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Coronary heart disease is a condition where the heart muscle (myocardium) does not receive enough oxygen and nutrients due to obstruction of blood flow in the heart’s blood vessels, known as the coronary arteries. It is therefore important to investigate the blood flow in these vessels. However, it is challenging to obtain accurate measurements due to the combination of poor blood flow, the constant movement of the heart throughout the cardiac cycle, and surrounding tissue causing noise signals.

Artificial intelligence can now help clinicians by automatically measuring the heart in ultrasound images. This can save time and may in the future enable inexperienced users to perform accurate measurements of the heart.

Making good digital ultrasound probes is extremely challenging because of the constrained amount of power that can be used. If significantly more than a couple of watts are consumed, the probe gets too hot to be allowed to touch your skin.

Increased availability of ultrasound devices is great news, but it also triggers a range of new challenges. At CIUS we believe modern machine learning methods, such as deep learning, can provide necessary solutions.

Today, second harmonic imaging is a preferred ultrasound imaging modality, mainly due to its ability to improve image quality by suppressing undesired acoustic reverberations. This imaging modality improves the diagnostics for different medical applications, and most of the cardiac ultrasound scanners in the hospitals use this method. We have established an experimental measurement system to measure, manipulate, and optimize excitation waveforms, and one of the goals is to reduce the undesired transmitted signal.

My personal experience from the Nepal Earthquake in 2015, and lessons learned from Dhulikhel Hospital (DH).
Samita Giri, PhD Candidate, Faculty of Medicine and Health Sciences.

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