David Black

Ryan Yeung, David Black, Patrick B. Chen et al.

To address the lack of access to ultrasound in remote communities, previous work introduced human teleoperation, a mixed reality and haptics-based tele-ultrasound system. In this approach, a novice takes the role of a cognitive robot controlled remotely by an expert through mixed reality. In this manuscript we summarize new developments to this system and describe a feasibility study assessing its use for long-distance remote abdominal ultrasound examinations. To provide simple but effective haptic feedback, we used an ellipsoid model of the patient with its parameters calibrated using our system's position and force sensors. We tested the system in Skidegate, Haida Gwaii, Canada, with the experts positioned 754 km away in Vancouver, Canada. We performed 11 total scans with 10 novices and 2 sonographers. The sonographers were tasked with acquiring 5 target images in the epigastric region. The image acquisition quality was assessed by 2 radiologists. We collected alignment data and the novices completed task load and usability questionnaires. Both the novices and sonographers provided written and verbal feedback to inform future design iterations. 92% of the acquired images had sufficient quality for interpretation by both radiologists. The mean task load reported by the novices was below reference values reported in literature and the usability was unanimously positive. No correlation was found between image quality and the follower's alignment error with the virtual transducer. Overall, we show that human teleoperation enables sonographers to perform remote abdominal ultrasound imaging with high performance, even across large distances and with novice followers. Future work will compare human teleoperation to conventional, robotic and tele-mentored ultrasound.

David Black, Declan Byrne, Anna Walke et al.

Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors. In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8). Four machine learning models were trained to classify tumor type, grade, glioma margins and IDH mutation. Using random forests and multi-layer perceptrons, the classifiers achieved average test accuracies of 84-87%, 96%, 86%, and 93% respectively. All five fluorophore abundances varied between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the fluorophores' differing abundances in different tissue classes, as well as the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

David Black, Jaidev Gill, Andrew Xie et al.

Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. We therefore propose two deep learning models for correction and unmixing, which can account for the nonlinear effects and produce more accurate estimates of abundances. Both models use an autoencoder-like architecture to process the captured spectra. One is trained with protoporphyrin IX (PpIX) concentration labels. The other undergoes semi-supervised training, first learning hyperspectral unmixing self-supervised and then learning to correct fluorescence emission spectra for heterogeneous optical and geometric properties using a reference white-light reflectance spectrum in a few-shot manner. The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0.997 and 0.990, respectively, whereas the classical approach achieved only 0.93 and 0.82. The semi-supervised approach's R values were 0.98 and 0.91, respectively. On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better removing bright spots of specular reflectance and reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery.