Affiliation(s)
- Miao Zhang
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Hui Huang
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Wei Liu
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Lihong Tang
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Qikang Li
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jia Wang
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Xinyun Huang
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Xiaozhu Lin
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Hongping Meng
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Jin Wang
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Shikun Zhan
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Biao Li
- grid.16821.3c0000 0004 0368 8293Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China ,Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, 200025 China
| | - Jie Luo
- grid.16821.3c0000 0004 0368 8293School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
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Zhao L, Zhang X, Luo Y, Hu J, Liang C, Wang L, Gao J, Qi X, Zhai F, Shi L, Zhu M. Automated detection of hippocampal sclerosis: Comparison of a composite MRI-based index with conventional MRI measures. Epilepsy Res 2021; 174:106638. [PMID: 33964793 DOI: 10.1016/j.eplepsyres.2021.106638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 04/01/2021] [Accepted: 04/06/2021] [Indexed: 11/27/2022]
Abstract
PURPOSE This study aims to compare the performance of an MRI-based composite index (HSI) with conventional MRI-based measures in hippocampal sclerosis (HS) detection and postoperative outcome estimation. METHODS Seventy-two temporal lobe epilepsy (TLE) patients with pathologically confirmed HS and fifteen TLE patients without HS were included retrospectively. The T1-weighted and FLAIR images of these patients were processed with AccuBrain to quantify the hippocampal volume (HV) and the hippocampal FLAIR signal. The HSI index that considered both HV and hippocampal FLAIR signal was also calculated. Two experienced neuropathologists rated the HS severity with the resected tissue and reached an agreement for all cases. The asymmetry indices of the MRI measures were used to lateralize the sclerotic side, and the original MRI measures were applied to detect HS vs. normal hippocampi. Operating characteristic curve (ROC) analyses were performed for these predictions. We also investigated the sensitivity of the ipsilateral MRI measures in characterizing the pathological severity of HS and the associations of the MRI measures with postoperative outcomes (Engel class categories). RESULTS With the optimal cutoffs, the asymmetry indices of HSI and HV both achieved excellent performance in differentiating left vs. right HS (accuracy = 100 %), and the absolute value of the asymmetry index of HSI performed best in differentiating unilateral vs. bilateral HS (accuracy = 91.7 %). Regarding the detection of HS, HSI performed better in sensitivity (94.4 % vs. 87.5 %) while HV performed better in specificity (93.6 % vs. 89.4 %) when the contralateral site of unilateral HS and both sides of non-HS patients were considered as the normal reference, and HSI performed even better than HV when only both sides of non-HS patients were considered as the normal reference (AUC: 0.956 vs. 0.934, p = 0.038). The ipsilateral HSI presented the strongest association with the pathological rating of HS severity (r = 0.405, p < 0.001). None of the ipsilateral or contralateral MRI measures was associated with the postoperative outcomes. Among the asymmetry indices, only the absolute value of the asymmetry index of HV presented a significant association with the Engel classifications for the Year 2∼3 visit (r = -0.466, p = 0.004) or the latest visit with >1 year follow-up (r = -0.374, p = 0.003) while controlling for disease duration and follow-up duration. CONCLUSION The HSI index and HV presented comparable good performance in HS detection, and HSI may have better sensitivity than HV in differentiating pathological HS severity. Higher magnitude of HV dissymmetry may indicate better post-surgical outcomes for HS patients.
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Affiliation(s)
- Lei Zhao
- BrainNow Research Institute, Shenzhen, China
| | - Xufei Zhang
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Jianxin Hu
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China
| | - Chenyang Liang
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China
| | - Lining Wang
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China
| | - Jie Gao
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China
| | - Xueling Qi
- Department of Pathology, Sanbo Brain Hospital, Capital Medical University, China
| | - Feng Zhai
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, China; Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
| | - Mingwang Zhu
- Department of Radiology, Sanbo Brain Hospital, Capital Medical University, China.
