Objective localisation of oral mucosal lesions using optical coherence tomography.

PhDIdentification of the most representative location for biopsy is critical in establishing the definitive diagnosis of oral mucosal lesions. Currently, this process involves visual evaluation of the colour characteristics of tissue aided by topical application of contrast enhancing agents. Although, this approach is widely practiced, it remains limited by its lack of objectivity in identifying and delineating suspicious areas for biopsy. To overcome this drawback there is a need to introduce a technique that would provide macroscopic guidance based on microscopic imaging and analysis. Optical Coherence Tomography is an emerging high resolution biomedical imaging modality that can potentially be used as an in vivo tool for selection of the most appropriate site for biopsy. This thesis investigates the use of OCT for qualitative and quantitative mapping of oral mucosal lesions. Feasibility studies were performed on patient biopsy samples prior to histopathological processing using a commercial OCT microscope. Qualitative imaging results examining a variety of normal, benign, inflammatory and premalignant lesions of the oral mucosa will be presented. Furthermore, the identification and utilisation of a common quantifiable parameter in OCT and histology of images of normal and dysplastic oral epithelium will be explored thus ensuring objective and reproducible mapping of the progression of oral carcinogenesis. Finally, the selection of the most representative biopsy site of oral epithelial dysplasia would be investigated using a novel approach, scattering attenuation microscopy. It is hoped this approach may help convey more clinical meaning than the conventional visualisation of OCT images

Automated quantification of mitral valve anatomy using anatomical intelligence in three-dimensional echocardiography

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Anatomical Regurgitant Orifice Detection and Quantification from 3-D Echocardiographic Images

The vena contracta and effective regurgitant orifice area (EROA) are currently used for the clinical assessment of mitral regurgitation (MR) from 2-D color Doppler imaging. In addition to being highly user dependent and having low repeatability, these methods do not represent accurately the anatomic regurgitant orifice (ARO), which affects the adequate assessment of MR patients. We propose a novel method for semi-automatic detection and quantitative assessment of the 3-D ARO shape from 3-D transesophageal echocardiographic images. The algorithm was tested on a set of 25 patients with MR, and compared with EROA for validation. Results indicate the robustness of the proposed approach, with low variability in relation to different settings of user-defined segmentation parameters. Although EROA and ARO exhibited a good correlation (r = 0.8), relatively large biases were measured, indicating that EROA probably underestimates the real shape and size of the regurgitant orifice. Along with the higher reproducibility of the proposed approach, this highlights the limitations of current clinical approaches and underlines the importance of accurate assessment of the ARO shape for diagnosis and treatment in MR patients

Development of a Harmonic Motion Imaging guided Focused Ultrasound system for breast tumor characterization and treatment monitoring

Breast cancer is the most common cancer and the second leading cause of cancer death among women. About 1 in 8 U.S. women (about 12%) will develop invasive breast cancer over the course of their lifetime. Existing methods of early detection of breast cancer include mammography and palpation, either by patient self-examination or clinical breast exam. Palpation is the manual detection of differences in tissue stiffness between breast tumors and normal breast tissue. The success of palpation relies on the fact that the stiffness of breast tumors is often an order of magnitude greater than that of normal breast tissue, i.e., breast lesions feel ''hard'' or ''lumpy'' as compared to normal breast tissue. A mammogram is an x-ray that allows a qualified specialist to examine the breast tissue for any suspicious areas. Mammography is less likely to reveal breast tumors in women younger than 50 years with denser breast than in older women. When a suspicious site is detected in the breast through a breast self-exam or on a screening mammogram, the doctor may request an ultrasound of the breast tissue. A breast ultrasound can provide evidence about whether the lump is a solid mass, a cyst filled with fluid, or a combination of the two. An invasive needle biopsy is the only diagnostic procedure that can definitely determine if the suspicious area is cancerous. In the clinic, 80% of women who have a breast biopsy do not have breast cancer. Most women with breast cancer diagnosed will have some type of surgery to remove the tumor. Depending on the type of breast cancer and how advanced it is, the patient might need other types of treatment as well, such as chemotherapy and radiation therapy. Image-guided minimally-invasive treatment of localized breast tumor as an alternative to traditional breast surgery, such as high intensity focused ultrasound (HIFU) treatment, has become a subject of intensive research. HIFU applies extreme high temperatures to induce irreversible cell injury, tumor apoptosis and coagulative necrosis. Compared with conventional surgical procedures the main advantages of HIFU ablation lie in the fact that it is non-invasive, less scarring and less painful, allowing for shorter recovery time. HIFU can be guided by MRI (MRgFUS) or by conventional diagnostic ultrasound (USgFUS). Worldwide, thousands of patients with uterine fibroids, liver cancer, breast cancer, pancreatic cancer, bone tumors, and renal cancer have been treated by USgFUS. In this dissertation, the objective is to develop an integrated Harmonic Motion Imaging guided Focused Ultrasound (HMIgFUS) system as a clinical monitoring technique for breast HIFU with the added capability of detecting tumors for treatment planning, evaluation of tissue stiffness changes during HIFU ablation for treatment monitoring in real time, and assessment of thermal lesion sizes after treatment evaluation. A new HIFU treatment planning method was described that used oscillatory radiation force induced displacement amplitude variations to detect the HIFU focal spot before lesioning. Using this method, we were able to visualize the HMIgFUS focal region at variable depths. By comparing the estimated displacement profiles with lesion locations in pathology, we demonstrated the feasibility of using this HMI-based technique to localize the HIFU focal spot and predict lesion location during the planning phase. For HIFU monitoring, a HIFU lesion detection and ablation monitoring method was first developed using oscillatory radiation force induced displacement amplitude variations in real time. Using this method, the HMIgFUS focal region and lesion formation were visualized in real time at a feedback rate of 2.4 Hz. By comparing the estimated lesion size against gross pathology, the feasibility of using HMIgFUS to monitor treatment and lesion formation without interruption is demonstrated. In order to reduce the imaging time, it is shown in this dissertation that using the steered FUS beam, HMI can be used to image a 2.3 times larger ROI without requiring physical movement of the transducer. Using steering for HMI can be used to shorten the total imaging duration without requiring physical movement of the transducer. For the application of breast tumor, HMI and HMIgFUS were optimized and applied to ex vivo breast tissue. The results showed that HMI is experimentally capable of mapping and differentiating stiffness in normal and abnormal breast tissues. HMIgFUS can also successfully generate thermal lesions on normal and pathological breast tissues. HMI has also been applied to post-surgical breast mastectomy specimens to mimic the in vivo environment. In the end, the first HMI clinical system has been built with added capability of GUP-based parallel beamforming. A clinical trial has been approved at Columbia University to image breast tumor on patient. The HMI clinical system has shown to be able to map fibroadenoma mass on two patients with valid HMI displacement. The study in this dissertation may yield an early-detection technique for breast cancer without any age discrimination and thus, increase the survival rate

