Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 60-62). Also available in electronic version. Access restricted to campus users.

By integrating three complementary imaging techniques - ultrasound, elasticity and photoacoustic imaging, a hybrid imaging system utilizing an array transducer is proposed for various biomedical imaging applications including cancer detection, diagnosis and therapy monitoring. Simultaneous imaging of the anatomy (ultrasound imaging), changes in biomechanical properties (elasticity imaging) and cancer-induced angiogenesis (photoacoustic imaging) of tissue is based on many synergistic features of these modalities and may result in a unique and important imaging tool. In this study, numerical analysis and experimental studies are presented to demonstrate the feasibility, to evaluate the performance, and also to improve the quality of the combined array-based ultrasound, elasticity and photoacoustic imaging system. To estimate spatial resolution, a point source was imaged using ultrasound and photoacoustic imaging modes. Then, several tissue mimicking phantoms were examined using ultrasound, photoacoustic and elasticity imaging. In elasticity imaging, ultrasound frames were acquired during deformation of the tissue. To reduce the data acquisition time of the system, high frame rate imaging was used. High frame rate imaging is possible by transmitting a broader and less focused ultrasound beam but the image quality is sacrificed. Thus, we compared the quality of the high frame rate and conventional ultrasound images. In photoacoustic imaging, acoustic transients are generated simultaneously in the entire volume of the laser irradiated tissue. Hence, image formation (beamforming) algorithms were developed based on the characteristics of the photoacoustic signals. Then, adaptive beamforming method is suggested to improve the image quality of the photoacoustic imaging. The results of the numerical analyses and experimental studies clearly indicate that ultrasound, elasticity and photoacoustic imaging techniques complement each other and together provide critical information needed for the reliable detection and diagnosis of diseases. / text

Cancer has been one of main causes of human deaths for many years. Early detection of cancer is essential to provide definitive treatment. Among many cancer treatment methods, nanoparticle-mediated photothermal therapy is considered as one of the promising cancer treatment methods because of its non-invasiveness and cancer-specific therapy. Ultrasound and photoacoustic imaging can be utilized for both cancer detection and photothermal therapy guidance. Ultrasound elasticity imaging can detect cancer using tissue elastic properties. Once cancer is diagnosed, spectroscopic photoacoustic imaging can be used to monitor nanoparticle delivery before photothermal therapy. When nanoparticles are well accumulated at the tumor, ultrasound and photoacoustic-based thermal imaging can be utilized for estimating temperature distribution during photothermal therapy to guide therapeutic procedure. In this dissertation, ultrasound beamforming, elasticity imaging, and spectroscopic photoacoustic imaging methods were developed to improve cancer detection and therapy guidance. Firstly, a display pixel based synthetic aperture focusing method was developed to fundamentally improve ultrasound image qualities. Secondly, an autocorrelation based sub-pixel displacement estimation method was developed to enhance signal-to-noise ratio of elasticity images. The developed elasticity imaging method was utilized to clinically evaluate the feasibility of using ultrasound elasticity imaging for prostate cancer detection. Lastly, a minimum mean square error based spectral separation method was developed to robustly utilize spectroscopic photoacoustic imaging. The developed spectroscopic photoacoustic imaging method was utilized to demonstrate ultrasound and photoacoustic image-guided photothermal cancer therapy using in-vivo tumor-bearing mouse models. The results of these studies suggest that ultrasound and photoacoustic imaging can assist both cancer detection and therapy guidance. / text

Imaging speed is an important issue in magnetic resonance imaging (MRI), as subject motion during image acquisition is liable to produce artifacts in the image. However, the speed at which data can be collected in conventional MRI is fundamentally limited by physical and physiological constraints. Parallel MRI is a technique that utilizes multiple receiver coils to increase the imaging speed beyond previous limits by reducing the amount of acquired data without degrading the image quality. In order to remove the image aliasing due to k-space undersampling, parallel MRI reconstructions invert the encoding matrix that describes the net effect of the magnetic field gradient encoding and the coil sensitivity profiles. The accuracy, stability, and efficiency of a matrix inversion strategy largely dictate the quality of the reconstructed image. This thesis addresses five specific issues pertaining to this linear inverse problem with practical solutions to improve clinical and research applications. First, for reconstruction algorithms adopting a k-space interpolation approach to the linear inverse problem, two methods are introduced that automatically select the optimal k-space subset samples participating in the synthesis of a missing datum, guaranteeing an optimal compromise between accuracy and stability, i.e. the best balance between artifacts and signal-to-noise ratio (SNR). While the former is based on cross-validation re-sampling technique, the second utilizes a newly introduced data consistency error (DCE) metric that exploits the shift invariance property of the reconstruction kernel to provide a goodness measure of k-space interpolation in parallel MRI. Additionally, the utility of DCE as a metric for characterizing and comparing reconstruction methods is demonstrated. Second, a DCE-based strategy is introduced to improve reconstruction efficiency in real time parallel dynamic MRI. Third, an efficient and reliable reconstruction method that operates on gridded k-space for parallel MRI using non-Cartesian trajectories is introduced with a significant computational gain for applications involving repetitive measurements. Finally, a pulse sequence that combines parallel MRI and multi-echo strategy is introduced for improving SNR and reducing the geometric distortion in diffusion tensor imaging. In addition, the sequence inherently provides a T2 map, complementing information that can be useful for some applications.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Hu, Tom; Committee Co-Chair: Rahnema, Farzad; Committee Member: Wang, Chris; Committee Member: Yanasak, Nathan.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22460号 / 工博第4721号 / 新制||工||1737(附属図書館) / 京都大学大学院工学研究科物質エネルギー化学専攻 / (主査)教授 近藤 輝幸, 教授 大江 浩一, 教授 中村 正治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

