DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.

BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. METHODS: Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. RESULTS: SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. CONCLUSIONS: The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

Surgical complications of vagus nerve stimulation surgery: A 14-years single-center experience.

Introduction: Vagus nerve stimulation (VNS) is the most frequently used neuromodulation treatment for Drug-Resistant Epilepsy (DRE) patients. Complications of VNS surgery include surgical site infection and unilateral vocal cord paresis. Complication rates vary across studies. Research question: What is the safety profile of VNS related surgeries? Materials and methods: Retrospective cohort study using patient files of DRE-patients who had undergone primary implantation of a VNS-system, replacement of the VNS pulse generator, replacement of the lead, replacement of both pulse generator and lead, or VNS removal surgery in the Maastricht UMC+. Multiple Imputation was used for missing data. Univariable and multivariable logistic regression analysis were performed to analyze possible risk factors, in case of a small sample size, an independent-samples t-test and Fisher's exact test or Pearson's X2-test were used. The complication rate was calculated as percentage. Results: This study included a total of 606 VNS surgical procedures, leading to 67 complications of which 3 permanent complications. Complication rate after primary implantation was 13.4%; 2,5% for pulse generator replacement; 21.4% for lead revision and 27.3% for complete VNS removal. No statistically significant results were found when analyzing the results of adults and children <18 years separately. Discussion and conclusion: Complication rates of VNS-related surgeries in our own institutional series are low and comparable to previously reported series. VNS surgery is a relatively safe procedure. The complication rate differs per type of surgery and mean surgery duration was longer for patients with complications after lead revision surgery compared to patients without complications.

Resective Epilepsy Surgery, QUality of life and Economic evaluation (RESQUE): the change in quality of life after resective epilepsy surgery-protocol for a multicentre, prospective cohort study.

INTRODUCTION: Resective epilepsy surgery is often seen as a last resort when treating drug-resistant epilepsy. Positive results on quality of life (QoL) and economic benefits after surgery argue for a less restrictive attitude towards epilepsy surgery for drug-resistant epilepsy. QoL and economic benefits are country-dependent. The objective of the Resective Epilepsy Surgery, QUality of life and Economic evaluation (RESQUE) trial is to evaluate the change in QoL before and after epilepsy surgery in Dutch people with drug-resistant epilepsy. The results will form part of an economic evaluation of epilepsy surgery in people with epilepsy (PWE) in The Netherlands. METHODS AND ANALYSIS: A longitudinal prospective multicentre cohort study involving 100 PWE undergoing epilepsy surgery between 2019 and 2025 is being performed in three Dutch academic hospitals. Excluded are PWE who have a lower level of intelligence (TIQ<70) or who do not master the Dutch language. Before surgery and 3, 6, 12 and 24 months after surgery, PWE receive validated online questionnaires (QOLIE-31, EQ-5D, iMCQ and iPCQ) on QoL, cost of care, expectations and satisfaction. Primary outcome is the change in QoL. Secondary outcomes are change in generic QoL, seizure reduction (International League Against Epilepsy Outcome Classification), medical consumption, productivity, the correlation between QoL and seizure reduction and expectation of and satisfaction with the surgery. ETHICS AND DISSEMINATION: The study design has been approved by the Medical Ethics Review Committee (METC) of Maastricht UMC+ (2019-1134) and the Amsterdam UMC (vu). At the time of writing, UMC Utrecht is in the process of considering approval. The study will be conducted according to the Dutch Medical Research Involving Human Subjects Act and the Declaration of Helsinki. The results will be publicly disclosed and submitted for publication in international peer-reviewed scientific journals. There is no veto on publication by the involved parties. TRIAL REGISTRATION: NL8278; Pre-results.