Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 90-94). Also available in electronic version.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 101-111). Also available in electronic version.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 100).

Ultrasound imaging plays an important role in biomedical diagnostics due its safety, noninvasive nature, and low cost. Conventional ultrasound systems typically form an image frame by scanning the region of interest line-by-line, using a focused beam during transmission and dynamic focusing during reception. Alternatively, the region of interest can be insonified at once using a plane wave, which allows for ultrafast data acquisition rates but reduces the resulting image quality. The latter can be improved by means of coherent plane-wave compounding (CPWC), whereby multiple plane waves are emitted at different angles to obtain multiple image datasets that are subsequently combined to enhance the final compounded image. We present two novel Fourier-domain techniques for CPWC image reconstruction from raw linear-array sensor data. In particular, we show how to modify two classic algorithms used for geophysical data processing, namely Stolt's and slant-stack depth migration under zero-offset constant-velocity assumptions, so that their new versions become applicable to plane-wave ultrasound data processing. To demonstrate the merits and limitations of our approach, we provide qualitative and quantitative comparisons with other Fourier-domain methods reported in the ultrasound literature. Our evaluation results are based on the image resolution, contrast, and similarity metrics obtained for several public-domain experimental benchmark datasets. We also describe another novel Fourier-domain method for CPWC image reconstruction that can be used in situations where the speed of sound varies with depth in a layered propagation medium. Our technique builds on Gazdag's phase-shift migration algorithm that has been modified to handle plane-wave ultrasound data processing. Our simulation results show that the proposed method is capable of accurately imaging point targets in a three-layer medium, mimicking tissue-bone-tissue ultrasound propagation. / Graduate

Image recovery and segmentation are always the fundamental tasks in image processing field, because of their so many contributions in practical applications. As in the past ten years, variational methods have achieved a great success on these two issues, in this thesis, we continue to work on proposing several new variational approaches for restoring and segmenting an image. This thesis contains two parts. The first part addresses recovering an image and the second part emphasizes on segmenting. Along with the wide utilization of magnetic resonance imaging (MRI) technique, we particularly deal with blurry images corrupted by Rician noise. In chapter 1, two new convex variational models for recovering an image corrupted by Rician noise with blur are presented. These two models are motivated by the non-convex maximum-a-posteriori (MAP) model proposed in the prior papers. In the first method, we use an approximation item to the zero order of the modified Bessel function in the MAP model and add an entropy-like item to obtain a convex model. Through studying on the statistical properties of Rician noise, we bring up a strictly convex model by adding an additional data-fidelity term in the MAP model in the second method. Primal-dual methods are applied to solve the models. The simulation outcomes show that our models outperform some existed effective models in both recovery image quality and computational time. Cone beam CT (CBCT) is routinely applied in image guided radiation therapy (IGRT) to help patient setup. Its imaging dose, however, is still a concern, limiting its wide applications. It has been an active research topic to develop novel technologies for radiation dose reduction. In chapter 2, we propose an improvement of practical CBCT dose control scheme - temporal non-local means (TNLM) scheme for IGRT. We denoise the scanned image with low dose by using the previous images as prior knowledge. We combine deformation image registration and TNLM. Different from the TNLM, in the new method, for each pixel, the search range is not fixed, but based on the motion vector between the prior image and the obtained image. By doing this, it is easy to find the similar pixels in the previous images, but also can reduce the computational time since it does not need large search windows. The phantom and patient studies illuminate that the new method outperforms the original one in both image quality and computational time. In the second part, we present a two-stage method for segmenting an image corrupted by blur and Rician noise. The method is motivated by the two-stage segmentation method developed by the authors in 2013 and restoration method for images with Rician noise. First, based on the statistical properties of Rician noise, we present a new convex variant of the modified Mumford-Shah model to get the smooth cartoon part {dollar}u{dollar} of the image. Then, we cluster the cartoon {dollar}u{dollar} into different parts to obtain the final contour of different phases of the image. Moreover, {dollar}u{dollar} from the first stage is unique because of the convexity of the new model, and it needs to be computed only once whenever the thresholds and the number of the phases {dollar}K{dollar} in the second stage change. We implement the simulation on the synthetic and real images to show that our model outperforms some existed segmentation models in both precision and computational time

X-ray CT based baggage scanners are widely used in security applications. Recently, there has been increased interest in view-limited systems which can improve the scanning throughput while maintaining the threat detection performance. However as very few view angles are acquired in these systems, the image reconstruction problem is challenging. Standard reconstruction algorithms such as the filtered backprojection create strong artifacts when working with view-limited data. In this work, we study the performance of a variety of reconstruction algorithms for both single and multi-energy view-limited systems.

No description available.