<p>  </p> <p>        Analyzing single molecule emission patterns plays a critical role in retrieving the structural and physiological information of their tagged targets, and further, understanding their interactions and cellular context. These emission patterns of tiny light sources (i.e. point spread functions, PSFs) simultaneously encode information such as the molecule’s location, orientation, the environment within the specimen, and the paths the emitted photons took before being captured by the camera. However, retrieving multiple classes of information beyond the 3D position from complex or high-dimensional single molecule data remains challenging, due to the difficulties in perceiving and summarizing a comprehensive yet succinct model. We developed smNet, a deep neural network that can extract multiplexed information near the theoretical limit from both complex and high-dimensional point spread functions. Through simulated and experimental data, we demonstrated that smNet can be trained to efficiently extract both molecular and specimen information, such as molecule location, dipole orientation, and wavefront distortions from complex and subtle features of the PSFs, which otherwise are considered too complex for established algorithms. </p> <p>        Single molecule localization microscopy (SMLM) forms super-resolution images with a resolution of several to tens of nanometers, relying on accurate localization of molecules’ 3D positions from isolated single molecule emission patterns. However, the inhomogeneous refractive indices distort and blur single molecule emission patterns, reduce the information content carried by each detected photon, increase localization uncertainty, and thus cause significant resolution loss, which is irreversible by post-processing. To compensate tissue induced aberrations, conventional sensorless adaptive optics methods rely on iterative mirror-changes and image-quality metrics to compensate aberrations. But these metrics result in inconsistent, and sometimes opposite, metric responses which fundamentally limited the efficacy of these approaches for aberration correction in tissues. Bypassing the previous iterative trial-then-evaluate processes, we developed deep learning driven adaptive optics (DL-AO), for single molecule localization microscopy (SMLM) to directly infer wavefront distortion and compensate distortion near real-time during data acquisition. our trained deep neural network monitors the individual emission patterns from single molecule experiments, infers their shared wavefront distortion, feeds the estimates through a dynamic filter (Kalman), and drives a deformable mirror to compensate sample induced aberrations. We demonstrated that DL-AO restores single molecule emission patterns approaching the conditions untouched by specimen and improves the resolution and fidelity of 3D SMLM through brain tissues over 130 µm, with as few as 3-20 mirror changes.</p>

<p dir="ltr">Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) via MRI are powerful, noninvasive methods for imaging of the human brain. Here, two studies are presented which explore algorithm development for the processing and analysis of fMRI and DTI-MRI data.</p><p dir="ltr">In the first study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34–80 days after the contact activities play season ended (post-season). For each imaging session, functional connectomes (FCs) were computed for each athlete and compared across sessions using a metric reflecting the (self) similarity between two FCs. HAEs were monitored during all practices and games throughout the season using head-mounted sensors. Relative to the pre-season scan session, football athletes exhibited decreased FC self-similarity at the later in-season session, with apparent recovery of self-similarity by the time of the post-season session. In addition, both within and post-season self-similarity was correlated with cumulative exposure to head acceleration events. These results suggest that repetitive exposure to HAEs produces alterations in functional brain connectivity and highlight the necessity of collision-free recovery periods for football athletes.</p><p dir="ltr">In the second study, a method for harmonization of DTI-MRI data across sites was assessed. Pooling of data from multiple sites is limited by noise characteristics of individual scanners and their receive chain elements (e.g., coils, filters, algorithms), requiring careful consideration of methods to harmonize multisite data. Here, the ComBat data harmonization method was assessed on DTI-MRI data to determine if the harmonizing transformation produced by the algorithm could be transferred to harmonize new subject data from previously-observed sites without necessitating reharmonization of pre-existing data. Results indicated that this transferable ComBat methodology (T-ComBat) yielded reduced differences in fractional anisotropy and mean diffusivity across sites when compared with unharmonized data but did not fully reach the performance of ComBat applied to the entire dataset. Results of this study provide guidelines for circumstances (namely, the proportion of subjects one may wish to add to an existing dataset) under which T-ComBat may be effectively applied to harmonize new subject DTI-MRI data.</p>

published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Diagnostic Radiology / Master / Master of Philosophy