Ultrasound-guided Optical Techniques for Cancer Diagnosis: System and Algorithm Development

Worldwide, breast cancer is the most common cancer among women. In the United States alone, the American cancer society has estimated there will be 271,270 new breast cancer cases in 2019, and 42,260 lives will be lost to the disease. Ultrasound (US), mammography, and magnetic resonance imaging (MRI) are regularly used for breast cancer diagnosis and therapy monitoring. However, they sometimes fail to diagnose breast cancer effectively. These shortcomings have motivated researchers to explore new modalities. One of these modalities, diffuse optical tomography (DOT), utilizes near-infrared (NIR) light to reveal the optical properties of tissue. NIR-based DOT images the contrast between a suspected lesion’s location and the background tissue, caused by the higher NIR absorption of the hemoglobin which characterizes tumors. The limitation of high light scattering inside tissue is minimized by using ultrasound image to find the tumor location. This thesis focuses on developing a compact, low-cost ultrasound guided diffuse optical tomography imaging system and on improving optical image reconstruction by extracting the tumor’s location and size from co-registered ultrasound images. Several electronic components have been redesigned and optimized to save space and cost and to improve the user experience. In terms of software and algorithm development, manual extraction of tumor information from ultrasound images has been replaced by using a semi-automated ultrasound image segmentation algorithm that reduces the optical image reconstruction time and operator dependency. This system and algorithm have been validated with phantom and clinical data and have demonstrated their efficacy. An ongoing clinical trial will continue to gather more patient data to improve the robustness of the imaging algorithm. Another part of this research focuses on ovarian cancer diagnosis. Ovarian cancer is the most deadly of all gynecological cancers, with a less than 50% five-year survival rate. This cancer can evolve without any noticeable symptom, which makes it difficult to diagnose in an early stage. Although ultrasound-guided photoacoustic tomography (PAT) has demonstrated potential for early detection of ovarian cancer, clinical studies have been very limited due to the lack of robust PAT systems. In this research, we have customized a commercial ultrasound system to obtain real-time co-registered PAT and US images. This system was validated with several phantom studies before use in a clinical trial. PAT and US raw data from 30 ovarian cancer patients was used to extract spectral and statistical features for training and testing classifiers for automatic diagnosis. For some challenging cases, the region of interest selection was improved by reconstructing co-registered Doppler images. This study will be continued in order to obtain quantitative tissue properties using US-guided PAT

Assesment and optimisation techniques for an electrical impedance mammography system

Breast cancer detection through the use of Electrical Impedance Tomography (EIT) has been proposed for a number of years. Typically in an EIT or when in use in breast cancer detection, Electrical Impedance Mammography (EIM) system, the patient interface is that of electrodes. Positive and negative sinusoidal signals are injected into a patient multiple times at multiple frequencies, recording the developed surface voltages that naturally develop from the impedance of the body. A 2D or 3D reconstruction and visualisation of the impedance distribution is possible through the use of the recorded voltage amplitude and phase. Higher resolution images are achieved through higher electrode density (i.e.smaller electrode distance). Furthermore, tissue has a characteristic frequency response,which can be recorded if different injection frequencies are utilised. The higher thefrequency, the deeper the signal can penetrate into tissue and deeper through the cellular structures, potentially leading to tissue characterisation through its frequency response. Our group has suggested a unique combined EIT and ultrasound multimodal imaging system to detect breast cancer. This will use an EIT system to initially scan the breast,combining this parametric data with the high-resolution images of ultrasound to give a more accurate diagnosis. The prototype, known as V2, was functional but performed poorly, especially with respect to the signal to noise ratio. Images generated with this system were unclear. My role within the group was to analyze the performance of the V2 system and research best practice methods to improve the existing design, taking into consideration the intricacies of EIT hardware design and the targeted application. The improvements I suggested were incorporated into the V3 system, which was then compared with the V2 system, and showed significant performance improvement

Fluorescence Guided Tumor Imaging: Foundations for Translational Applications

Optical imaging for medical applications is a growing field, and it has the potential to improve medical outcomes through its increased sensitivity and specificity, lower cost, and small instrumentation footprint as compared to other imaging modalities. The method holds great promise, ranging from direct clinical use as a diagnostic or therapeutic tool, to pre-clinical applications for increased understanding of pathology. Additionally, optical imaging uses non-ionizing radiation which is safe for patients, so it can be used for repeated imaging procedures to monitor therapy, guide treatment, and provide real-time feedback. The versatile features of fluorescence-based optical imaging make it suited for cancer related imaging applications to increase patient survival and improve clinical outcomes. This dissertation focuses on the development of image processing methods to obtain semi-quantitative fluorescence imaging data. These methods allow for the standardization of fluorescence imaging data for tumor characterization. When a fluorophore is located within tissue, changes in the fluorescence intensity can be used to isolate structures of interest. Typically, this is done through the accumulation of a dye in a target tissue either by the enhanced permeation and retention effect (EPR), or through targeted peptide sequences that bind receptors present in specific tissue types. When imaged, the contrast generated by a fluorescent probe can be used to indicate the presence or absence of a structure, bio-chemical compound, or receptor. Fluorescence intensity contrast can answer many biological and clinical questions effectively; however, we were interested in analyzing more than solely contrast when using planar fluorescence imaging. To better understand tumor properties, we developed a series of algorithms that harness additional pieces of information present in the fluorescence signal. We demonstrated that adding novel image processing algorithms enhanced the knowledge obtained from planar fluorescence images. Through this work, we gained an understanding of alternative approaches for processing planar fluorescence imaging data with the goal of improving future cancer diagnostics and therapeutics