2018
DOI: 10.3174/ajnr.a5814
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Clinical Value of Hybrid TOF-PET/MR Imaging–Based Multiparametric Imaging in Localizing Seizure Focus in Patients with MRI-Negative Temporal Lobe Epilepsy

Abstract: BACKGROUND AND PURPOSE: Temporal lobe epilepsy is the most common type of epilepsy. Early surgical treatment is superior to prolonged medical therapy in refractory temporal lobe epilepsy. Successful surgical operations depend on the correct localization of the epileptogenic zone. This study aimed to evaluate the clinical value of hybrid TOF-PET/MR imaging-based multiparametric imaging in localizing the epileptogenic zone in patients with MR imaging-negative for temporal lobe epilepsy. MATERIALS AND METHODS: Tw… Show more

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Cited by 38 publications
(41 citation statements)
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“…The decay-and attenuation-corrected PET images were reconstructed using an ordered subset expectation maximization algorithm (8 iterations, 32 subsets, and full width at half maximum of a Gaussian filter of 3.0 mm) with ToF and point-spread function. The reconstructed PET image matrix was 192 × 192, the field of view was 350 × 350 mm 2 , and the voxel size was 1.82 × 1.82 × 2.78 mm 3 , with spatial resolution of 4.5 mm [25][26][27].…”
Section: Pet/mri Data Acquisitionmentioning
confidence: 99%
“…The decay-and attenuation-corrected PET images were reconstructed using an ordered subset expectation maximization algorithm (8 iterations, 32 subsets, and full width at half maximum of a Gaussian filter of 3.0 mm) with ToF and point-spread function. The reconstructed PET image matrix was 192 × 192, the field of view was 350 × 350 mm 2 , and the voxel size was 1.82 × 1.82 × 2.78 mm 3 , with spatial resolution of 4.5 mm [25][26][27].…”
Section: Pet/mri Data Acquisitionmentioning
confidence: 99%
“…With PET/MRI guidance, we were able to localize hypometabolic epileptic areas more precisely, and therefore could reduce the number of SEEG electrodes needed. The number of SEEG electrodes was reduced to 7.07 ± 1.85 (range, [6][7][8][9][10][11][12][13][14] electrodes for each patient, which had two main advantages: reduced brain injury and decreased patient financial burdens. Hybrid PET/MRI helped to navigate SEEG to localize the SOZ in 35 patients (83.33 %) and showed the same SOZ localization with SEEG in 27 patients (64.29 %), values that were defined as being concordant.…”
Section: Utility Of Hybrid Pet/mri In Navigating Seeg Electrode Implamentioning
confidence: 99%
“…Previous studies have shown increased accuracy of hybrid PET/MRI imaging compared to separate MRI imaging and PET/CT for SOZ localization with less radiation exposure [9][10][11]. However, until now, very few hybrid PET/MRI data have been available for navigating SEEG electrode implantation in MRI-negative refractory epilepsy.…”
Section: Introductionmentioning
confidence: 99%
“…In our cohort, the initial 1.5 T MRI evaluation was on average eight months prior to the clinically indicated PET/CT. Acquiring PET and diffusion MRI scans separately can create spatial and temporal registration problems, making it difficult to accurately identify the seizure-onset zone and map its effects on brain structure and function undergoing disease-related changes (Wang et al 2018;Shang et al 2018). Misalignment errors are usually due to the subject's head position being different in image space between scans which are significantly minimized by hybrid PET/MRI.…”
Section: Resultsmentioning
confidence: 99%
“…Glucose hypometabolic regions of interest (ROIs) are often identified by visual assessment of FDG-PET images, however, some abnormalities may be missed during this process. Therefore, semi-quantitative approaches such as asymmetry index (AI) mapping have been proposed to aid visual detection of hypometabolic PET ROIs (Henry et al 1990;Rausch et al 1994;Van Bogaert et al 2000;Didelot et al 2010;Boscolo Galazzo et al 2016;Anazodo et al 2018;Kamm et al 2018;Shang et al 2018). AI mapping investigates metabolic abnormalities by measuring the voxel-wise difference in cerebral glucose metabolism between hemispheres and has been shown to be a very sensitive biomarker for epileptogenicity (Didelot et al 2010;Boscolo Galazzo et al 2016).…”
Section: Introductionmentioning
confidence: 99%