• The Korean Journal of Nuclear Medicine
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• v.29 no.4
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• pp.445-450
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• 1995

The epileptogenic zones should be localized precisely before surgical resection of these zones in intractable epilepsy. The localization is more difficult in patients with neocortical epilepsy than in patients with temporal lobe epilepsy. This study aimed at evaluation of the usefulness of ictal brain perfusion SPECT for the localization of epileptogenic zones in neocortical epilepsy. We compared the performance of ictal SPECT with MRI referring to ictal scalp electroencephalography(sEEG). Ictal $^{99m}Tc$-HMPAO SPECT were done in twenty-one patients. Ictal EEG were also obtained during video monitoring. MRI were reviewd. According to the ictal sEEG and semiology, 8 patients were frontal lobe epilepsy, 7 patients were lateral temporal lobe epilepsy, 2 patients were parietal lobe epilepsy, and 4 patients were occipital lobe epilepsy. Ictal SPECT showed hyperperfusion in 14 patients(67%) in the zones which were suspected to be epileptogenic according to ictal EEG and semiology. MRI found morphologic abnormalities in 9 patients(43%). Among the 12 patients, in whom no epileptogenic zones were revealed by MRI, ictal SPECT found zones of hyperperfusion concordant with ictal SEEG in 9 patients(75%). However, no zones of hyperperfusion were found in 4 among 9 patients who were found to have cerebromalacia, abnormal calcification and migration anomaly in MRI. We thought that ictal SPECT was useful for localization of epileptogenic zones in neocortical epilepsy and especially in patients with negative findings in MRI.

• The Korean Journal of Nuclear Medicine
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• v.32 no.1
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• pp.20-31
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• 1998

A robust algorithm to disclose and display the difference of ictal and interictal perfusion may facilitate the detection of ictal hyperfusion foci. Diagnostic performance of localizing epileptogenic zones with subtracted SPECT images was compared with the visual diagnosis using ictal and interictal SPECT, MR, or PET. Ietal and interictal Tc-99m-HMPAO cerebral perfusion SPECT images of 48 patients(pts) were processed to get parametric subtracted images. Epileptogenic foci of all pts were diagnosed by seizure free state after resection of epileptogenic zones. In subtraction SPECT, we used normalized difference ratio of pixel counts(ictal-interictal)/interictal ${\times}100%$) after correcting coordinates of ictal and interictal SPECT in semi-automatized 3-dimensional fashion. We found epileptogenic zones in subtraction SPECT and compared the performance with visual diagnosis of ictal and interictal SPECT, MR and PET using post-surgical diagnosis as gold standard. The concordance of subtraction SPECT and ictal-interictal SPECT was moderately good(kappa=0.49). The sensitivity of ictal-interictal SPECT was 73% and that of subtraction SPECT 58%. Positive predictive value of ictal-interictal SPECT was 76% and that of subtraction SPECT was 64%. There was no statistical difference between sensitivity or positive predictive values of subtraction SPECT and ictal-interictal SPECT, MR or PET. Such was also the case when we divided patients into temporal lobe epilepsy and neocortical epilepsy. We conclude that subtraction SPECT we produced had equivalent diagnostic performance compared with ictal-interictal SPECT in localizing epileptogenic zones. Additional value of these subtraction SPECT in clinical interpretation of ictal and interictal SPECT should be further evaluated.

• The Korean Journal of Nuclear Medicine
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• v.35 no.3
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• pp.131-141
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• 2001

Purpose: We evaluated the sensitivity of the F-18 FDG PET by visual assessment and statistical parametric mapping (SPM) analysis for the localization of the epileptogenic zones in frontal lobe epilepsy. Materials and Methods: Twenty-four patients with frontal lobe epilepsy were examined. All patients exhibited improvements after surgical resection (Engel class I or II). Upon pathological examination, 18 patients revealed cortical dysplasia, 4 patients revealed tumor, and 2 patients revealed cortical scar. The hypometabolic lesions were found in F-18 FDG PET by visual assessment and SPM analysis. On SPM analysis, cutoff threshold was changed. Results: MRI showed structural lesions in 12 patients and normal results in the remaining 12. F-18 FDG PET correctly localized epileptogenic zones in 13 patients (54%) by visual assessment. Sensitivity of F-18 FDG PET in MR-negative patients (50%) was similar to that in MR-positive patients (67%). On SPM analysis, sensitivity decreased according to the decrease of p value. Using uncorrected p value of 0.05 as threshold, sensitivity of SPM analysis was 53%, which was not statistically different from that of visual assessment. Conclusion: F-18 FDG PET was sensitive in finding epileptogenic zones by revealing hypometabolic areas even in MR-negative patients with frontal lobe epilepsy as well as in MR-positive patients. SPM analysis showed comparable sensitivity to visual assessment and could be used as an aid in the diagnosis of epileptogenic zones in frontal lobe epilepsy.

