Cardiovascular MRI

Collaborators for clinical translation projects include GE, Siemens, Circle, and Vista.ai.

Ongoing Projects:

• Image quality evaluation with cardiac implantable electronic devices (Lead: Calder Sheagren)
• Cardiac ablation lesion evaluation in patients with arrhythmias (Lead: Terenz Escartin)
• Evaluation of cardiac scan efficiency using an AI-assisted protocol (Lead: Labonny Biswas)

The focus of this project is predictions of endovascular procedural success and long-term clinical outcomes in critical limb ischemia patients.

We address gaps in knowledge about how to select patients for percutaneous vascular interventions. This study characterizes the superficial femoral artery chronic total occlusion using a novel MRI protocol, identifying imaging biomarkers of hard vs soft peripheral arterial lesion components, which impact procedure approach.

This is an ongoing collaboration with Houston Methodist Hospital.

Excitement surrounds the use of stem cells, since these cells can potentially become functional cells of any given tissue. Stem cells implanted to a damaged area of the heart may be able to fully replace the damaged area in all the necessary aspects.

This project requires the ability to:

• Visualize the catheter needed to deliver the stem cells
• Image (in real-time) the progress of the catheter movement and maintain knowledge of its orientation
• Study the function in the neighbourhood of the cells
• Characterize the general functions of the tissue such as wall motion recovery, perfusion, viability, proliferation and differentiation

Ongoing projects:

• MR-based myocardial tissue characterization in a regenerative model (Lead: Moses Cook)

Arrhythmias associated with heart attacks can lead to open-heart surgery, which involves slicing up the heart muscle to eliminate the irregular heart contraction. To reduce the severity of intervention, we are studying muscle ablation via radio frequency (RF) heating and pulsed fields.

This study involves the ability to observe the delivery of the ablation to the desired place and imaging the ablation lesion.

Ongoing projects:

• Evaluating ablation therapies of cardiac arrhythmias using MRI biomarkers (Lead: Terenz Escartin)
• Catheter device tracking and visualization for MRI interventional guidance of arrhythmia ablations (Lead: Jay Patel)

The nature of the data produced with MRI is, in many ways, well-suited to subsequent image analysis. Specifically: (i) many different organs can be distinguished with the flexible soft tissue contrast; (ii) resolution can be tailored to specific applications and noise is generally well behaved; (iii) signal can be resolved in the three spatial dimensions to sub-millimeter levels; (iv) one can resolve temporal signal changes at the sub-second level; and (v) one can obtain multiple measurements of the same volume element with different contrast characteristics. The vast volume of data alone often demands computer image analysis for interpretation and display.

Areas of research in image analysis include methods for image segmentation [35,36,37] and registration [37,38]. Image segmentation facilitates (i) volume estimation for characterizing disease (ejection fraction in the heart, tumor volumes), (ii) isolation of a tissue of interest from a three-dimensional data set, notably for the display of vascular anatomy, and (iii) multiple image registration through the identification of common landmarks in the images. Registration facilitates (i) temporal signal analysis of a specified volume within the same study, (ii) comparative studies of the same patient over multiple studies perhaps with data of different contrast weightings or even data from different imaging modalities, and (iii) comparative studies of anatomy across multiple individuals.

The task of image analysis is simplified greatly when attention is paid to the nature of the basis images. As noted in the next section, image sequence parameters can be selected to maximize contrast between tissues, facilitating segmentation. Acquisition can be synchronized to motion or priority can be given to imaging speed to reduce the demands on registration algorithms. Similarly, registration of multiple images with different contrasts is aided by acquiring lines of k-space from the different images in a time-interleaved manner. For segmentation, image resolution should be selected to minimize the difficulties associated with partial voluming, where multiple tissues are present in the same voxel, based on the sizes of the structures of interest. Finally, the additive, Gaussian nature of the noise can be exploited to optimize classification strategies in segmentation; as a caveat, the noise takes on a Rician distribution in magnitude images most often used for segmentation as a result of the magnitude operation.

