The Biomedical Signal and Image Computing Lab (BiSICL) is a multidisciplinary research facility at UBC dedicated to computational research in biomedical imaging. Our lab's main objectives are to create, develop and translate innovative techniques for automated processing, analysis, understanding, and visualization of structural and functional medical imaging data so that they can be applied in a clinically-focused, disease-specific manner. The uniqueness of the research conducted at BiSICL stems from the close and direct collaboration with local and international clinicians of the highest caliber and on-site presence in leading hospitals and clinical sites spanning literally from the lab to the bedside.
Applications of our work range from basic imaging biomarker based study of disease and recovery mechanisms to emerging image-guided surgical interventions where innovations in computational methods are essential for enabling, advancing, accelerating and enhancing the quality and efficacy of health research and clinical practice. Currently our projects focus on neuroimaging (functional and diffusion MRI), robotic surgery (abdominal cancer) and novel uses of 3D ultrasound (orthopedics). Our research promises substantial benefits to large sections of the world's population especially with our aging demographics where related diseases are common, debilitating and pose a tremendous burden and cost on healthcare systems.
List of our publications are available here Updated on May 2018
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Although it is now well established that brain fiber pathways serve as the physical substrate for functional interactions, this information is rarely exploited in current fMRI studies. In this project, we investigate the implications of fusing information regarding brain structure and brain activity in the presence and absence of explicit input. This multimodal approach enables us to analyze how the brain responds to stimuli around its baseline and to regularize the analyses of functional brain dynamics using structural connectivity information. The methods we develop ultimately serve to enhance our general understanding of the human brain organization by shedding light on the structure-function relationship in the brain.
fMRI has become the dominant imaging modality for studying human brain function non-invasively. Most fMRI studies have focused on inferring changes in brain activity from fMRI signal intensity modulations. Here at BiSICL, we are extending traditional fMRI analysis methods by designing new robust spatial descriptors to characterize brain activation pattern in areas implicated in neurodegenerative diseases such as PD. In particular, we are exploring the use of invariant spatial features to examine the spatiotemporal properties of activation within various brain regions of interest (ROIs).
In addition to inferring brain activation, fMRI is often used to study the functional integration of different brain regions. The apparent inter-subject variability, however, renders detection of representative group networks very challenging. Therefore, we are currently designing novel approaches based on sparse multivariate models that integrate group information in detecting common brain networks across subjects. We are also investigating the implications of inferring functional connectivity based on spatial modulations of blood oxygen level dependent (BOLD) signals in contrast to traditional mean region of interest (ROI) intensity time courses.
Our objective is to overlay data derived from pre-operative CT images onto the surgeon's stereo endoscopic view. Moreover, the pre-operative data will be first positioned and then deformed (e.g. stretched, bent, or cut) continuously to match the current state of the patientâ€™s anatomy. Lastly, left and right (stereo) projective views of the overlaid data will be streamed onto the left and right camera views provided to the surgeon console, thus augmenting the 3D endoscopic view of the operation field. This MIS enhancement will radically improve the surgeonâ€™s experience and efficiency, increase the precision of the surgery, decrease the surgery time, and reduce collateral damage, which in turn will lead to improved surgery outcomes and patient recovery.
Orthopedic imaging has traditionally relied on ionizing radiation based modalities such as x-ray, fluoroscopy and CT. We are investigating the employment of 3D US as an alternative safer imaging modality for a prospective minimally invasive computer assisted surgery system designed specifically for pre-operative bone fracture assessment and intra-operative guidance in fracture reduction procedures. We have demonstrated fracture detection and have developed US-CT fusion methods to improve intra-operative visualization of bone surfaces.
Orthopedic imaging has traditionally relied on ionizing radiation based modalities such as x-ray, fluoroscopy and CT. At BiSICL, we are investigating the use of 3D US as an alternative safer imaging modality for a prospective minimally invasive computer assisted surgery system designed, specifically for pre-operative bone fracture assessment and inter-operative guidance in fracture reduction procedures.
Most anatomical structures and regions of interst captured with various 3D medical imaging technologies are of complex shape and topology. Due to this as well as the commonly encourntered poor image quality and pathology, accurate yet robust segmentation of such structures is generally extremely difficult to achieve using fully automatic methodologies. On the other end of the spectrum, manual segmentation alternatives are very tedious and impractically time-consuming. At BiSICL, we are developing highly-automated 3D segmentation tools that incorporate human knowledge through minimal user interaction that guides 3D segmentation tasks in a very intuitive and efficient manner improving accuracy and robustness.
In this project, we are investigating extracting the 3D biomechanical patient-specific model of the tongue tissue mostly from magnetic resonance images with specific focus on simulating the swallowing process. The successful solution will enclose an efficient and reasonably fast combination of different image processing techniques with minimum user interaction, aiming to be included in a computerized visual platform like Artisynth, in order to help in visualizing the patient-specific anatomy and dynamics of the tongue as well as predicting the possible effects of any treatment or surgery in the overall functionality of the oropharyngeal structures in case of any swallowing disorders.