Intraoperative cerebral blood flow monitoring in neurosurgery: A review of contemporary technologies and emerging perspectives

A robust motion correction technique for infrared thermography during awake craniotomy

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.

Analysis of laser Doppler flowmetry long-term recordings for investigation of cerebral microcirculation during neurointensive care

Cerebral blood flow is monitored in the neurointensive care unit (NICU) to avoid further brain damage caused by secondary insults following subarachnoid hemorrhage and brain trauma. Current techniques are mainly snap-shot based and focus on larger vessels. However, continuous monitoring of the smaller vessels may help detect the onset of secondary insults at an earlier stage. In this study, long-term measurements of brain microcirculation with laser Doppler flowmetry (LDF) were performed and evaluated. The aim was to identify and describe physiological signal variations and separate these from movement artifacts. Fiberoptic probes for subcortical LDF recordings of perfusion and total light intensity (TLI) were implanted in three patients with subarachnoid hemorrhage. Data were successfully collected and visualized in real-time over 4 days, resulting in 34, 12, and 8.5 h per patient. Visual observation, wavelet transforms, moving medians, and peak envelopes were used to identify and describe movement artifacts and physiological changes. Artifacts occurred in <5% of the total recording time and could be identified through signal processing. Identified physiological signal patterns included a slowly increasing perfusion trend over hours, vasomotion mainly at 2 cycles/min both in the perfusion and the TLI, and rapid, synchronized changes in the TLI and the perfusion on 38 occasions. Continuous LDF recordings indicating changes in the microvascular blood flow can increase the understanding of the microcirculation in the injured brain. In the long run, this may become a complement for the detection of secondary insults at an earlier stage than possible with today's techniques.

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.