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.

Imaging Interstitial Fluid With MRI: A Narrative Review on the Associations of Altered Interstitial Fluid With Vascular and Neurodegenerative Abnormalities.

Interstitial fluid (ISF) refers to the fluid between the parenchymal cells and along the perivascular spaces (PVS). ISF plays a crucial role in delivering nutrients and clearing waste products from the brain. This narrative review focuses on the use of MRI techniques to measure various ISF characteristics in humans. The complementary value of contrast-enhanced and noncontrast-enhanced techniques is highlighted. While contrast-enhanced MRI methods allow measurement of ISF transport and flow, they lack quantitative assessment of ISF properties. Noninvasive MRI techniques, including multi-b-value diffusion imaging, free-water-imaging, T2 -decay imaging, and DTI along the PVS, offer promising alternatives to derive ISF measures, such as ISF volume and diffusivity. The emerging role of these MRI techniques in investigating ISF alterations in neurodegenerative diseases (eg, Alzheimer's disease and Parkinson's disease) and cerebrovascular diseases (eg, cerebral small vessel disease and stroke) is discussed. This review also emphasizes current challenges of ISF imaging, such as the microscopic scale at which ISF has to be measured, and discusses potential focus points for future research to overcome these challenges, for example, the use of high-resolution imaging techniques. Noninvasive MRI methods for measuring ISF characteristics hold significant potential and may have a high clinical impact in understanding the pathophysiology of neurodegenerative and cerebrovascular disorders, as well as in evaluating the efficacy of ISF-targeted therapies in clinical trials. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Advances in Image Processing for Epileptogenic Zone Detection with MRI.

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

Stereo-electroencephalography (SEEG) reference based on low-variance signals.

For a correct assessment of stereo-electroencephalographic (SEEG) recordings, a proper signal electrical reference is necessary. Such a reference might be physical or virtual. Physical reference can be noisy and a proper virtual reference calculation is often time-consuming. This paper uses the variance of the SEEG signals to calculate the reference from relatively low noise signals to reduce the contamination by distant sources, while maintaining negligible computing time.Ten patients with SEEG recordings were used in this study. 20-second long recordings from each patient, sampled at 5000 Hz, were used to calculate variances of SEEG signals and a low-variance (LV) subset of signals was selected for each patient. Consequently, 4 different reference signals were calculated using: 1) an average signal from WM contacts only (AVG_WM); 2) an average signal from LV contacts only (AVG_LV); 3) independent component analysis (ICA) method from WM contacts only (ICA_WM); and 4) ICA method from LV signals only (ICA_LV). Also, the original testing reference, an average signal from all SEEG contacts (AVG) was utilized. Finally, bipolar signals and average signals from anatomical structures were calculated and used to evaluate reference signals.91.7% of the WM SEEG contacts were found below the average variance. ICA_LV showed the best and AVG_WM the worst overall results. AVG_LV had the most positive impact on minimizing the mutual correlations between separate brain structures and correcting the outliers. The average processing time for ICA methods was 66.72 seconds and 0.7870 seconds for AVG methods (100 000 samples, 125.7±20.4 SEEG signals).Utilizing the LV data subset improves the reference signal. WM references are difficult to obtain and seem to be more susceptible to errors caused by low number of WM contacts in the dataset. ICA_LV can be considered as one of the best reference estimations, however the calculation is very demanding and time consuming. AVG_LV shows good and stable results, while it is based on a straightforward methodology and outstandingly fast calculation.