Signal Processing Methods for Quantitative Power Doppler Microvascular Angiography

Operator-dependent instrument settings and the likelihood of image artifacts are two challenges for reliably using three-dimensional (3-D) power Doppler angiography in flow depiction and quantification applications. To address the operator-dependent settings challenge, an automated method for wall filter cut-off selection, the wall filter selection curve (WFSC) method, was developed using flow-phantom images. The flow-phantom WFSCs guided the development of a theoretical signal model relating color pixel density (CPD) and wall filter cut-off frequency. Simulations using the theoretical model were used to define criteria for the WFSC method to be applied to unprocessed power Doppler signals from 3-D vasculature. The adapted WFSC method was combined with a 3-D skeletonization and vessel network reconstruction method to present a two-stage processing method aimed at improving vascular detection, visualization and quantification. The two-stage method was evaluated using two in vivo models; a murine tumor model was used to test the performance of the method in a flow quantification application and a chick embryo chorioallantoic membrane (CAM) model was used to evaluate the method’s value for flow depiction applications. Applying the WFSC method to flow-phantom images improved vessel delineation and vascular quantification to within 3% of the vascular volume fraction of the phantom. Criteria for the WFSC method from the simulations were to assess at least 100 cut-off frequencies and that the CPD variability should be less than 5% to ensure quantification accuracy. Large variations in the cut-off frequency selected using the WFSC among images acquired at different time points and across different animals in the murine tumor model signified the relevance of spatially and temporally adjusting the cut-off frequency. The two-stage method improved visualization of the vascular network and significantly reduced artifacts in both the tumor and CAM models in comparison to images using conventional Doppler processing. In the CAM model, vessel diameters measured in two-stage processed images were more accurate than measurements in images exported from a commercial scanner. The proposed signal processing methods increase accuracy and robustness of qualitative and quantitative studies using 3-D power Doppler angiography to assess vascular networks for flow depiction and quantification

Deep learning in automated ultrasonic NDE -- developments, axioms and opportunities

The analysis of ultrasonic NDE data has traditionally been addressed by a trained operator manually interpreting data with the support of rudimentary automation tools. Recently, many demonstrations of deep learning (DL) techniques that address individual NDE tasks (data pre-processing, defect detection, defect characterisation, and property measurement) have started to emerge in the research community. These methods have the potential to offer high flexibility, efficiency, and accuracy subject to the availability of sufficient training data. Moreover, they enable the automation of complex processes that span one or more NDE steps (e.g. detection, characterisation, and sizing). There is, however, a lack of consensus on the direction and requirements that these new methods should follow. These elements are critical to help achieve automation of ultrasonic NDE driven by artificial intelligence such that the research community, industry, and regulatory bodies embrace it. This paper reviews the state-of-the-art of autonomous ultrasonic NDE enabled by DL methodologies. The review is organised by the NDE tasks that are addressed by means of DL approaches. Key remaining challenges for each task are noted. Basic axiomatic principles for DL methods in NDE are identified based on the literature review, relevant international regulations, and current industrial needs. By placing DL methods in the context of general NDE automation levels, this paper aims to provide a roadmap for future research and development in the area.Comment: Accepted version to be published in NDT & E Internationa