Breast Imaging beyond 2D: from Screening to Treatment

Abstract

Background: Breast cancer is the most common cancer in women worldwide, affecting one in eight. It can be detected by x-ray mammography, which is used in screening programs to find cancer at an early stage when treatment is often effective. A mammogram provides high resolution and shows even small and subtle tissue changes, but for some applications the two-dimensional (2D) projection does not provide sufficient information. Analysis of the three-dimensional (3D) composition of the breast can give additional knowledge from screening to treatment. Aim: The aim of this work was to overcome some of the limitations associated with mammography and interpret information "beyond 2D" from Digital Breast Tomosynthesis (DBT) and surface imaging in a clinically relevant way. Methods: Unmet user requirements in current breast imaging practice were identified and the properties of DBT and surface images were investigated with quantitative image analysis in mind. New imaging analysis methodology was developed with applications in cancer risk estimation, cancer assessment and treatment planning. Three separate studies were conducted to assess feasibility and clinical performance. Breast density, a well established risk factor for breast cancer was calculated from mammograms and DBT images of 35 women with different methods and compared. 3D segmentations from DBT were validated with histo-pathological assessment of 20 breast tumours. The feasibility of using Microsoft Kinect, an inexpensive depth sensor for gaming applications, was demonstrated using a phantom, then assessed in 10 women undergoing reconstructive surgery. Results: DBT provides spatial information about breast composition in reconstructed image volumes, but resolution is highly anisotropic and images exhibit artefacts. It is currently not possible to distinguish between different breast tissue types and imaging artefacts overlapping anatomical structures for the breast as a whole. The Microsoft Kinect can map the surface of the breast with adequate precision (error

Date of Award	31 Dec 2017
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Chris Taylor (Supervisor), Susan Astley Theodossiadis (Supervisor) & Yit Lim (Supervisor)