Augmented Image-Guidance for Transcatheter Aortic Valve Implantation

The introduction of transcatheter aortic valve implantation (TAVI), an innovative stent-based technique for delivery of a bioprosthetic valve, has resulted in a paradigm shift in treatment options for elderly patients with aortic stenosis. While there have been major advancements in valve design and access routes, TAVI still relies largely on single-plane fluoroscopy for intraoperative navigation and guidance, which provides only gross imaging of anatomical structures. Inadequate imaging leading to suboptimal valve positioning contributes to many of the early complications experienced by TAVI patients, including valve embolism, coronary ostia obstruction, paravalvular leak, heart block, and secondary nephrotoxicity from contrast use. A potential method of providing improved image-guidance for TAVI is to combine the information derived from intra-operative fluoroscopy and TEE with pre-operative CT data. This would allow the 3D anatomy of the aortic root to be visualized along with real-time information about valve and prosthesis motion. The combined information can be visualized as a `merged\u27 image where the different imaging modalities are overlaid upon each other, or as an `augmented\u27 image, where the location of key target features identified on one image are displayed on a different imaging modality. This research develops image registration techniques to bring fluoroscopy, TEE, and CT models into a common coordinate frame with an image processing workflow that is compatible with the TAVI procedure. The techniques are designed to be fast enough to allow for real-time image fusion and visualization during the procedure, with an intra-procedural set-up requiring only a few minutes. TEE to fluoroscopy registration was achieved using a single-perspective TEE probe pose estimation technique. The alignment of CT and TEE images was achieved using custom-designed algorithms to extract aortic root contours from XPlane TEE images, and matching the shape of these contours to a CT-derived surface model. Registration accuracy was assessed on porcine and human images by identifying targets (such as guidewires or coronary ostia) on the different imaging modalities and measuring the correspondence of these targets after registration. The merged images demonstrated good visual alignment of aortic root structures, and quantitative assessment measured an accuracy of less than 1.5mm error for TEE-fluoroscopy registration and less than 6mm error for CT-TEE registration. These results suggest that the image processing techniques presented have potential for development into a clinical tool to guide TAVI. Such a tool could potentially reduce TAVI complications, reducing morbidity and mortality and allowing for a safer procedure

The Imaginary Illness and the Rhetorical Cure

Ye

1. Introduction Optical Tactile Sensors for Medical Palpation

Surgeons use palpation for a variety of medical procedures- to find tumors and arteries, as well as assess the health of soft tissues. Minimally invasive surgical procedures preven

Scientific overview: CSCI-CITAC annual general meeting and young investigator’s forum 2010

In 2010, the annual general meeting of the Clinical Investigator Trainee Association of Canada – Association des cliniciens-chercheurs en formation du Canada (CITAC-ACCFC) and the Canadian Society for Clinician Investigators (CSCI) was held between September 20 and 22 in Ottawa. Several globally-renowned scientists, including this year’s CSCI/Royal College Henry Friesen Award recipient, Dr. Paul Kubes, Distinguished Scientist Award recipient, Dr. Gideon Koren and Joe Doupe Young Investigator Award recipient, Dr. Torsten Neil, discussed a variety of topics relating to the role of technology in medicine. The meeting was well attended by clinician scientists and trainees from across Canada and offered trainees mentorship and networking opportunities in addition to showcasing their research at the young investigator forum. The aim of this scientific overview is to highlight the research presented by trainees at both the oral plenary session as well as the poster presentation sessions of this meeting. Similar to last year’s meeting [1], research questions being investigated by trainees covered the spectrum of medical disciplines, encompassing both basic science as well as clinical areas, and are summarized below. [1] Ong Tone, S., Dugani, S., Marshall, H., Shamji, M.F., Murray, J-C., and Bossé, D. 2010 Scientific overview of the CSCI-CITAC 2009 conference. Clin Invest Med 33: E69-72, E73-6

Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging

© The Author(s) 2020. Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation

Tumor molecular differences associated with outcome disparities of Black patients with head and neck cancer

BackgroundNumerous studies of head and neck squamous cell carcinoma (HNSCC) have demonstrated disparate outcomes by race and ethnicity. Beyond known associations with socioeconomic variables, whether these are also associated with differences in tumor molecular composition has thus far been poorly explored.MethodsWe downloaded clinical and multiplatform molecular data from The Cancer Genome Atlas and other published studies. These were compared between non-Hispanic Black (n = 43) and White (n = 354) patients with non-HPV-related tumors, using multivariable models. Publicly available validation cohorts were used.ResultsBlack patients had poorer progression-free survival than White patients. Tumors of Black patients had greater copy number aberrations, and increased SFRP1 methylation and miRNA-mediated PRG4 silencing associated with poor survival. PI3K/AkT/mTOR pathway proteins were differentially expressed.ConclusionsThere are molecular differences between tumors of Black and White patients that may partially account for differences in survival. These may inform targeted treatment decisions to achieve equitable outcomes