Integration of Vibro-Acoustography Imaging Modality with the Traditional Mammography

Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Integrating unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we establish correspondence between the VA images and traditional X-ray mammogram by adopting a flexible control-point selection technique for image registration. A modified second-order polynomial, which simply leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the X-ray mammography with a registration error of less than 1.65 mm. The fused image is defined as a linear integration of the VA and X-ray images. Moreover, a color-based fusion technique was employed to integrate the images for better visualization of structural information

Medical Diagnosis with Multimodal Image Fusion Techniques

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and  the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications

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

Diagnosis of Breast Cancer by Modular Evolutionary Neural Networks

Abstract Machine learning and pattern recognition play a vital role in the field of biomedical engineering, where the task is to identify or classify a disease based on a set of observations. The inability of a single method to effectively solve the problem gives rise to the use multiple models for solving the same problem in a 'Mixture of Experts' mode. Further the data may be too large for any system to effectively solve the problem. This motivates the use of computational modularity in the system where a number of modules independently solve part of the problem. In this paper we construct a Mixture of Experts model where a number of different techniques are applied to solve the same problem. The individual decision by each of these experts is fused by an integrator that gives the final output. Each of the units is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using Genetic Algorithms which optimizes both the architecture and the parameters. The complete system is used for the diagnosis of Breast Cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches. The system on the whole is highly scalable to both number of attributes and data items

Ultrasound modulated optical tomography in optical diffuse medium using acoustic radiation force

Ultrasound modulated optical tomography (UOT) is a hybrid technique which combines optical contrast with ultrasound (US) resolution to achieve deeper tissue imaging. However, the technique is currently limited due to the weak modulation signal strength and consequently a low Signal-to-Noise Ratio (SNR). One potential way to increase the SNR of UOT is to increase the ultrasound induced particle displacement, by either increasing the ultrasound amplitude or using the acoustic radiation force (ARF). In this thesis, I theoretically studied the relationship between the scatterers‘ displacements and the modulation signal strength and experimentally investigated the ARF in addition to investigating the detrimental effects of shear wave propagation on ultrasound modulated optical (UO) signals. A Monte Carlo simulation tool was developed to investigate how the UOT signal changes with increasing amplitude of ultrasound induced particle displacement in the simulation object. By combining a realistic ultrasound field with UOT simulation, the nonlinear effect of ultrasound on UOT signal was studied for the first time. An UOT experiment system, using a CCD camera and a single element transducer driven by an amplitude-modulated (AM) ultrasound signal to generate an oscillatory ARF, was tested on a tissue mimic phantom. The effect of AM ultrasound on UO signals was investigated for the first time. It was found that with longer CCD exposure times, larger ARF induced particle movements can be captured and the UO signal was increased. Next the effects of an ARF induced shear wave on UO signals are studied. The ARF induced shear waves can propagate transversely out of the focal region and may reduce spatial resolution. This is the first examination of the time evolution of the shear wave effect generated by a short ultrasound burst on the UO signal. The spatial resolution of the system was studied by scanning the phantoms. It was found that by adjusting the timing and length of CCD exposure, shear wave effects can be minimised and both the optical and mechanical properties of the phantom can be detected and distinguished

Ultra-high-speed imaging of bubbles interacting with cells and tissue

Ultrasound contrast microbubbles are exploited in molecular imaging, where bubbles are directed to target cells and where their high-scattering cross section to ultrasound allows for the detection of pathologies at a molecular level. In therapeutic applications vibrating bubbles close to cells may alter the permeability of cell membranes, and these systems are therefore highly interesting for drug and gene delivery applications using ultrasound. In a more extreme regime bubbles are driven through shock waves to sonoporate or kill cells through intense stresses or jets following inertial bubble collapse. Here, we elucidate some of the underlying mechanisms using the 25-Mfps camera Brandaris128, resolving the bubble dynamics and its interactions with cells. We quantify acoustic microstreaming around oscillating bubbles close to rigid walls and evaluate the shear stresses on nonadherent cells. In a study on the fluid dynamical interaction of cavitation bubbles with adherent cells, we find that the nonspherical collapse of bubbles is responsible for cell detachment. We also visualized the dynamics of vibrating microbubbles in contact with endothelial cells followed by fluorescent imaging of the transport of propidium iodide, used as a membrane integrity probe, into these cells showing a direct correlation between cell deformation and cell membrane permeability