Colloquia and Seminars

Presenter: Dr.Dietmar Cordes

Abstract: A wide range of studies in functional MRI (fMRI) show increased capacity of multivariate methods to improve sensitivity of detecting brain activations in a noisy environment. A new method uses local canonical correlation analysis (CCA), to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign this value to the center voxel of the local neighborhood; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation, leading to low specificity.

Presenter: Dr. Eugenia Kumacheva, Professor
Departments of Chemistry and Chemical Engineering and The Institute of Biomaterials and Biomedical Engineering
University of Toronto

Abstract:

Organized arrays of inorganic nanoparticles show electronic, optical, and magnetic properties that originate from the coupling of size-­ and shape-­dependent properties of individual nanoparticles (NPs). Self-­organization of NPs is an efficient strategy for producing nanostructures with complex, hierarchical architectures. However, currently the quantitative prediction of the architecture of nanoparticle ensembles and of the kinetics of their formation remains a challenge.

We propose two new paradigms for the self-assembly of asymmetric metal NPs. One of the approaches utilizes a striking analogy between amphilphilic ABA triblock copolymers and hydrophilic metal nanorods tethered with hydrophobic polymer chains at both ends in order to assemble the nanorods in structures with varying morphologies and varying optical properties. The self-assembly process is rationalized and mapped by using phase-like diagrams.

In the second approach we exploit a marked similarity between the self-assembly and step-growth polymerization processes, in order to describe the kinetics and statistics of metal nanoparticle self-assembly. In this approach, the nanoparticles act as multifunctional monomer units (nanomers), which form reversible, noncovalent bonds at specific bond angles and organize themselves into a colloidal polymer. We show that the kinetics and statistics of step-growth polymerization enable a quantitative prediction of the architecture of linear, branched, and cyclic self-assembled nanostructures; their aggregation numbers and size distribution; and the formation of structural isomers.

The proposed strategy provides a new route to the quantitatively predicted organization of nanoparticles in supracoloidal assemblies with new properties.

Speaker:Elyas Shaswary, M.Sc. candidate
Department of Physics, Ryerson University
Advisors: Dr. Jahan Tavakkoli and Dr. Yuan Xu

Abstract:

Ultrasound elastography is a branch of tissue characterization which reveals information about the stiffness of tissues. It has been long known that many diseases cause changes in stiffness of tissues. For instance, tumors are normally stiffer than the normal surrounding tissues. In general, ultrasound B-mode imaging does not visualize the stiffness of tissues, which makes the detection of tumors challenging. Traditionally, palpation has been used by the physicians for detection of stiff lumps near the surface of the skin. Many techniques of ultrasound elastography have been proposed in literature and they all involve some type of tissue excitation which could be in the form of a compressional force or a vibrational source. The response of tissue to external excitations is monitored by ultrasound imaging system. Ultrasound elastographic techniques mainly rely on radiofrequency (RF) data to estimate tissue displacement. The quality of the images in elastography depends on accurate estimation of the tissue displacement. Numerous algorithms have been proposed for estimation of tissue displacement and they are generally referred to as time-delay estimator (TDE). However, each of algorithms offers tradeoff between accuracy and spatial resolution. In this study a new algorithm will be used to improve the quality of the images in ultrasound elastography. The new algorithm is modified from normalized cross-correlation methods. Previous numerical and experimental investigations have shown that the new algorithm can reduce the jitter noise in TDE while having a high-spatial resolution. This study will focus on applying the new algorithm to improve the signal-to-noise ratio in estimating displacement.

Speaker: Raphael Jakubovic
Department of Physics, Ryerson University
Department of Medical Imaging, Sunnybrook Health Sciences Centre
Supervisors: Dr. Ana Pejović-Milić, Dr. Richard Aviv

Abstract:

Primary Intracerebral hemorrhage (ICH) occurs in 10-15% of all strokes, accounts for a high rate of mortality, and is usually caused by hypertensive vasculopathy or cerebral amyloid angiopathy (CAA). In CAA, cerebral microbleeds (CMB) are considered imaging surrogates of prior chronic, subclinical, hemorrhagic episodes from bleeding- prone vessels affected by moderate to severe lipohyalinosis and/or amyloid deposits. Very limited comparative MRI/ radio-isotope studies are available in the context of CAA. There is compelling evidence that CAA deposition may precede the radiographic evidence of vascular disease. However, factors determining whether a CAA patient undergoes multiple subclinical CMBs or presents with symptomatic ICH (or macrobleed) are not yet fully elucidated. ApoE genotype has also been implicated as critical factor in amyloid deposition, CMB, and ICH. Whereas ApoE e4 is associated with increased Aß cerebral vasculature deposition, ApoE e2 predisposes to increased hemorrhage. The CTA Spot Sign (CSS), defined by contrast extravasation on CT angiography, predicts hematoma expansion and has been shown to be an independent predictor of hematoma growth, mortality and clinical outcome. It is plausible to suggest that the same mural characteristics causing CMBs may impact upon vessel contractility and therefore bleeding rate, hematoma growth and CSS frequency.