Rising Star in Bio-Imaging in Quebec lecture
By presenting the annual Rising Star in Bio-Imaging in Quebec Award, QBIN highlights the work of an emerging researcher in the field of bio-imaging.
Exceptionally this year, we will be awarding two Rising Star in Bio-Imaging in Quebec awards. Each will give a 30-minute lecture, followed by a 15-minute question period.
Professor Sylvia Villeneuve, McGill University
Multimodal imaging in the early phase of Alzheimer’s disease
Alzheimer’s disease (AD) is the most common cause of dementia, affecting almost half of Canadians aged 85 years or older.Amyloid-b (Aβ) plaques and neurofibrillary tau tangles, the pathological hallmarks of AD appear years before cognitive impairments can be detected in individuals with AD. Unless the disease can be prevented, the prevalence of dementia is expected to increase dramatically with the aging of the population. In this presentation we will discuss brain changes that characterize the preclinical phase of the disease. We will discuss potential modifiable risk factors that could slow down disease progression. Finally, we will revise new imaging criteria to identify individuals in the preclinical phase of AD.
Sylvia Villeneuve
Dr. Sylvia Villeneuve is an Associate Professor in the Department of Psychiatry at McGill University. She is the co-director of the PREVENT-AD cohort and holds the Canada Research Chair in Early Detection of Alzheimer’s Disease. Dr. Villeneuve received her PhD from the Université de Montréal in 2011, where she assessed the nature of memory disorders in people with mild vascular and non-vascular cognitive disorders. She received her first postdoctoral training at the University of California Berkeley where she examined the interactions between amyloid deposits, vascular diseases and cognition in the preclinical phase of Alzheimer’s disease. She did a second postdoc at Northwestern University where she evaluated the predictive value of neurovascular changes to detect early changes associated with amyloid deposits. Dr. Villeneuve has been a Professor at McGill University since 2015 and a member of the Ordre des Psychologues du Québec since 2009.
Professor Hassan Rivaz, Concordia University
Transforming ultrasound with AI
This talk focuses on developing AI-driven image analysis techniques that reveal otherwise hidden information in clinical ultrasound signals. Ultrasound is one of the most used imaging modalities because of its low cost and ease of use. However, it has two main drawbacks. First, raw ultrasound data is not suitable for visualization, and as such, is converted to the familiar grey-scale images which leads to a loss of most of its information. Second, these grey-scale images are hard to interpret since they are noisy and collected at oblique angles. In this talk, we tackle these issues by developing techniques that extract clinically important information such as tissue elasticity and attenuation from the complex raw ultrasound signals, and register them to other modalities such as Magnetic Resonance Imaging (MRI) to help with their interpretation.
Hassan Rivaz
Dr. Hassan Rivaz earned his PhD (2011) from Johns Hopkins University and completed an NSERC post-doctoral fellowship at the Montreal Neurological Institute (MNI) before joining Concordia University’s Department of Electrical and Computer Engineering as well as the PERFORM Centre as an Assistant Professor, in 2014. He holds the Concordia University Research Chair in Medical Image Analysis and is the Director of the IMPACT Lab: Image Processing And Characterization of Tissue. He is an Associate Editor of IEEE TMI and IEEE TUFFC.
Dr. Rivaz is the recipient of more than 20 research awards and honours, including: (1) three papers short-listed for the Robert F. Wagner All Conference Best Student Paper Award, all with his graduate students as first authors in 2016 and 2021; (2) a First Rank paper (tied) in the international challenge on ultrasound beamforming with deep learning (Goudarzi, Asif, Rivaz, IEEE IUS 2020); (3) an Editors’ Selected paper in IEEE TUFFC (Hashemi & Rivaz, 2017); and (4) (Zhou & Rivaz 2016) was among only 10 papers out of 2948 submissions to the IEEE EMBC proceedings to be invited for submission of a full journal paper.
William Feindel Lecture
The William Feindel Lecture honours a scientist who has made significant contributions to the field of imaging.
Professor Louis Collins, McGill University
A recent history of image-guided surgery in the NIST lab in the Montreal Neurological Institute
This talk will track the history of image guided surgery over the past two decades in the NeuroImaging and Surgical Technology (NIST) lab at the Montreal Neurological Institute as an excuse to talk about the joy of working with curious, bright and hardworking students. The NIST lab was the first to use frameless stereotaxic techniques with tracked intra-operative ultrasound to improve guidance during surgery while accounting for brain shift. The procedure matches intra-operative ultrasound to the pre-operative MRI data using image intensities or blood vessels as features to drive the registration. The result of this mapping is a deformation field that can be used to warp the pre-operative data to fit the anatomy of the patient during surgery, thus correcting for brain shift. Over the past 15 years, though the work of many students and fellows, this matching procedure has been improved by using additional anatomical landmarks, using simulated ultrasound, using statistically-driven intensity-based similarity functions and implemented with a fast GPU implementations. The NIST lab developed brain atlases that can be customized to a patients anatomy to help in the identification of deep targets for planning movement disorder surgery. In addition to the brain, the NIST lab has developed tools to register preoperative spine CT to the patient, again with intra-operative ultrasound, to help in pedicle screw insertion when instrumenting one or more vertebrae. All these tools have been made publicly available as open source neuronavigation software known as IBIS (at http://ibisneuronav.org/) and templates and atlases (at http://nist.mni.mcgill.ca/atlases/).
Louis Collins
Dr. Collins is a professor in the departments of Biomedical Engineering and Neurology & Neurosurgery, associate member of the McGill Centre for Studies in Aging, and the Center for Intelligent Machines at McGill and world-renowned expert in image processing for quantitative analysis of medical images. He heads the Neuro Imaging and Surgical Technologies (NIST) laboratory at the Brain Imaging Center of the Montreal Neurological Institute where his team of ~20 students, fellows and engineers develop and use computerized image processing techniques such as non-linear image registration and model-based segmentation to automatically identify structures within the human brain.
In neurology, these tools are used to characterize differences in brain morphology of patients with multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, amyotrophic lateral sclerosis and other neurodegenerative diseases in comparison to the healthy brain as it changes from birth, through adolescence and adulthood and eventually death. In neurosurgery, these tools are used combine data from different medical imaging modalities to generate 3D models of the skin, skull, dura, cortical surface, blood vessels, functional regions and brain lesions to help neurosurgeons plan surgery. The pre-operative images and 3D models are then integrated in a neuronavigation platform that uses intra-operative ultrasound to correct for tissue deformations during neurosurgery.
In the past 5y, his group has published more than 140 journal articles (from 380 career total) and 34 conference papers and abstracts (388 total) yielding a Google h-index=111 (i10-index=396). His work is detailed at http://nist.mni.mcgill.ca/ .
