This project objective is to develop a new ultrasound breast imaging modality that is safer and more accurate than mammography and cheaper than magnetic resonance imaging (MRI). The intention is not only to establish it as a new standard routine screening tool to replace mammography, but also to complement MRI diagnosis for women with breast cancer.
Breast cancer is the most common type of cancer amongst women worldwide and has the second highest mortality rate. Advances in imaging techniques are key to improve its early detection and diagnosis in order to reduce its mortality rate. Due to its low cost, mammography is generally the preferred technique for standard routine screenings. This imaging technique has helped reduce the number of breast cancer deaths considerably, but it has several disadvantages: it uses harmful ionizing radiation, it has lower sensitivity when applied to younger patients, and it can lead to false-positive diagnoses in early cancer stages.
Ultrasound breast imaging provides a safe alternative but existing techniques are not able to provide images with enough resolution or penetration to be diagnostically useful. Recent advances in this field applying imaging techniques developed in the field of seismology have demonstrated its feasibility in medical imaging, providing high quality and high-resolution images of the breast employing a technique called full-waveform inversion. This new imaging modality does not suffer from the limitations of mammography, but it is still in its infancy and needs further development to reduce its cost and increase its reliability.
We propose to use methods and techniques from deep learning, and in particular from deep learning, to re-formulate this ultrasound imaging process in order to improve its quality and reduce current computational costs to make it applicable in clinical settings. We will implement
This project will have both societal and economic impact. The former is the main objective of the project: to reduce the mortality rate caused by breast cancer as well as improve the quality of life of women suffering the disease by improving early diagnosis and patient follow-up (enhance treatment effectiveness thanks to better monitoring imaging tools). From an economic perspective, the impact of the project is significant, as it could help reduce healthcare costs associated to breast cancer, which in the EU are €6 billion each year.