@conference {Wiercigroch2020a, title = {Force and torque feedback in endoscopic vessel harvesting}, booktitle = {Medical Imaging 2020: Image-Guided Procedures, Robotic Inverventions and Modeling}, volume = {11315}, year = {2020}, month = {2020}, publisher = {SPIE}, organization = {SPIE}, address = {Houston, Texas, United States}, doi = {https://doi.org/10.1117/12.2550160}, author = {Julia Wiercigroch and Keyvan Hashtrudi-Zaad and Tamas Ungi and Gianluigi Bisleri and Gabor Fichtinger} } @conference {Asselin2019a, title = {Navigated real-time molecular analysis in the operating theatre, demonstration of concept}, booktitle = {SPIE Medical Imaging 2019}, year = {2019}, abstract = {

PURPOSE: In the operating theatre surgeons are accustomed to using spatially navigated tools in conjunction with
standard clinical imaging during a procedure. This gives them a good idea where they are in the patients{\textquoteright} anatomy but
doesn{\textquoteright}t provide information about the type of tissue they are dissecting. In this paper we demonstrate an integrated
system consisting of a spatially navigated surgical electrocautery combined with real-time molecular analysis of the
dissected tissue using mass spectrometry.
METHODS: Using the 3D Slicer software package, we have integrated a commercially available neurosurgical
navigation system with an intra-operative mass spectrometer (colloquially referred to as the intelligent knife, or iKnife)
that analyzes the charged ions in the smoke created during cauterization. We demonstrate this system using a simulated
patient comprised of an MRI scan from a brain cancer patient deformably registered to a plastic skull model. On the
skull model we placed porcine and bovine tissues to simulate cancerous and healthy tissue, respectively. We built a
PCA/LDA model to distinguish between these tissue types. The tissue classifications were displayed in a spatially
localized manner in the pre-operative imaging, in both 2D and 3D views.
RESULTS: We have demonstrated the feasibility of performing spatially navigated intra-operative analysis of tissues by
mass spectrometry. We show that machine learning can classify our sample tissues, with an average computed
confidence of 99.37 \% for porcine tissue and 99.36\% for bovine tissue.
CONCLUSION: In this paper we demonstrate a proof of concept system for navigated intra-operative molecular
analysis. This system may enable intra-operative awareness of spatially localized tissue classification during dissection,
information that is especially useful in tumor surgeries where margins may not be visible to the unassisted eye.
Keywords: image guided therapy, intra-operative mass spectrometry, iKnife, 3D Slicer, open-source, rapid evaporative
ionization mass spectrometry (REIMS)

}, author = {Mark Asselin and Martin Kaufmann and Julia Wiercigroch and Tamas Ungi and Andras Lasso and John Rudan and Gabor Fichtinger} }