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The precise identification and preservation of functional brain areas during neurosurgery are crucial for optimizing surgical outcomes and minimizing postoperative deficits. Intraoperative imaging plays a vital role in this context, offering insights that guide surgeons in protecting critical cortical regions.
Aim
We aim to evaluate and compare the efficacy of intraoperative thermal imaging (ITI) and intraoperative optical imaging (IOI) in detecting the primary somatosensory cortex, providing a detailed assessment of their potential integration into surgical practice.
Approach
Data from nine patients undergoing tumor resection in the region of the somatosensory cortex were analyzed. Both IOI and ITI were employed simultaneously, with a specific focus on the areas identified as the primary somatosensory cortex (S1 region). The methodologies included a combination of imaging techniques during distinct phases of rest and stimulation, confirmed by electrophysiological monitoring of somatosensory evoked potentials to verify the functional areas identified by both imaging methods. The data were analyzed using a Fourier-based analytical framework to distinguish physiological signals from background noise.
Results
Both ITI and IOI successfully generated reliable activity maps following median nerve stimulation. IOI showed greater consistency across various clinical scenarios, including those involving cortical tumors. Quantitative analysis revealed that IOI could more effectively differentiate genuine neuronal activity from artifacts compared with ITI, which was occasionally prone to false positives in the presence of cortical abnormalities.
Conclusions
ITI and IOI produce comparable functional maps with moderate agreement in Cohen’s kappa values. Their distinct physiological mechanisms suggest complementary use in specific clinical scenarios, such as cortical tumors or impaired neurovascular coupling. IOI excels in spatial resolution and mapping reliability, whereas ITI provides additional insights into metabolic changes and tissue properties, especially in pathological areas. Combined, these modalities could enhance the understanding and analysis of functional and pathological processes in complex neurosurgical cases.
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TOPICS: Artificial neural networks, Data modeling, Skin, RGB color model, Education and training, Hyperspectral imaging, Tissues, Nervous system, Performance modeling, In vivo imaging
Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).
Aim
We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.
Approach
We propose a two-component system for extracting physiological parameters from hyperspectral images. First, our system models the relationship between the measured spectra and the tissue parameters as a distribution rather than a point estimate and is thus able to generate multiple possible solutions. Second, the proposed tissue parameters are then refined using the neural network that approximates the biological tissue model.
Results
The proposed model was tested on simulated and in vivo data. It outperformed current models with an overall mean absolute error of 0.0141 and can be used as a faster alternative to the IAD algorithm.
Conclusions
Results suggest that Bayesian neural networks coupled with the approximation of a biological tissue model can be used to reliably and accurately extract tissue properties from hyperspectral images on the fly.
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Personalized photodynamic therapy (PDT) treatment planning requires knowledge of the spatial and temporal co-localization of photons, photosensitizers (PSs), and oxygen. The inter- and intra-subject variability in the photosensitizer concentration can lead to suboptimal outcomes using standard treatment plans.
Aim
We aim to quantify the PS spatial variation in tumors and its effect on PDT treatment planning solutions.
Approach
The spatial variability of two PSs is imaged at various spatial resolutions for an orthotopic rat glioma model and applied in silico to human glioblastoma models to determine the spatial PDT dose, including in organs at risk. An open-source interstitial photodynamic therapy (iPDT) planning tool is applied to these models, deriving the spatial photosensitizer quantification resolution that consistently impacts iPDT source placement and power allocation.
Results
The ex vivo studies revealed a bimodal photosensitizer distribution in the tumor. The concentration of the PS can vary by a factor of 2 between the tumor core and rim, with slight variation within the core but a factor of 5 in the rim. An average sampling volume of 1mm3 for photosensitizer quantification will result in significantly different iPDT planning solutions for each case.
Conclusions
Assuming homogeneous photosensitizer distribution results in suboptimal therapeutic outcomes, we highlight the need to predict the photosensitizer distribution before source placement for effective treatment plans.
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