Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy.

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

The effect of errors in segmented attenuation maps on PET quantification.

PURPOSE: Accurate attenuation correction is important for PET quantification. Often, a segmented attenuation map is used, especially in MRI-based attenuation correction. As deriving the attenuation map from MRI images is difficult, different errors can be present in the segmented attenuation map. The goal of this paper is to determine the effect of these errors on quantification. METHODS: The authors simulated the digital XCAT phantom using the GATE Monte Carlo simulation framework and a model of the Philips Gemini TF. A whole body scan was simulated, spanning an axial field of view of 70 cm. A total of fifteen lesions were placed in the lung, liver, spine, colon, prostate, and femur. The acquired data were reconstructed with a reference attenuation map and with different attenuation maps that were modified to reflect common segmentation errors. The quantitative difference between reconstructed images was evaluated. RESULTS: Segmentation into five tissue classes, namely cortical bone, spongeous bone, soft tissue, lung, and air yielded errors below 5%. Large errors were caused by ignoring lung tissue (up to 45%) or cortical bone (up to 17%). The interpatient variability of lung attenuation coefficients can lead to errors of 10% and more. Up to 20% tissue misclassification from bone to soft tissue yielded errors below 5%. The same applies for up to 10% misclassification from lung to air. CONCLUSIONS: When using a segmented attenuation map, at least five different tissue types should be considered: cortical bone, spongeous bone, soft tissue, lung, and air. Furthermore, the interpatient variability of lung attenuation coefficients should be taken into account. Limited misclassification from bone to soft tissue and from lung to air is acceptable, as these do not lead to relevant errors.

Fast 3D iterative image reconstruction for SPECT with rotating slat collimators.

As an alternative to the use of traditional parallel hole collimators, SPECT imaging can be performed using rotating slat collimators. While maintaining the spatial resolution, a gain in image quality could be expected from the higher photon collection efficiency of this type of collimator. However, the use of iterative methods to do fully three-dimensional (3D) reconstruction is computationally much more expensive and furthermore involves slow convergence compared to a classical SPECT reconstruction. It has been proposed to do 3D reconstruction by splitting the system matrix into two separate matrices, forcing the reconstruction to first estimate the sinograms from the rotating slat SPECT data before estimating the image. While alleviating the computational load by one order of magnitude, this split matrix approach would result in fast computation of the projections in an iterative algorithm, but does not solve the problem of slow convergence. There is thus a need for an algorithm which speeds up convergence while maintaining image quality for rotating slat collimated SPECT cameras. Therefore, we developed a reconstruction algorithm based on the split matrix approach which allows both a fast calculation of the forward and backward projection and a fast convergence. In this work, an algorithm of the maximum likelihood expectation maximization (MLEM) type, obtained from a split system matrix MLEM reconstruction, is proposed as a reconstruction method for rotating slat collimated SPECT data. Here, we compare this new algorithm to the conventional split system matrix MLEM method and to a gold standard fully 3D MLEM reconstruction algorithm on the basis of computational load, convergence and contrast-to-noise. Furthermore, ordered subsets expectation maximization (OSEM) implementations of these three algorithms are compared. Calculation of computational load and convergence for the different algorithms shows a speedup for the new method of 38 and 426 compared to the split matrix MLEM approach and the fully 3D MLEM respectively and a speedup of 16 and 21 compared to the split matrix OSEM and the fully 3D OSEM respectively. A contrast-to-noise study based on simulated data shows that our new approach has comparable accuracy as the fully 3D reconstruction method. The algorithm developed in this study allows iterative image reconstruction of rotating slat collimated SPECT data with equal image quality in a comparable amount of computation time as a classical SPECT reconstruction.