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Dou W, Zhao L, Su C, Lu Q, Liu Q, Guo J, Zhao Y, Luo Y, Shi L, Zhang Y, Wang R, Feng F. A quantitative MRI index for assessing the severity of hippocampal sclerosis in temporal lobe epilepsy. BMC Med Imaging 2020; 20:42. [PMID: 32334546 PMCID: PMC7183666 DOI: 10.1186/s12880-020-00440-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Abstract
Background Hippocampal sclerosis (HS) is associated with post-surgery outcome in patients with temporal lobe epilepsy (TLE), and an automated method that quantifies HS severity is still lacking. Here, we aim to propose an MRI-based HS index (HSI) that integrates hippocampal volume and FLAIR signal to measure the severity of HS. Methods Forty-two pre-surgery TLE patients were included retrospectively, with T1-weighted (T1W) and FLAIR images acquired from each subject. Two experienced neurosurgeons (W.D. and C.S.) and one neurologist (Q.L.) rated HS severity with a four-class grading scale (normal, mild, moderate and severe) based on both hippocampal volume loss and increased FLAIR signal. A consensus of HS severity for each subject was made by voting among the three visual rating results. Regarding the automatic quantification, the hippocampal volume was quantified by AccuBrain on T1W image, and the FLAIR signal of hippocampus was calculated as the mean intensity of hippocampal region on the FLAIR image (normalized by the mean intensity of gray matter). To fit the HSI from visual rating, we applied ordinal regression with the voted visual rating as the dependent variable, and hippocampal volume and FLAIR signal as the independent variables. The HSI was calculated by weighting the predicted probabilities of the four-class grading scales from ordinal regression. Results The intra-class correlation coefficient (single measure) of the three raters was 0.806. The generated HSI was significantly correlated with the visual rating scales of the three raters (W.D.: 0.823, Q.L.: 0.817, C.S.: 0.717). HSI scores well differentiated the different HS categories as defined by the agreed HS visual rating (normal vs. mild: p < 0.001, mild vs. moderate: p < 0.001, moderate vs. severe: p = 0.001). Conclusions The proposed HSI was consistent with visual rating scales from epileptologists and sensitive to HS severity. This MRI-based index may help to evaluate HS severity in clinical practice. Further validations are needed to associate HSI with post-surgery outcomes.
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Affiliation(s)
- Wanchen Dou
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Lei Zhao
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Changbao Su
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Qiang Lu
- Department of Neurology, Peking Union Medical College Hospital, Beijing, China
| | - Qi Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Jinzhu Guo
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Yuming Zhao
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, Guangdong Province, China
| | - Lin Shi
- BrainNow Research Institute, Shenzhen, Guangdong Province, China.,Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, SAR, China
| | - Yiwei Zhang
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Renzhi Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing, China.
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
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Louis S, Morita-Sherman M, Jones S, Vegh D, Bingaman W, Blumcke I, Obuchowski N, Cendes F, Jehi L. Hippocampal Sclerosis Detection with NeuroQuant Compared with Neuroradiologists. AJNR Am J Neuroradiol 2020; 41:591-597. [PMID: 32217554 DOI: 10.3174/ajnr.a6454] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/17/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE NeuroQuant is an FDA-approved software that performs automated MR imaging quantitative volumetric analysis. This study aimed to compare the accuracy of NeuroQuant analysis with visual MR imaging analysis by neuroradiologists with expertise in epilepsy in identifying hippocampal sclerosis. MATERIALS AND METHODS We reviewed 144 adult patients who underwent presurgical evaluation for temporal lobe epilepsy. The reference standard for hippocampal sclerosis was defined by having hippocampal sclerosis on pathology (n = 61) or not having hippocampal sclerosis on pathology (n = 83). Sensitivities, specificities, positive predictive values, and negative predictive values were compared between NeuroQuant analysis and visual MR imaging analysis by using a McNemar paired test of proportions and the Bayes theorem. RESULTS NeuroQuant analysis had a similar specificity to neuroradiologist visual MR imaging analysis (90.4% versus 91.6%; P = .99) but a lower sensitivity (69.0% versus 93.0%, P < .001). The positive predictive value of NeuroQuant analysis was comparable with visual MR imaging analysis (84.0% versus 89.1%), whereas the negative predictive value was not comparable (79.8% versus 95.0%). CONCLUSIONS Visual MR imaging analysis by a neuroradiologist with expertise in epilepsy had a higher sensitivity than did NeuroQuant analysis, likely due to the inability of NeuroQuant to evaluate changes in hippocampal T2 signal or architecture. Given that there was no significant difference in specificity between NeuroQuant analysis and visual MR imaging analysis, NeuroQuant can be a valuable tool when the results are positive, particularly in centers that lack neuroradiologists with expertise in epilepsy, to help identify and refer candidates for temporal lobe epilepsy resection. In contrast, a negative test could justify a case referral for further evaluation to ensure that false-negatives are detected.
