Object Advances in brain imaging have allowed for more sophisticated mapping of crucial neural structures. Functional MRI (fMRI) measures local changes in blood oxygenation associated with changes in neural activity and is useful in mapping cortical activation. Applications of this imaging modality have generally been restricted to cooperative patients; however, fMRI has proven successful in localizing the motor cortex for neurosurgical planning in uncooperative children under sedation. The authors demonstrate that the use of fMRI to localize the visual cortex in sedated children can be safely and effectively performed, allowing for more accurate presurgical planning to spare visual structures. Methods Between 2007 and 2009, 11 children (age range 1–11 years) underwent fMRI for neurosurgical planning while under sedation. Blood oxygen level–dependent fMRI was performed to detect visual cortex activation during stimulation through closed eyelids. Visual stimulation was presented in block design with periods of flashing light alternated with darkness. Results Functional MRI was successful in identifying visual cortex in each of the 11 children tested. There were no complications with propofol sedation or the fMRI. All children suffered from epilepsy, 5 had brain tumors, and 1 had tuberous sclerosis. After fMRI was performed, 6 patients underwent surgery. Frameless stereotactic guidance was synchronized with fMRI data to design an approach to spare visual structures during resection. There were no cases where a false negative led to unexpected visual field deficits or other side effects of surgery. In 2 cases, the fMRI results demonstrated that the tracts were already disrupted: in one case from a prior tumor operation and in another from dysplasia. Conclusions Functional MRI for evaluation of visual pathways can be safely and reproducibly performed in young or uncooperative children under light sedation. Identification of primary visual cortex aids in presurgical planning to avoid vision loss in appropriately selected patients.

Considerable progress has been made in the management of diseases of the thoracic and abdominal aorta over the past decades, ranging from advances in open repair to the advent of minimally invasive endovascular techniques. Along with this comes an equivalent rise in imaging necessity for these patients, both in preoperative planning and postoperative surveillance. With the growing complexity and diversity of vascular procedures and techniques, it is essential to have a solid understanding of the imaging features and postoperative complications of these procedures to avoid imaging pitfalls. This review is an attempt to define the normal postoperative appearance and important complications of various open and endovascular surgical techniques of the thoracic and abdominal aorta.

Imaging of the venous system plays a vital role in the diagnosis and management of a wide range of clinically significant disorders. There have been great advances in venous imaging techniques, culminating in the use of magnetic resonance venography (MRV). Although MRV has distinct advantages in anatomic and quantitative cross sectional imaging without ionizing radiation, there are well-known challenges in acquisition timing and contrast administration in patients with renal impairment. The latest advancement involves the addition of new contrast media agents, which have emerged as valuable alternatives in these difficult scenarios. In this review, we will focus on a group of specific contrast agents called blood pool agents and discuss their salient features and clinical applications.

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The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.