Ing. Kateřina Macková

Introduction: The quality of fluorodeoxyglucose positron emission tomography (FDG-PET) images is limited by low effective resolution, which leads to blurring. The resolution can be additionally improved by partial volume correction (PVC) algorithms. The key input parameter influencing PVC performance is the correct specification of full width at half maximum (FWHM) of the scanner point spread function (PSF). The FWHM is standardly measured using a point source phantom under the standard protocol defined by the National Electrical Manufacturers Association (NEMA). However, the PSF varies in real conditions of human/animal neuroimaging and must be estimated individually. Therefore, a new method for fully automatic estimation of FWHM from FDG-PET images of the brain was proposed without the need for information on the scanner and protocol used. Methods: The method was based on estimating the parameters of edge spread function at the border of the gray matter from the FDG-PET image intensity function. The FWHM estimation algorithm was applied to FDG-PET brain images of 31 candidates for surgical epilepsy treatment in Motol University Hospital. The estimates were compared with FWHM measured according to NEMA standard on the same PET-CT scanners (Siemens Biograph mCT: 16 patients, Siemens Biograph Vision: 15 patients). Results: The median (interquartile range) value of estimated FWHM was 4 (3.9 - 4.1) mm, which was 11% lower than measured 4.5 mm for Siemens Biograph mCT scanner in transversal direction, and 6% lower 3.4 (3.3 -3.6) mm vs. measured 3.2 mm for Biograph Vision scanner. In the axial direction, estimated value 4.7 (4.5 - 5.2) mm was the same as the measured 4.7 mm for the Siemens Biograph mCT scanner, and 6% higher 3.6 (3.4 -3.8) mm vs. measured 3.4 mm for the Siemens Biograph Vision scanner. Conclusion: The proposed method can fully automatically estimate FWHM of PET scanner PSF only based on FDG-PET image. The estimates agreed with FWHM measured according to NEMA standard. FWHM estimation improves the performance of PVC algorithms that increase the resolution of FDG-PET images for the glucose metabolism assessment.

Objective: Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. Methods: This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. Results: Patients with FCD type II had a significantly higher IED rate (p<0.005), a shorter inter-discharge interval within RD episodes (p<0.003), sleep influence on decreased RD periodicity (p<0.036), and longer RD episode duration (p<0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. Conclusion: Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. Significance: Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.