Generation of echocardiogram images for 3D image enhancement and localisation

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Echocardiography (Echo) is key to the assessment and management of all cardiac diseases. Echocardiograms are produced using ultrasound waves to create a moving picture of the heart. At the power levels used in the clinic, the use of sound waves is painless and harmless, and the devices required to generate them are low cost and portable which provides convenience [Potter]. These advantages of echocardiography contribute to its widespread clinical use today. 3D Echocardiography (3D Echo) allows the quantification of absolute cardiac chamber volumes and visualisation of the 3D structure and dynamic motion images of the heart, especially heart valve structures [Shiota]. It offers significant additional clinical information to traditional 2D echo, and has been identified as the best echocardiographic method for sequential quantification of left ventricle volumes and ejection fractions in patients with cancer undergoing chemotherapy. Moreover, unlike the 2D version, 3D echo is not reliant on plane positioning, does not require geometric modelling and does not make assumptions about the shapes of the chambers of the heart [Cheng], furthering its reproducibility and accuracy. However, the main limitation of 3D echo has been and still is the inferior image quality compared with today's 2D imaging technology [Lang]. Currently, the spatial resolution is limited by the number of beams and sweeps that the probe can send and receive. This limits lateral resolution specifically and as a result, also weakens image contrast. Higher resolution 3D images would enable more accuracy when calculating salient chamber volumes and improve the visualisation capabilities of this method. Attempts to improve image quality have mostly focused on changes to the transducer itself [Casas]. An important issue with the echo modality is that it is difficult to use without significant training and experience, especially in transoesophageal echocardiography (TEE) imaging. It can be difficult to know the probe's position and understand what exactly is being imaged. Automatic localisation of the probe would ease the process of performing TEE by helping the user identify the probe pose within the body. Furthermore, automatic localisation creates scope for more automation in performing the echo itself. For example, robotic actuators could be called on at certain stages of the echo to perform tasks that they would be better suited to compared to a medical professional. In order to tackle these issues, we plan to develop a pipeline that determines the probe's position and orientation, within the body, from 2D echo image inputs. This will be achieved by firstly developing an algorithm that, when given a 2D echo image, identifies its most likely location in a 3D anatomical model of the heart. Once developed, we will use this algorithm to predict the probe's position and orientation within the body, and hence develop methods to support 3D image guidance of TEE imaging, using 2D slices. Finally, in the final part of the DPhil we will use this pipeline to support the leveraging of the higher quality 2D echo images to enhance the 3D echo images, overcoming one of the main obstacles to 3D TEE. The methodologies used throughout the project will be mostly based on state-of-the-art machine learning tools, in particular Convolutional Neural Networks. This project is undertaken in partnership with GE Healthcare and falls within the EPSRC Medical Imaging research area. References Potter, A., Pearce, K., & Hilmy, N. (2019). The benefits of echocardiography in primary care. British Journal of General Practice, 69(684), 358-359. https://doi.org/10.3399/BJGP19X704513 Shiota, T. (2008). 3D echocardiography: The present and the future. Journal of Cardiology, 52(3), 169-185. https://doi.org/10.1016/J.JJCC.2008.09.004