Digital breast tomosynthesis is a new technology that provides three-dimensional information of the breast and makes it possible to distinguish the cancer from overlying breast tissues. We are dedicated to optimizing image reconstruction and imaging configuration for a new multi-beam parallel digital breast tomosynthesis prototype system. Several commonly used algorithms from the typical image reconstruction models which were used for iso-centric tomosynthesis systems were investigated for our multi-beam parallel tomosynthesis imaging system. The representative algorithms, including back-projection (BP), filtered back-projection (FBP), matrix inversion tomosynthesis reconstruction (MITS), maximum likelihood expectation maximization (MLEM), ordered-subset maximum likelihood expectation maximization (OS-MLEM), simultaneous algebraic reconstruction technique (SART), were implemented to fit our system design. An accelerated MLEM algorithm was proposed, which significantly reduced the running time but had the same image quality. Furthermore, two statistical variants of BP reconstruction were validated for our tomosynthesis prototype system. Experiments based on phantoms and computer simulations show that the prototype system combined with our algorithms is capable of providing three-dimensional information of the objects with good image quality and has great potentials to improve digital breast tomosynthesis technology. Four methodologies were employed to optimize the reconstruction algorithms and different imaging configurations for the prototype system. A linear tomosynthesis imaging analysis tool was used to investigate blurring-out reconstruction algorithms. Computer simulations of sphere and wire objects aimed at the performance of out-of-plane artifact removal. A frequency-domain-based methodology, relative NEQ(f) analysis, was investigated to evaluate the overall system performance based on the propagation of signal and noise. Conclusions were made to determine the optimal image reconstruction algorithm and imaging configuration of this new multi-beam parallel digital breast tomosynthesis prototype system for better image quality and system performance.

No description available.

Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L₁-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction. / Ph. D. / Medical imaging techniques play a central role in modern clinical diagnoses and treatments. Consequently, there is a constant demand to increase the overall quality of medical images. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. Multimodal imaging techniques can provide more detailed diagnostic information by fusing medical images from different imaging modalities, thereby allowing clinical results to be more comprehensive to improve clinical interpretation. A new form of multimodal imaging technique, which combines the imaging procedures of computed tomography (CT) and magnetic resonance imaging (MRI), is known as the “omnitomography.” Both computed tomography and magnetic resonance imaging are the most commonly used medical imaging techniques today and their intrinsic properties are complementary. For example, computed tomography performs well for bones whereas the magnetic resonance imaging excels at contrasting soft tissues. Therefore, a multimodal imaging system built upon the fusion of these two modalities can potentially bring much more information to improve clinical diagnoses. However, the planned omni-tomography systems face enormous challenges, such as the limited ability to perform image reconstruction due to mechanical and hardware restrictions that result in significant undersampling of the raw data. Image reconstruction is a procedure required by both computed tomography and magnetic resonance imaging to convert raw data into final images. A general condition required to produce a decent quality of an image is that the number of samples of raw data must be sufficient and abundant. Therefore, undersampling on the omni-tomography system can cause significant degradation of the image quality or artifacts after image reconstruction. To overcome this drawback, we resort to compressed sensing techniques, which exploit the sparsity of the medical images, to perform iterative based image reconstruction for both computed tomography and magnetic resonance imaging. The sparsity of the images is found by applying sparse transform such as discrete gradient transform or wavelet transform in the image domain. With the sparsity and undersampled raw data, an iterative algorithm can largely compensate for the data inadequacy problem and it can reconstruct the final images from the undersampled raw data with minimal loss of quality. In addition, a novel “projection distance” is created to perform a joint reconstruction which further promotes the quality of the reconstructed images. Specifically, the projection distance exploits the structural similarities shared between the image of computed tomography and magnetic resonance imaging such that the insufficiency of raw data caused by undersampling is further accounted for. The improved performance of the proposed approach is demonstrated using a pair of undersampled body images and a pair of undersampled head images, each of which consists of an image of computed tomography and its magnetic resonance imaging counterpart. These images are tested using the proposed joint reconstruction method in this work, the conventional reconstructions such as filtered backprojection and Fourier transform, and reconstruction strategy without using multimodal imaging information (independent reconstruction). The results from this work show that the proposed method addressed these challenges by significantly improving the image quality from highly undersampled raw data. In particular, it improves about 5dB in signal-to-noise ratio and nearly 10% in structural similarity measurements compared to other methods. It achieves similar image quality by using less than 5% of the X-ray dose for computed tomography and 30% sampling rate for magnetic resonance imaging. It is concluded that, by using compressed sensing techniques and exploiting structural similarities, the planned joint computed tomography and magnetic resonance imaging system can perform imaging outstanding tasks with highly undersampled raw data.