Intraoperative mapping of the sensory cortex by time-resolved thermal imaging.

The resection of brain tumor requires a precise distinction between eloquent areas of the brain and pathological tumor tissue in order to improve the extent of resection as well as the patient's progression free survival time. In this study, we discuss mathematical tools necessary to recognize neural activity using thermal imaging cameras. The main contribution to thermal radiation of the exposed human cortex is regional cerebral blood flow (CBF). In fact, neurovascular coupling links neural activity to changes in regional CBF which in turn affects the cortical temperature. We propose a statistically sound framework to visualize neural activity of the primary somatosensory cortex. The framework incorporates a priori known experimental conditions such as the thermal response to neural activity as well as unrelated effects induced by random neural activity and autoregulation. These experimental conditions can be adopted to certain electrical stimulation protocols so that the framework allows to unveil arbitrary evoked neural activity. The method was applied to semisynthetic as well as two intraoperative cases with promising results as we were able to map the eloquent sensory cortex with high sensitivity. Furthermore, the results were validated by anatomical localization and electrophysiological measurements.

Framework for 2D-3D image fusion of infrared thermography with preoperative MRI.

Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.

A divide and conquer strategy for the maximum likelihood localization of low intensity objects.

In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.