Purpose: To investigate reasons for patients not proceeding to resective epilepsy surgery after subdural grid evaluation (SDE). To correlate noninvasive investigation results with invasive EEG observations in a set of patients with nonlesional brain MRIs. Methods: Retrospective study of adult epilepsy patients undergoing SDE during an 8-year period at Cleveland Clinic. Construction of semiquantitative "scores" and Bayesian predictors summarizing the localizing value and concordance between noninvasive parameters in a subset with nonlesional MRIs. Results: One hundred forty patients underwent SDE, 25 of whom were subsequently denied resective surgery. In 10 of 25, this was caused by a nonlocalizing subdural ictal EEG onset. Eight of 10 such patients were nonlesional on MRI. Among all nonlesional patients (n = 34 of 140), n 1 = 10 of 34 patients had nonlocalizing and n2 = 24 of 34 had localizing, subdural ictal onsets. As groups, n1 and n 2 were statistically disjoint relative to their noninvasive scores. Bayesian measures predictive of focal invasive ictal EEG were highest for complete concordance of noninvasive parameters, decreasing with lesser degrees of concordance. A localizing scalp interictal EEG was a particularly good Bayesian prognosticator. Conclusions: A small but significant proportion of SDE patients are denied subsequent therapeutic resective surgery. This is due to several reasons, including a nonlocalizing intracranial ictal EEG. The majority of such patients have nonlesional MRIs. The noninvasive data may be summarized by a semiquantitative score, as well as Bayesian likelihood ratios, which correlate with subsequent invasive outcome. This approach may find use in the selection and counseling of potential surgical candidates offered SDE.

The objective of the current research was to develop an automated algorithm with no or little user assistance for extraction of Subdural Grid Electrodes (SGE) from post-implant MRI scans for epilepsy surgery. The algorithm utilizes the knowledge about the artifacts created by Subdural Electrodes (SE) in MRI scans. Also the algorithm does not only extract individual electrodes, but it also extracts them as a SGE structures. Information about the number and type of implanted electrodes is recorded during the surgery [1]. This information is used to reduce the search space and produce better results. Currently, the extraction of SGE from post-implant MRI scans is performed manually by a technologist [1, 2, 3]. It is a time-consuming process, requiring on average a few hours, depending on the number of implanted SE. In addition, the process does not conserve the geometry of the structures, since electrodes are identified individually. Usually SGE extraction is complicated by nearby artifacts, making manual extraction a non-trivial task that requires a good visualization of 3D space and orientation of SGE in it. Currently, most of the technologists use 2D slice viewers for extraction of SGE from 3D MRI scans. There is no commercial software to perform the automated extraction task. The only algorithm suggested in the literature is [4]. The goal of the proposed algorithm is to improve the performance of the algorithm in [4]. As a goal, the proposed algorithm performs extraction of SGE not only for individual electrodes, but by applying geometric constraints on SGE.