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Affiliation(s)
- S Louis
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
| | - M Morita-Sherman
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
| | - S Jones
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
| | - D Vegh
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
| | - W Bingaman
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
| | - I Blumcke
- Institute of Neuropathology (I.B.), University Hospitals Erlangen, Erlangen, Germany
| | - N Obuchowski
- Quantitative Health Sciences (N.O.), Cleveland Clinic, Cleveland, Ohio
| | - F Cendes
- Department of Neurology (F.C.), University of Campinas-UNICAMP, Campinas, São Paulo, Brazil
| | - L Jehi
- From the Epilepsy Center (S.L., M.M.-S., S.J., D.V., W.B., L.J.), and
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Ma D, Jones SE, Deshmane A, Sakaie K, Pierre EY, Larvie M, McGivney D, Blümcke I, Krishnan B, Lowe M, Gulani V, Najm I, Griswold MA, Wang ZI. Development of high-resolution 3D MR fingerprinting for detection and characterization of epileptic lesions. J Magn Reson Imaging 2018; 49:1333-1346. [DOI: 10.1002/jmri.26319] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/10/2018] [Accepted: 08/10/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Dan Ma
- Radiology; Case Western Reserve University; Cleveland Ohio USA
| | | | - Anagha Deshmane
- Magnetic Resonance Center; Max Planck Institute for Biological Cybernetics; Tuebingen Germany
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic; Cleveland Ohio USA
| | - Eric Y. Pierre
- Florey Institute of Neuroscience and Mental Health; Melbourne Australia
| | - Mykol Larvie
- Imaging Institute, Cleveland Clinic; Cleveland Ohio USA
| | - Debra McGivney
- Radiology; Case Western Reserve University; Cleveland Ohio USA
| | - Ingmar Blümcke
- Epilepsy Center; Cleveland Clinic; Cleveland Ohio USA
- Institute of Neuropathology, University Hospitals Erlangen; Erlangen Germany
| | - Balu Krishnan
- Epilepsy Center; Cleveland Clinic; Cleveland Ohio USA
| | - Mark Lowe
- Imaging Institute, Cleveland Clinic; Cleveland Ohio USA
| | - Vikas Gulani
- Radiology; Case Western Reserve University; Cleveland Ohio USA
| | - Imad Najm
- Epilepsy Center; Cleveland Clinic; Cleveland Ohio USA
| | | | - Z. Irene Wang
- Epilepsy Center; Cleveland Clinic; Cleveland Ohio USA
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9
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Affiliation(s)
- Daniela Prayer
- From the Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Waehringerguertel 18-20, A-1090 Vienna, Austria
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10
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Winston GP, Vos SB, Burdett JL, Cardoso MJ, Ourselin S, Duncan JS. Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy. Epilepsia 2017; 58:1645-1652. [PMID: 28699215 PMCID: PMC5599984 DOI: 10.1111/epi.13843] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2017] [Indexed: 12/31/2022]
Abstract
Objective Hippocampal sclerosis (HS), the most common cause of refractory temporal lobe epilepsy, is associated with hippocampal volume loss and increased T2 signal. These can be identified on quantitative imaging with hippocampal volumetry and T2 relaxometry. Although hippocampal segmentation for volumetry has been automated, T2 relaxometry currently involves subjective and time‐consuming manual delineation of regions of interest. In this work, we develop and validate an automated technique for hippocampal T2 relaxometry. Methods Fifty patients with unilateral or bilateral HS and 50 healthy controls underwent T1‐weighted and dual‐echo fast recovery fast spin echo scans. Hippocampi were automatically segmented using a multi‐atlas–based segmentation algorithm (STEPS) and a template database. Voxelwise T2 maps were determined using a monoexponential fit. The hippocampal segmentations were registered to the T2 maps and eroded to reduce partial volume effect. Voxels with T2 >170 msec excluded to minimize cerebrospinal fluid (CSF) contamination. Manual determination of T2 values was performed twice in each subject. Twenty controls underwent repeat scans to assess interscan reproducibility. Results Hippocampal T2 values were reliably determined using the automated method. There was a significant ipsilateral increase in T2 values in HS (p < 0.001), and a smaller but significant contralateral increase. The combination of hippocampal volumes and T2 values separated the groups well. There was a strong correlation between automated and manual methods for hippocampal T2 measurement (0.917 left, 0.896 right, both p < 0.001). Interscan reproducibility was superior for automated compared to manual measurements. Significance Automated hippocampal segmentation can be reliably extended to the determination of hippocampal T2 values, and a combination of hippocampal volumes and T2 values can separate subjects with HS from healthy controls. There is good agreement with manual measurements, and the technique is more reproducible on repeat scans than manual measurement. This protocol can be readily introduced into a clinical workflow for the assessment of patients with focal epilepsy.
