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Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Biomedical Engineering

Supervisor

Samani, Abbas

Abstract

Breast cancer is one of the most common cancers, representing 25% of all new cancers and 13% of all cancer related deaths in Canadian women. Early detection before treatment of breast cancer is paramount as survival rates decrease significantly over time. Some of the most common diagnostic and screening procedures include breast manual examination, X-ray mammography, ultrasonography and Magnetic Resonance Imaging (MRI). These methods are either unreliable, associated with dangerous ionization or too costly while they all have difficulty differentiating malignant tumors from benign ones without a follow-up biopsy. One technique that has shown a potential to minimize the number of biopsy cases is ultrasound elastography (USE), which images the breast tissue stiffness that is known to be substantially different for normal and pathological tissue. One of the issues plaguing USE is the lack of data quality due to input tissue displacement data quality and quantity. This data is obtained through processing radiofrequency data acquired at two compression states of the tissue that need to be acquired under the same ultrasound probe orientation to ensure high quality tissue stiffness image. Moreover, there exists no objective and automatic way to assess the quality and consistency of radio-frequency acquired throughout USE. Furthermore, methods capable of producing high quality lateral displacements are limited. As such, part of this research was dedicated to address these issues.. These issues compromise the practical utility of USE in clinical settings. This thesis introduces methodologies to tackle these issues, with the aim of optimizing USE for real time clinical settings, hence allowing reliable breast cancer assessment. It also introduces a series of metrics which can be used to objectively measure data quality. Finally, an open-source software solution was developed to guarantee data quality by generating it in-silico to facilitate the development and assessment of new displacement estimators.

Summary for Lay Audience

Breast cancer is one of the most common cancers, representing 25% of all new cancers and 13% of all cancer related deaths in Canadian women. Early detection before treatment of breast cancer is paramount as survival rates decrease significantly over time. Some of the most common diagnostic and screening procedures include breast manual examination, X-ray mammography, ultrasonography and Magnetic Resonance Imaging (MRI). These methods are either unreliable, associated with dangerous ionization or too costly while they all have difficulty detecting or differentiating malignant tumors from benign ones without a follow-up biopsy. One technique that has shown a potential to minimize the number of biopsy cases is ultrasound elastography, which images the breast tissue stiffness that is known to be substantially different for normal and pathological tissue. To perform ultrasound elastography, the clinician needs to take two ultrasound images, one before applying pressure, and one after applying pressure manually through the ultrasound probe. The differences between these two images are captured by an algorithm called a displacement estimator, which is then turned into tissue stiffness. One of the issues with this technique, however, is that it is almost impossible to guarantee that the pair of image frames will result in good quality stiffness imaging data without manually assessing the resulting images, making it too labor intensive to be used in the clinical setting. Moreover, it makes developing new algorithms difficult, as it can be nearly impossible to determine if the algorithm has failed, or the quality of the data is insufficient. Another relevant issue is most current displacement estimators fail to capture the tissue displacement completely, hence further degrading the stiffness image quality. This thesis introduces methodologies to tackle these issues, towards developing accurate and real time breast stiffness imaging technique, which allows for effective breast cancer assessment. It also introduces a series of metrics which can be used to objectively measure data quality. Finally, an open-source software solution was developed to guarantee data quality by generating it completely on the computer to facilitate the development and assessment of new displacement estimation techniques

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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