Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article


Doctor of Philosophy


Medical Biophysics

Collaborative Specialization

Molecular Imaging


Fenster, Aaron


Robarts Research Institute


The successful intervention of breast cancer relies on effective early detection and definitive diagnosis. While conventional screening mammography has substantially reduced breast cancer-related mortalities, substantial challenges persist in women with dense breasts. Additionally, complex interrelated risk factors and healthcare disparities contribute to breast cancer-related inequities, which restrict accessibility, impose cost constraints, and reduce inclusivity to high-quality healthcare. These limitations predominantly stem from the inadequate sensitivity and clinical utility of currently available approaches in increased-risk populations, including those with dense breasts, underserved and vulnerable populations.

This PhD dissertation aims to describe the development and validation of alternative, cost-effective, robust, and high-resolution systems for point-of-care (POC) breast cancer screening and image-guided needle interventions. Specifically, 2D and 3D ultrasound (US) and positron emission mammography (PEM) were employed to improve detection, independent of breast density, in conjunction with mechatronic and automated approaches for accurate image acquisition and precise interventional workflow. First, a mechatronic guidance system for US-guided biopsy under high-resolution PEM localization was developed to improve spatial sampling of early-stage breast cancers. Validation and phantom studies showed accurate needle positioning and 3D spatial sampling under simulated PEM localization. Subsequently, a whole-breast spatially-tracked 3DUS system for point-of-care screening was developed, optimized, and validated within a clinically-relevant workspace and healthy volunteer studies. To improve robust image acquisition and adaptability to diverse patient populations, an alternative, cost-effective, portable, and patient-dedicated 3D automated breast (AB) US system for point-of-care screening was developed. Validation showed accurate geometric reconstruction, feasible clinical workflow, and proof-of-concept utility across healthy volunteers and acquisition conditions. Lastly, an orthogonal acquisition and 3D complementary breast (CB) US generation approach were described and experimentally validated to improve spatial resolution uniformity by recovering poor out-of-plane resolution. These systems developed and described throughout this dissertation show promise as alternative, cost-effective, robust, and high-resolution approaches for improving early detection and definitive diagnosis. Consequently, these contributions may advance breast cancer-related equities and improve outcomes in increased-risk populations and limited-resource settings.

Summary for Lay Audience

Breast cancer is the most commonly diagnosed cancer in women and a leading cause of cancer-related deaths. The successful management of breast cancer relies on early detection with mammography and diagnosis with needle biopsies. However, mammography faces challenges in women with dense breasts, as dense breast tissues hide abnormalities and make the interpretation of images more difficult. Additionally, healthcare disparities and other interrelated factors can contribute to an increased risk for breast cancer in several populations. These challenges stem from the limitations of existing methods in clinical practice.

This dissertation aims to develop and validate alternative, accessible, and cost-effective systems to improve breast cancer detection and needle-based diagnosis. Two medical imaging approaches were employed for their ability to detect breast cancer in dense breasts: ultrasound (US) as a widely available, cost-effective, radiation-free, and real-time method, and positron emission mammography (PEM) as a specialized technique that uses radioactive substances to visualize cancer activity. These imaging approaches were integrated with specialized hardware components and software modules to improve accuracy for the detection and diagnosis of breast cancer. Three systems and a software algorithm to improve image quality was developed. First, a combined PEM and US-guided needle biopsy system was developed to sample breast cancer with high accuracy and precision. Next, a whole-breast three-dimensional (3D) US system and a cost-effective, portable, and personalizable 3D automated breast (AB) US system were developed for bedside image acquisition and breast cancer screening. Lastly, a software algorithm was created to improve image quality by generating an image called a 3D complementary breast (CB) US. This work involved testing the systems and methods in controlled settings and evaluating their performance with various breast models and healthy human volunteers to demonstrate their potential clinical use. Ultimately, this research shows promise in improving early detection and diagnosis of breast cancer, potentially leading to better outcomes in women with dense breasts and those in limited-resource healthcare settings.

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.