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Thesis Format

Integrated Article


Master of Engineering Science


Biomedical Engineering


Samani, Abbas

2nd Supervisor

So, Aaron

Joint Supervisor


Fundamental to treatment planning for patients that have suffered myocardial infarction are predictive biomarkers and risk factors. Important among these in terms of a patient’s treatment plan or prognosis are the contractility of the damaged myofibers, final infarct volume, and poor infarct healing rate. Proposed and developed in this thesis are techniques to predict these biomarkers and risk factors using cardiac biomechanical modelling. One of the developed techniques was a CT compatible shape optimization technique which can predict the contraction force of healthy, and stunned myofibers within 6.3% and the distribution of potentially necrotic myofibers within 10% accuracy. The second study involved development of infarct healing network proposed to reduce the complexity of modeling hearts with myocardial infarction while also staging the healing rate and measure collagen concentration in the infarct region reasonably accurately. Finally, an evaluation of how best to measure cardiac output by indicator dilution theory was executed.

Summary for Lay Audience

When patients suffer a heart attack, it is important to assess the damage as best as possible to prepare a treatment plan for the patient. During this damage assessment there are many measurements which describe the heart’s function in the current state, however, very measurements can predict future heart’s function after recovery.

This thesis describes development of a technique for patient specific heart models to predict how the patient’s heart will function after it has healed from a heart attack. Specifically, the techniques are designed to predict the range the heart muscle function and its efficiency after healing. This information can be used to predict how much blood the heart will be able to pump in a single heartbeat which is an important measurement used to monitor heart failure.

For the above technique to be accurate, it is important that different regions of the heart (healthy and injured) are modelled correctly. To this end, we introduced a novel technique which requires a very small number of parameters accurately model heart tissue with sustained injury from myocardial infarction. This simplified technique can determine the healing stage of scar tissue and predict the amount of building blocks (microconstituents) of heart tissue in the damaged area. Such information is very useful for diagnosis and it can aid in determining an optimal course of treatment.

The above techniques must be validated in animal studies before use in the clinic. To prepare a future study on animal models, we also improved current techniques to predict blood flow in the heart using CT which is required for damage assessment and modelling validation.

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Creative Commons Attribution-Share Alike 4.0 License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.