Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Department of Biomedical EngineeringAlthough photoacoustic endoscopy (PAE) is a great technique with a huge potential in vascular imaging, it yet has some limitation for the clinical translation. Currently, one of the shortcomings of this system is electromagnetic interference (EMI) noise, which decreases signal-to-noise ratio (SNR) and slows down the technology development. The problem can not be simply overcome by increasing the optical pulse energy, unlike in ultrasound endoscopy, due to laser safety requirements. In addition, because PAE requires a wide separation between ultrasound sensor and amplifier, it is a hard task to make PAE system without EMI noise. To accelerate the progress of PAE field development, we accessed the feasibility of deep-learning-based methods for EMI noise removal. We chose four convolutional neural networks (CNN) architectures: U-Net, Segnet, FCN-16s, FCN-8s, and concluded that modified and tuned U-Net architecture suits the best for our application. We also compared deep-learning-based approach to a classical methods of noise removal to prove CNN supremacy. Applying trained and fine-tuned U-Net allowed us to see a tiny capillary mesh-like structures in a successfully denoised 3D vasculature map image, which can be used in future for the angiogenesis studies. For the future work, as we effectively removed noise from PAE images, we also expect that if we increase training dataset, our method can be applied more broadly to many areas of photoacoustic tomography to overcome EMI noise and poor SNR.ope

In Vivo Vascular Imaging with Photoacoustic Microscopy

Photoacoustic (PA) tomography (PAT) has received extensive attention in the last decade for its capability to provide label-free structural and functional imaging in biological tissue with highly scalable spatial resolution and penetration depth. Compared to modern optical modalities, PAT offers speckle-free images and is more sensitive to optical absorption contrast (with 100% relative sensitivity). By implementing different regimes of optical wavelength, PAT can be used to image diverse light-absorbing biomolecules. For example, hemoglobin is of particular interest in the visible wavelength regime owing to its dominant absorption, and lipids and water are more commonly studied in the near-infrared regime. In this dissertation, one challenge was to quantitatively investigate red-blood-cell dynamics in nailfold capillaries with single-cell resolution PA microscopy (PAM). We recruited healthy volunteers and measured multiple hemodynamic parameters based on individual red blood cells (RBCs). Statistical analysis revealed the process of oxygen release and changes in flow speed for RBCs in a capillary. For the first time on record, oxygen release from individual RBCs in human capillaries was imaged with nearly real-time speed, and the work paved the way for our following study of a specific blood disorder. We next conducted a pilot study on sickle cell disease (SCD), measuring and comparing the parameters related to RBC dynamics between healthy subjects and patients with SCD. In the patient group, we found that capillaries tended to be more tortuous, dilated, and had higher number density. In addition, abnormal RBCs tended to have lower oxygenation in the inlet of a capillary, from where they flowed slower and released a larger fraction of oxygen than normal RBCs. As the only imaging modality able to observe the real-time dynamics of the oxygen release of individual RBCs, PAM provides medically valuable information for diagnostic purposes. As the last focus of this dissertation, we tackled the limited view problem in PAM by introducing an off-axis illumination technique for complementing the original detection view. We demonstrated this technique numerically and then experimentally on phantoms and animals. This simple but very effective method revealed abundant vertical vasculature in a mouse brain that had long been missed by conventional top-illumination PAM. This technique greatly advances future studies on neurovascular responses in mouse brains

Optical and single element transducers for the generation of arbitrary acoustic fields

Precise control over the temporal and spatial properties of acoustic fields in 2 or 3-D is essential for nearly all modern, biomedical applications of ultrasound. At present, piezoelectric arrays dominate, however, despite their ubiquity they have a number of drawbacks that compromise the fidelity with which the output field can be manipulated, particularly at high frequencies and in three dimensions. The development of new novel alternatives for manipulating acoustic fields in 3-D is therefore essential. This thesis presents several new techniques through which this can be achieved using both the optical generation of ultrasound and single element piezoelectric transducers. First, the use of multiple Q-switch laser sources in combination with binary amplitude holograms is investigated for the generation of single and multi-focal acoustic fields. The conditions required for the generation of a focus are established numerically and the method is validated experimentally. Next, two approaches are developed for the generation of arbitrary spatial distributions of pressure using a single optical pulse. The first employs multi-layer optical absorbers: structures composed of several absorbing layers each individually patterned such that the field constructively interferes at a set of target points. The second uses tailored optically absorbing surface profiles: arbitrary surface shapes, fabricated through 3-D printing, designed to geometrically focus over a continuous pattern. Finally, the last chapter of the thesis investigates the use of multi-frequency kinoforms for mapping the field of single element piezoelectric transducers onto multiple complex target distributions. The properties of these kinoforms are explored in depth numerically and experimentally it is shown that multiple complex distributions can be generated in a target plane using this approach