Signal processing tools are fundamental to MRI, from the design of acquisition strategies through to the analysis of the resulting images. Linear systems theory and digital filter design are used to spatially localize the signal of interest through selective excitation. Data acquisition occurs in the spatial frequency (k-space) domain where sampling theory determines resolution and field of view. Strategies for reducing image artifacts are often best developed in this domain. Image reconstruction involves fast multi-dimensional Fourier transforms, often preceded by data interpolation, re-sampling, and apodization. The resulting large digital data sets lend themselves to computer image analysis. Considerations for image analysis either performed by the computer or an expert observer begin with appropriate selection of acquisition parameters; there is a great deal of flexibility in trading off SNR and resolution and maximizing contrast between tissues of interest at this stage. As the quality of the data continues to improve with better hardware, imaging sequences, and analysis tools, new clinical applications are being developed for this very flexible, non-invasive modality. With these applications come greater challenges for the underlying signal processing.

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Motion-compensated T1 mapping in cardiovascular magnetic resonance imaging: a technical review

Using diffusion tensor imaging to depict myocardial changes after matured pluripotent stem cell-derived cardiomyocyte transplantation

MRI-Guided Cardiac RF Ablation for Comparing MRI Characteristics of Acute Lesions and Associated Electrophysiologic Voltage Reductions

Cine and Late Gadolinium Enhancement MRI Registration and Automated Myocardial Infarct Heterogeneity Quantification

Multi-contrast Volumetric Imaging with Isotropic Resolution for Assessing Infarct Heterogeneity: Initial Clinical Experience

Principal investigator

Graham Wright

Members

• Terenz Escartin is investigating the capacity of MRI to improve treatment outcomes of cardiac arrhythmias using radiofrequency ablation therapy and pulsed field ablation. The overall goal is to evaluate procedural success of different cardiac ablation therapies using cardiac MRI biomarkers.
• Jay Patel‘s research focuses on advancing MRI-guided interventions by developing novel device tracking and visualization algorithms. He is working on improving lesion visualization in 3D high-resolution imaging, particularly for real-time assessments. Additionally, he is enhancing catheter tracking techniques and implementing motion correction methods for motion-resolved imaging, aimed at improving the clarity and precision of high-resolution MRI datasets during cardiac interventions.
• Moses Cook completed his H.B.Sc. degree in Biological Physics at the University of Toronto. There, he pursued research in signal analysis of the cardiovascular system. Moses is now applying these principles in his current preclinical project, myocardial tissue characterization in a regenerative model using MRI.
• Bonny Biswas worked in medical software companies and at the Hospital for Sick Children prior to joining the Wright lab. She has since worked on various projects in MR imaging and analysis. She manages development of software and pipelines related to visualization, image-guided interventions, image analysis, and reproducible research.
• Calder Sheagren joined the Wright Lab in Fall 2020 after completing a B.S. in Mathematics with Honors at the University of Chicago. Calder’s research interests in the Wright group include pulse sequence programming, image reconstruction, and imaging patients with cardiac implantable electronic devices. For more information, see https://caldersheagren.com/

Alumni

Clinical collaborators

Idan Roifman, Sunnybrook Health Sciences Centre
Gideon Cohen, Sunnybrook Health Sciences Centre
Richard Farb, University Health Network
Stephen Fremes, Sunnybrook Health Sciences Centre
Naeem Merchant, University Health Network
Trisha Roy, Houston Methodist
Duncan Stewart, St. Michael’s Hospital
Bradley Strauss, Sunnybrook Health Sciences Centre
Shi-Joon Yoo, Hospital for Sick Children

Basic research collaborators

Haydar Celik, George Washington University
Margaret Cheng, Hospital for Sick Children
Hong Huang, University Health Network
Chris Macgowan, Hospital for Sick Children
Marshall Sussman, University Health Network
Ali Tavallaei, Toronto Metropolitan University
Mihaela Pop, Sunnybrook Health Sciences Centre