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Affiliation(s)
- Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom.,Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Sjoerd B Vos
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom.,Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Jane L Burdett
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom.,Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - M Jorge Cardoso
- Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, UCL, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, United Kingdom.,Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
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11
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Adler S, Lorio S, Jacques TS, Benova B, Gunny R, Cross JH, Baldeweg T, Carmichael DW. Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI. Neuroimage Clin 2017; 15:95-105. [PMID: 28491496 PMCID: PMC5413300 DOI: 10.1016/j.nicl.2017.04.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Revised: 03/10/2017] [Accepted: 04/18/2017] [Indexed: 12/31/2022]
Abstract
Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.
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Affiliation(s)
- Sophie Adler
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sara Lorio
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.
| | - Thomas S Jacques
- Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Barbora Benova
- Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, UK; Department of Paediatric Neurology, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; 2nd Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Roxana Gunny
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - J Helen Cross
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - David W Carmichael
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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Cardinale F, Francione S, Gennari L, Citterio A, Sberna M, Tassi L, Mai R, Sartori I, Nobili L, Cossu M, Castana L, Lo Russo G, Colombo N. SUrface-PRojected FLuid-Attenuation-Inversion-Recovery Analysis: A Novel Tool for Advanced Imaging of Epilepsy. World Neurosurg 2017; 98:715-726.e1. [DOI: 10.1016/j.wneu.2016.11.100] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 11/16/2016] [Accepted: 11/17/2016] [Indexed: 01/17/2023]
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13
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Duncan JS, Winston GP, Koepp MJ, Ourselin S. Brain imaging in the assessment for epilepsy surgery. Lancet Neurol 2016; 15:420-33. [PMID: 26925532 DOI: 10.1016/s1474-4422(15)00383-x] [Citation(s) in RCA: 184] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 11/22/2015] [Accepted: 12/02/2015] [Indexed: 01/14/2023]
Abstract
Brain imaging has a crucial role in the presurgical assessment of patients with epilepsy. Structural imaging reveals most cerebral lesions underlying focal epilepsy. Advances in MRI acquisitions including diffusion-weighted imaging, post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection. Functional MRI can be used to identify areas of the cortex that are essential for language, motor function, and memory, and tractography can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. PET, SPECT, simultaneous EEG and functional MRI, and electrical and magnetic source imaging can be used to infer the localisation of epileptic foci and assist in the design of intracranial EEG recording strategies. Progress in semi-automated methods to register imaging data into a common space is enabling the creation of multimodal three-dimensional patient-specific datasets. These techniques show promise for the demonstration of the complex relations between normal and abnormal structural and functional data and could be used to direct precise intracranial navigation and surgery for individual patients.
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Affiliation(s)
- John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; National Hospital for Neurology and Neurosurgery, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, Gerrards Cross, UK.
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; National Hospital for Neurology and Neurosurgery, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, Gerrards Cross, UK
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; National Hospital for Neurology and Neurosurgery, London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter, Gerrards Cross, UK
| | - Sebastien Ourselin
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, London, UK; Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK; National Hospital for Neurology and Neurosurgery, London, UK
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