To compare by 7 Tesla (7T) magnetic resonance imaging (MRI) in patients with focal epilepsy who have non-lesional clinical MRI scans with healthy controls. 37 patients with focal epilepsy, based on clinical and electroencephalogram (EEG) data, with non-lesional MRIs at clinical field strengths and 21 healthy controls were recruited for the 7T imaging study. The MRI protocol consisted of high resolution T₁-weighted, T₂-weighted and susceptibility weighted imaging sequences of the entire cortex. The images were read by two neuroradiologists, who were initially blind to clinical data, and then reviewed a second time with knowledge of the seizure onset zone. A total of 25 patients had findings with epileptogenic potential. In five patients these were definitely related to their epilepsy, confirmed through surgical intervention, in three they co-localized to the suspected seizure onset zone and likely caused the seizures. In seven patients the imaging findings co-localized to the suspected seizure onset zone but were not the definitive cause, and ten had cortical lesions with epileptogenic potential that did not localize to the suspected seizure onset zone. There were multiple other findings of uncertain significance found in both epilepsy patients and healthy controls. The susceptibility weighted imaging sequence was instrumental in guiding more targeted inspection of the other structural images and aiding in the identification of cortical lesions. Information revealed by the improved resolution and enhanced contrast provided by 7T imaging is valuable in noninvasive identification of lesions in epilepsy patients who are non-lesional at clinical field strengths.

We present a method to calculate the electric (E)‐fields within and surrounding a human body in a gradient coil, including E‐fields induced by the changing magnetic fields and “conservative” E‐fields originating with the scalar electrical potential in the coil windings. In agreement with previous numerical calculations, it is shown that magnetically‐induced E‐fields within the human body show no real concentration near the surface of the body, where nerve stimulation most often occurs. Both the magnetically‐induced and conservative E‐fields are shown to be considerably stronger just outside the human body than inside it, and under some circumstances the conservative E‐fields just outside the body can be much larger than the magnetically‐induced E‐fields there. The order of gradient winding and the presence of conductive RF shield can greatly affect the conservative E‐field distribution in these cases. Though the E‐fields against the outer surface of the body are not commonly considered, understanding gradient E‐fields may be important for reasons other than peripheral nerve stimulation (PNS), such as potential interaction with electrical equipment. Magn Reson Med, 2006.

In this methods development, we present an ultra-high-field, diffusion-weighted MRI method to quantitatively assess u-fibers and use it to compare u-fiber counts in nonlesional epilepsy patients with controls. Emerging evidence implicates white matter abnormalities in nonlesional epilepsy, including the short-range, cortical–cortical connections, or u-fibers. Eight patients with nonlesional epilepsy and eight demographically matched controls underwent 7 T MRI consisting of a T1-weighted sequence (0.7 mm isotropic resolution) and high-angular-resolved diffusion-weighted MRI (1.05 mm isotropic resolution, 68 directions). MRI data were used to quantify u-fiber counts in known u-fiber populations on the basis of an atlas and fiber tractography. From tractography, connectivity matrices summarizing the u-fiber counts were computed. Quantitative group comparisons were performed on the connectivity matrices. U-fiber counts were found to be lower on average in patients with epilepsy than in healthy controls. The results indicate that the density or the number of u-fibers is reduced in patients with nonlesional epilepsy. Future work will focus on histological validation and determining whether differences in u-fiber counts can be used clinically to noninvasively identify seizure-onset zones.

Medical imaging tasks are very challenging due to the lack of publicly available labeled datasets. Hence, it is difficult to achieve high performance with existing deep learning models as they require a massive labeled dataset to be trained effectively. An alternative solution is to use pre-trained models and fine-tune them using a medical imaging dataset. However, all existing models are pre-trained using natural images, which represent a different domain from that of medical imaging; this leads to poor performance due to domain shift. To overcome these problems, we propose a pre-trained backbone using a collected medical imaging dataset with a self-supervised learning tool called a masked autoencoder. This backbone can be used as a pre-trained model for any medical imaging task, as it is trained to learn a visual representation of different types of medical images. To evaluate the performance of the proposed backbone, we use four different medical imaging tasks. The results are compared with existing pre-trained models. These experiments show the superiority of our proposed backbone in medical imaging tasks.