• The Korean Journal of Nuclear Medicine
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• v.33 no.3
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• pp.262-272
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• 1999

Purpose: Interictal F-18-fluorodeoxyglucose (FDG) PET and ictal Tc-99m-HMPAO SPECT are found to be useful in localizing epileptogenic zones in neocortical lateral temporal or frontal lobe epilepsy. We investigated whether interictal F-18-FDG PET or ictal Tc-99m-HMPAO SPECT was useful to find epileptogenic Bones in occipital lobe epilepsy (OLE). Materials and Methods: We reviewed patterns of hypometabolism in interictal F-18-FDG PET and of hyperperfusion in ictal Tc-99m-HMPAO SPECT in 17 OLE patients (mean age=$27{\pm}6.8$ year, M:F= 10:7, injection time= $30{\pm}17$ sec). OLE was diagnosed based on invasive electroencephalography (EEG) study, surgery and post-surgical outcome (Engel class I in all for average 14 months). Results: Epileptogenic zones were correctly localized in 9 (60%) out of 15 patients by interictal F-18-FDG PET. Epiletogenic hemispheres were correctly lateralized in 14 patients (93%). By ictal Tc-99m-HMPAO SPECT, epileptogenic hemispheres were correctly lateralized in 13 patients (76%), but localization was possible only in 3 patients (18%). Among patients who showed no abnormality with MR imaging and no correct localization with ictal Tc-99m-HMPAO SPECT, interictal F-18-FDG PET was helpful in 2 patients. Conclusion: Ictal Tc-99m-HMPAO SPECT was helpful in lateralization but not in localization in OLE. Interictal F-18-FDG PET was helpful for localization of epileptogenic zones even in patients with ambiguous MR or ictal SPECT findings.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.2
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• pp.97-101
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• 2007

Correct localization of epileptogenic zone is important for the successful epilepsy surgery. Both ictal perfusion single photon emission computed tomography (SPECT) and interictal F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information in the presurgical localization of intractable partial epilepsy. These imaging modalities have excellent diagnostic sensitivity in medial temporal lobe epilepsy and provide good presurgical information in neocortical epilepsy. Also provide functional information about cellular functions to better understand the neurobiology of epilepsy and to better define the ictal onset zone, symptomatogenic zone, propagation pathways, functional deficit zone and surround inhibition zones. Multimodality imaging and developments in analysis methods of ictal perfusion SPECT and new PET ligand other than FDG help to better define the localization.

• The Korean Journal of Nuclear Medicine
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• v.38 no.1
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• pp.30-40
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• 2004

Purpose: The aims of this study were to find brain regions in which gray matter volume was reduced and to show the capability of voxel-based morphometry (VBM) analysis for lateralizing epileptogenic zones in medial temporal lobe epilepsy (mTLE). The findings were compared with fluorodeoxyglucose positron omission tomography (FDG PET). Materials and Methods: MR T1-weighted images of 12 left mTLE and 11 right mTLE patients were compared with those of 37 normal controls. Images were transformed to standard MNI space and averaged in order to create study-specific brain template. Each image was normalized to this local template and brain tissues were segmented. Modulation VBM analysis was performed in order to observe gray matter volume change. Gray matter was smoothed with a Gaussian kernel. After these preprocessing, statistical analysis was peformed using statistical parametric mapping software (SPM99). FDG PET images were compared with those of 22 normal controls using SPM. Results: Gray matter volume was significantly reduced in the left amygdala and hippocampus in left mTLE. In addition, volume of cerebellum, anterior cingulate, and fusiform gyrus in both sides and left insula was reduced. In right mTLE, volume was reduced significantly in right hippocampus. In contrast, FDG uptake was decreased in broad areas of left or right temporal lobes in left TLE and right TLE, respectively. Conclusions: Gray matter loss was found in the ipsilateral hippocampus by modulation VBM analysis in medial temporal lobe epilepsy. This VBM analysis might be useful in lateralizing the epileptogenic zones in medial temporal lobe epilepsy, while SPM analysis of FDG PET disclosed hypometabolic epileptogenic zones.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.233-240
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• 2004

Purpose: We developed a computer-aided classifier using artificial neural network (ANN) to discriminate the cerebral metabolic pattern of medial and lateral temporal lobe epilepsy (TLE). Materials and Methods: We studied brain F-18-FDG PET images of 113 epilepsy patients sugically and pathologically proven as medial TLE (left 41, right 42) or lateral TLE (left 14, right 16). PET images were spatially transformed onto a standard template and normalized to the mean counts of cortical regions. Asymmetry indices for predefined 17 mirrored regions to hemispheric midline and those for medial and lateral temporal lobes were used as input features for ANN. ANN classifier was composed of 3 independent multi-layered perceptrons (1 for left/right lateralization and 2 for medial/lateral discrimination) and trained to interpret metabolic patterns and produce one of 4 diagnoses (L/R medial TLE or L/R lateral TLE). Randomly selected 8 images from each group were used to train the ANN classifier and remaining 51 images were used as test sets. The accuracy of the diagnosis with ANN was estimated by averaging the agreement rates of independent 50 trials and compared to that of nuclear medicine experts. Results: The accuracy in lateralization was 89% by the human experts and 90% by the ANN classifier Overall accuracy in localization of epileptogenic zones by the ANN classifier was 69%, which was comparable to that by the human experts (72%). Conclusion: We conclude that ANN classifier performed as well as human experts and could be potentially useful supporting tool for the differential diagnosis of TLE.