Evaluation of multi-wavelengths LED-based photoacoustic imaging for maximum safe resection of glioma: a proof of concept study.

PURPOSE: A real-time intra-operative imaging modality is required to update the navigation systems during neurosurgery, since precise localization and safe maximal resection of gliomas are of utmost clinical importance. Different intra-operative imaging modalities have been proposed to delineate the resection borders, each with advantages and disadvantages. This preliminary study was designed to simulate the photoacoustic imaging (PAI) to illustrate the brain tumor margin vessels for safe maximal resection of glioma. METHODS: In this study, light emitting diode (LED)-based PAI was selected because of its lower cost, compact size and ease of use. We developed a simulation framework based on multi-wavelength LED-based PAI to further facilitate PAI during neurosurgery. This framework considers a multilayer model of the tumoral and normal brain tissue. The simulation of the optical fluence and absorption map in tissue at different depths was computed by Monte Carlo. Then, the propagation of initial photoacoustic pressure was simulated by using k-wave toolbox. RESULTS: To evaluate the LED-based PAI, we used three evaluation criteria: signal-to-noise ratio (SNR), contrast ratio (CR) and full width of half maximum (FWHM). Results showed that by using proper wavelengths, the vessels were recovered with the same axial and lateral FWHM. Furthermore, by increasing the wavelength from 532 to 1064 nm, SNR and CR were increased in the deep region. The results showed that vessels with larger diameters at same wavelength have a higher CR with average improvement 28%. CONCLUSION: Multi-wavelength LED-based PAI provides detailed images of the blood vessels which are crucial for detection of the residual glioma: The longer wavelengths like 1064 nm can be used for the deeper tumor margins, and the shorter wavelengths like 532 nm for tumor margins closer to the surface. LED-based PAI may be considered as a promising intra-operative imaging modality to delineate tumor margins.

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging.

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.