A robust motion correction technique for infrared thermography during awake craniotomy

Abstract

Purpose Intraoperative infrared thermography is an emerging technique for image-guided neurosurgery, whereby physiological and pathological processes result in temperature changes over space and time. However, motion during data collection leads to downstream artifacts in thermography analyses. We develop a fast, robust technique for motion estimation and correction as a preprocessing step for brain surface thermography recordings. Methods A motion correction technique for thermography was developed which approximates the deformation field associated with motion as a grid of two-dimensional bilinear splines (Bispline registration), and a regularization function was designed to constrain motion to biomechanically feasible solutions. The performance of the proposed Bispline registration technique was compared to phase correlation, a band-stop filter, demons registration, and the Horn–Schunck and Lucas–Kanade optical flow techniques. Results All methods were analyzed using thermography data from ten patients undergoing awake craniotomy for brain tumor resection, and performance was compared using image quality metrics. The proposed method had the lowest mean-squared error and the highest peak-signal-to-noise ratio of all methods tested and performed slightly worse than phase correlation and Demons registration on the structural similarity index metric (p < 0.01, Wilcoxon signed-rank test). Band-stop filtering and the Lucas–Kanade method were not strong attenuators of motion, while the Horn–Schunck method was well-performing initially but weakened over time. Conclusion Bispline registration had the most consistently strong performance out of all the techniques tested. It is relatively fast for a nonrigid motion correction technique, capable of processing ten frames per second, and could be a viable option for real-time use. Constraining the deformation cost function through regularization and interpolation appears sufficient for fast, monomodal motion correction of thermal data during awake craniotomy.

A Novel Intraoperative Mapping Device Detects the Thermodynamic Response Function

“…Data were downsampled to 5 Hz and cropped to an epoch duration of 20 s. All data were used in the analysis except the first epoch from patient 4, which was excluded due to a sudden large motion event. Optimal motion correction parameters have been found previously for this downsampling rate: image downsampling rate of 4, grid downsampling rate of 4, regularization coefficient of 0.008, and four optimization steps per frame [25]. These parameters were used for the base pyramid level, for which the upper pyramid level doubled both downsampling rates.…”

“…As the temperature gradients with respect to space typically exceed the temperature gradients with respect to time, motion-related temperature changes become the dominant signal, interfering with subsequent analysis. Our approach to motion correction has been described previously [25], in which frame-to-frame motion is modeled as a two-dimensional spline function. We expand this approach further by implementing a pyramid approach, where the deformation field is first estimated on a downsampled version of the image and then upsampled as initial conditions for the final calculation.…”