Nico Hoffmann

Highly sensitive time-resolved thermography and multivariate image analysis of the cerebral cortex for intrasurgical diagnostics

Time-resolved thermography is a novel method to assess thermal variations and heterogeneities in tissue and blood. The recent generation of thermal cameras provides a sensitivity of less than mK. This high sensitivity in conjunction with non-invasive, label-free and radiation-free monitoring makes thermography a promising tool for intrasurgical diagnostics. In brain surgery, time-resolved thermography can be employed to distinguish between normal and anomalous tissue. In this study, we investigated and discussed the potential of time-resolved thermography in neurosurgery for the intraoperative detection and demarcation of tumor borders. Algorithms for segmentation, reduction of movement artifacts and image fusion were developed. The preprocessed image stacks were subjected to discrete wavelet transform to examine individual frequency components. K-means clustering was used for image evaluation to reveal similarities within the image sequence. The image evaluation shows significant differences for both types of tissue. Tumor and normal tissues have different time characteristics in heat production and transfer. Furthermore, tumor could be highlighted. These results demonstrate that time-resolved thermography is able to support the detection of tumors in a contactless manner without any side effects for the tissue. The intraoperative usage of time-resolved thermography improves the accuracy of tumor resections to prevent irreversible brain damage during surgery.

Wavelet Subspace Analysis of Intraoperative Thermal Imaging for Motion Filtering

Intraoperative thermography allows fast capturing of small temperature variations during neurosurgical operations. External influences induce periodic vibrational motion to the whole camera system superimposing signals of high-frequent neuronal activity, heart rate activity and injected perfusion tracers by motion artifacts. In this work, we propose a robust method to eliminate the effects induced by the vibrational motion allowing further inference of clinical information. For this purpose, an efficient wavelet shrinkage scheme is developed based on subspace analysis in 1D wavelet domain to recognize and remove motion related patterns. The approach does not require any specific motion modeling or image warping, making it fast and preventing image deformations. Promising results of a simulation study and by intraoperative measurements make this method a reliable and efficient method improving subsequent perfusion and neuronal activity analysis.

Motion correction of thermographic images in neurosurgery: Performance comparison

In this paper, the correction of motion-induced high frequency artifacts on sequences of thermographic images by a real-time capable approach based on Cellular Nonlinear Networks (CNN) is evaluated. For comparison, an offline analysis of frequency subspaces is presented and results will be compared. Both simulated data sets as well as data sets recorded during neurosurgery are considered as inputs and two measures are evaluated.

Learning Thermal Process Representations for Intraoperative Analysis of Cortical Perfusion During Ischemic Strokes

Thermal imaging is a non-invasive and marker-free approach for intraoperative measurements of small temperature variations. In this work, we demonstrate the abilities of active dynamic thermal imaging for analysis of tissue perfusion state in case of cerebral ischemia. For this purpose, a NaCl irrigation is applied to the exposed cortex during hemicraniectomy. The caused temperature changes are measured by a thermal imaging system whilst tissue heating is modeled by a double exponential function. Modeled temperature decay constants allow us to characterize tissue perfusion with respect to its dynamic thermal properties. As intraoperative imaging prevents the usage of computational intense parameter optimization schemes we discuss a deep learning framework that approximates these constants given a simple temperature sequence. The framework is compared to common Levenberg-Marquardt based parameter optimization approaches. The proposed deep parameter approximation framework shows good performance compared to numerical optimization with random initialization. We further validated the approximated parameters by an intraoperative case suffering acute cerebral ischemia. The results indicate that even approximated temperature decay constants allow us to quantify cortical perfusion. Latter yield a standardized representation of cortical thermodynamic properties and might guide further research regarding specific intraoperative therapies and characterization of pathologies with atypical cortical perfusion.

Motion correction of thermographic images in neurosurgery

Neurosurgery relies strongly on medical imaging techniques. However, many state-of-the-art diagnostic tools such as computer tomography (CT) and magneto-resonance imaging (MRI) cannot be applied during ongoing surgery in general. Our aim is to realize a multi-purpose imaging platform capable of real-time assistance to the surgeon. Therefore, we apply thermography: a non-invasive, contactless, marker-free technique that has been used in various medical applications, albeit some challenges remain. In this paper, we propose a Cellular Nonlinear Network-based image pre-processing for feature enhancing and extraction followed by a cepstrum-based motion correction algorithm allowing a real-time removal of breathing artifacts from thermographic images.

Intraoperative imaging of cortical perfusion by time-resolved thermography using cold bolus approach

During the past decade, thermographic cameras with high thermal and temporal resolution of up to 30 mK and 50 Hz, respectively, have been developed. These camera systems can be used to reveal thermal variations and heterogeneities of tissue and blood. Thus, they provide a fast, sensitive, noninvasive, and label-free application to investigate blood perfusion and to detect perfusion disorders. Therefore, time-resolved thermography is evaluated and tested for intraoperative imaging of the cerebral cortex during neurosurgeries. The motivation of this study is the intraoperative evaluation of the cortical perfusion by observing the temporal temperature curve of the cortex during and after the intravenous application of a cold bolus. The temperature curve caused by a cold bolus is influenced by thermodilution, depending on the temperature difference to the patient’s circulation, and the pattern of mixing with the patient’s blood. In this initial study, a flow phantom was used in order to determine the temperature variations of cold boli under stable conditions in a vascular system. The typical temperature profile of cold water passing by can be approximated by a bi- Gaussian function involving a set of four parameters. These parameters can be used to assess the cold bolus, since they provide information about its intensity, duration and arrival time. The findings of the flow phantom can be applied to thermographic measurements of the human cortex. The results demonstrate that time-resolved thermographic imaging is a suitable method to detect cold boli not only at a flow phantom but also at the human cortex.

Fast Mapping of the Eloquent Cortex by Learning L2 Penalties

The resection of brain tumors beneath eloquent areas of the human brain requires precise delineation of eloquent areas for maximum removal of tumor mass while minimizing the risk for postoperative functional deficits. Non-invasive mapping of eloquent areas can be carried out by intraoperative thermal imaging since neural activity alters the cortical temperature distribution. These characteristic changes in cortical temperature can be modeled by a response function. A prominent choice for this response function is the haemodynamic response function. However, the signal is typically superimposed by various effects such as motion artifacts, physiological effects, sensor drifts as well as autoregulation which have to be compensated.

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

Abstract 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

Abstract 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.

Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging

Abstract Intraoperative thermal neuroimaging is a novel intraoperative imaging technique for the characterization of perfusion disorders, neural activity and other pathological changes of the brain. It bases on the correlation of (sub-)cortical metabolism and perfusion with the emitted heat of the cortical surface. In order to minimize required computational resources and prevent unwanted artefacts in subsequent data analysis workflows foreground detection is a important preprocessing technique to differentiate pixels representing the cerebral cortex from background objects. We propose an efficient classification framework that integrates characteristic dynamic thermal behaviour into this classification task to include additional discriminative features. The first stage of our framework consists of learning this representation of characteristic thermal time-frequency behaviour. This representation models latent interconnections in the time-frequency domain that cover specific, yet a priori unknown, thermal properties of the cortex. In a second stage these features are then used to classify each pixel’s state with conditional random fields. We quantitatively evaluate several approaches to learning high-level features and their impact to the overall prediction accuracy. The introduction of high-level features leads to a significant accuracy improvement compared to a baseline classifier.