A Novel Language Paradigm for Intraoperative Language Mapping: Feasibility and Evaluation

(1) Background—Mapping language using direct cortical stimulation (DCS) during an awake craniotomy is difficult without using more than one language paradigm that particularly follows the demand of DCS by not exceeding the assessment time of 4 s to prevent intraoperative complications. We designed an intraoperative language paradigm by combining classical picture naming and verb generation, which safely engaged highly relevant language functions. (2) Methods—An evaluation study investigated whether a single trial of the language task could be performed in less than 4 s in 30 healthy subjects and whether the suggested language paradigm sufficiently pictured the cortical language network using functional magnetic resonance imaging (fMRI) in 12 healthy subjects. In a feasibility study, 24 brain tumor patients conducted the language task during an awake craniotomy. The patients’ neuropsychological outcomes were monitored before and after surgery. (3) Results—The fMRI results in healthy subjects showed activations in a language-associated network around the (left) sylvian fissure. Single language trials could be performed within 4 s. Intraoperatively, all tumor patients showed DCS-induced language errors while conducting the novel language task. Postoperatively, mild neuropsychological impairments appeared compared to the presurgical assessment. (4) Conclusions—These data support the use of a novel language paradigm that safely monitors highly relevant language functions intraoperatively, which can consequently minimize negative postoperative neuropsychological outcomes

Joint Superresolution and Rectification for Solar Cell Inspection

Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multiframe superresolution (MFSR) to a sequence of low-resolution measurements. In addition, the rectification and removal of lens distortion simplifies subsequent analysis. Therefore, we propose to fuse this preprocessing with standard MFSR algorithms. This is advantageous, because we omit a separate processing step, the motion estimation becomes more stable and the spacing of high-resolution pixels on the rectified module image becomes uniform w.r.t. the module plane, regardless of perspective distortion. We present a comprehensive user study showing that MFSR is beneficial for defect recognition by human experts and that the proposed method performs better than the state-of-the-art. Furthermore, we apply automated crack segmentation and show that the proposed method performs 3× better than bicubic upsampling and 2× better than the state-of-the-art for automated inspection