System Optimization and Iterative Image Reconstruction in Photoacoustic Computed Tomography for Breast Imaging

Photoacoustic computed tomography(PACT), also known as optoacoustic tomography (OAT), is an emerging imaging technique that has developed rapidly in recent years. The combination of the high optical contrast and the high acoustic resolution of this hybrid imaging technique makes it a promising candidate for human breast imaging, where conventional imaging techniques including X-ray mammography, B-mode ultrasound, and MRI suffer from low contrast, low specificity for certain breast types, and additional risks related to ionizing radiation. Though significant works have been done to push the frontier of PACT breast imaging, it is still challenging to successfully build a PACT breast imaging system and apply it to wide clinical use because of various practical reasons. First, computer simulation studies are often conducted to guide imaging system designs, but the numerical phantoms employed in most previous works consist of simple geometries and do not reflect the true anatomical structures within the breast. Therefore the effectiveness of such simulation-guided PACT system in clinical experiments will be compromised. Second, it is challenging to design a system to simultaneously illuminate the entire breast with limited laser power. Some heuristic designs have been proposed where the illumination is non-stationary during the imaging procedure, but the impact of employing such a design has not been carefully studied. Third, current PACT imaging systems are often optimized with respect to physical measures such as resolution or signal-to-noise ratio (SNR). It would be desirable to establish an assessing framework where the detectability of breast tumor can be directly quantified, therefore the images produced by such optimized imaging systems are not only visually appealing, but most informative in terms of the tumor detection task. Fourth, when imaging a large three-dimensional (3D) object such as the breast, iterative reconstruction algorithms are often utilized to alleviate the need to collect densely sampled measurement data hence a long scanning time. However, the heavy computation burden associated with iterative algorithms largely hinders its application in PACT breast imaging. This dissertation is dedicated to address these aforementioned problems in PACT breast imaging. A method that generates anatomically realistic numerical breast phantoms is first proposed to facilitate computer simulation studies in PACT. The non-stationary illumination designs for PACT breast imaging are then systematically investigated in terms of its impact on reconstructed images. We then apply signal detection theory to assess different system designs to demonstrate how an objective, task-based measure can be established for PACT breast imaging. To address the slow computation time of iterative algorithms for PACT imaging, we propose an acceleration method that employs an approximated but much faster adjoint operator during iterations, which can reduce the computation time by a factor of six without significantly compromising image quality. Finally, some clinical results are presented to demonstrate that the PACT breast imaging can resolve most major and fine vascular structures within the breast, along with some pathological biomarkers that may indicate tumor development

Conditional Injective Flows for Bayesian Imaging

Most deep learning models for computational imaging regress a single reconstructed image. In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspire to make such point estimates misleading or insufficient. The Bayesian approach models images and (noisy) measurements as jointly distributed random vectors and aims to approximate the posterior distribution of unknowns. Recent variational inference methods based on conditional normalizing flows are a promising alternative to traditional MCMC methods, but they come with drawbacks: excessive memory and compute demands for moderate to high resolution images and underwhelming performance on hard nonlinear problems. In this work, we propose C-Trumpets -- conditional injective flows specifically designed for imaging problems, which greatly diminish these challenges. Injectivity reduces memory footprint and training time while low-dimensional latent space together with architectural innovations like fixed-volume-change layers and skip-connection revnet layers, C-Trumpets outperform regular conditional flow models on a variety of imaging and image restoration tasks, including limited-view CT and nonlinear inverse scattering, with a lower compute and memory budget. C-Trumpets enable fast approximation of point estimates like MMSE or MAP as well as physically-meaningful uncertainty quantification.Comment: 23 pages, 23 figure