T2 mapping outperforms normalised FLAIR in identifying hippocampal sclerosis

Rodionov, Roman; Bartlett, Philippa A.; He, Ci; Vos, Sjoerd B.; Focke, Niels K.; Ourselin, Sébastien; Duncan, John S.

doi:10.1016/j.nicl.2015.03.004

Cited by 40 publications

(14 citation statements)

References 8 publications

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“…However, these tools are not FDAapproved; NeuroQuant is. 15,16 It is hoped that, in the future, these tools will be more readily available to better supplement the detection capability of NeuroQuant.…”

Section: Discussionmentioning

confidence: 99%

Hippocampal Sclerosis Detection with NeuroQuant Compared with Neuroradiologists

Louis

Morita-Sherman

Jones

et al. 2020

AJNR Am J Neuroradiol

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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 ¼ 61) or not having hippocampal sclerosis on pathology (n ¼ 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 ¼ .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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Section: Discussionmentioning

confidence: 99%

Hippocampal Sclerosis Detection with NeuroQuant Compared with Neuroradiologists

Louis

Morita-Sherman

Jones

et al. 2020

AJNR Am J Neuroradiol

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“…Standard T 2 ‐weighted clinical acquisitions may be analyzed but are less sensitive than T2 relaxometry . Computational analysis of conventional fluid‐attenuated inversion recovery (FLAIR) images is also less sensitive and specific than T2 relaxometry …”

Section: Discussionmentioning

confidence: 99%

Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy

et al. 2017

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SummaryObjectiveHippocampal 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.MethodsFifty 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.ResultsHippocampal 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.SignificanceAutomated 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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“…In previous studies, the T 2 value increase was shown to be concordant with the putative seizure onset 7,8 ; the T 2 map has been shown to significantly outperform fluid attenuated inversion recovery (FLAIR) in differentiating sclerotic from healthy hippocampi in one study. 9 However, due to the long scan time, low spatial resolution, and lack of robustness of the conventional quantitative MRI techniques, clinical adoption of quantitative imaging has been limited.…”

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confidence: 99%

Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions

Jones

Deshmane

et al. 2018

Magnetic Resonance Imaging

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Background Conventional MRI can be limited in detecting subtle epileptic lesions or identifying active/epileptic lesions among widespread, multifocal lesions. Purpose We developed a high‐resolution 3D MR fingerprinting (MRF) protocol to simultaneously provide quantitative T1, T2, proton density, and tissue fraction maps for detection and characterization of epileptic lesions. Study type Prospective. Population National Institute of Standards and Technology (NIST) / International Society for Magnetic Resonance in Medicine (ISMRM) phantom, five healthy volunteers and 15 patients with medically intractable epilepsy undergoing presurgical evaluation with noninvasive or invasive electroclinical data. Field Strength/Sequence 3D MRF scans and routine clinical epilepsy MR protocols were acquired at 3 T. Assessment The accuracy of the T1 and T2 values were first evaluated using the NIST/ISMRM phantom. The repeatability was then estimated with both phantom and volunteers based on the coefficient of variance (CV). For epilepsy patients, all the maps were qualitatively reviewed for lesion detection by three independent reviewers (S.E.J., M.L., I.N.) blinded to clinical data. Region of interest (ROI) analysis was performed on T1 and T2 maps to quantify the multiparametric signal differences between lesion and normal tissues. Findings from qualitative review and quantitative ROI analysis were compared with patients' electroclinical data to assess concordance. Statistical Tests Phantom results were compared using R‐squared, and patient results were compared using linear regression models. Results The phantom study showed high accuracy with the standard values, with an R2 of 0.99. The volunteer study showed high repeatability, with an average CV of 4.3% for T1 and T2 in various tissue regions. For the 15 patients, MRF showed additional findings in four patients, with the remaining 11 patients showing findings consistent with conventional MRI. The additional MRF findings were highly concordant with patients' electroclinical presentation. Data Conclusion The 3D MRF protocol showed potential to identify otherwise inconspicuous epileptogenic lesions from the patients with negative conventional MRI diagnosis, as well as to correlate with different levels of epileptogenicity when widespread lesions were present. Level of Evidence: 3. Technical Efficacy Stage: 3. J. Magn. Reson. Imaging 2019;49:1333–1346.

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T2 mapping outperforms normalised FLAIR in identifying hippocampal sclerosis

Cited by 40 publications

References 8 publications

Hippocampal Sclerosis Detection with NeuroQuant Compared with Neuroradiologists

Hippocampal Sclerosis Detection with NeuroQuant Compared with Neuroradiologists

Automated T2 relaxometry of the hippocampus for temporal lobe epilepsy

Development of high‐resolution 3D MR fingerprinting for detection and characterization of epileptic lesions

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