Atypical Abnormal Pulmonary Vein Drainage with Atrial Septal Defect: Surgical Treatment

Sinus venosus atrial septal defect (SV‐ASD) usually coexists with partial anomalous pulmonary vein connection (PAPVC). It is a difficult diagnosis in transthoracic echocardiography (TTE) due to eccentric position of defects. We present a rare case of atypical anatomical variation in PAPVC, which was never described before. Two right pulmonary veins drained into superior vena cava, which overrode SV‐ASD and interatrial septum, a third pulmonary vein into the right atrium. Complete diagnosis could not be set after TTE, nor transesophageal echocardiography, whereas angio‐CT was finally conclusive. This diagnostic approach allowed the surgical planning.     

Intraoperative cerebral blood flow monitoring in neurosurgery: A review of contemporary technologies and emerging perspectives

Intraoperative monitoring of cerebral blood flow (CBF) has become an invaluable adjunct to vascular and oncological neurosurgery, reducing the risk of postoperative morbidity and mortality. Several technologies have been developed during the last two decades, including laser-based techniques, videomicroscopy, intraoperative MRI, indocyanine green angiography, and thermography. Although these technologies have been thoroughly studied and clinically applied outside the operative room, current practice lacks an optimal technology that perfectly fits the workflow within the neurosurgical operative room. The different available technologies have specific strengths but suffer several drawbacks, mainly including limited spatial and/or temporal resolution. An optimal CBF monitoring technology should meet particular criteria for intraoperative use: excellent spatial and temporal resolution, integration in the operative workflow, real-time quantitative monitoring, ease of use, and non-contact technique. We here review the main contemporary technologies for intraoperative CBF monitoring and their current and potential future applications in neurosurgery.     

Optimal tracking control for non-zero-sum games of linear discrete-time systems via off-policy reinforcement learning

In this article, a model-free off-policy reinforcement learning algorithm is applied to address the optimal tracking problem based on multiplayer non-zero-sum games for discrete-time linear systems. In contrast to the traditional method and the policy iteration method for solving the optimal tracking problems, the proposed algorithm operates with the system data rather than the knowledge of the system dynamics. For performing the proposed algorithm, an auxiliary augmented system is constructed via assembling the original system and the reference trajectory while a discount factor is introduced into the performance indexes. It is analyzed that the solutions of the proposed algorithm converge to the Nash equilibrium and the result is not influenced by the probing noise. Two simulations are presented to verify the feasibility and effectiveness of the proposed algorithm.