Accelerating Magnetic Resonance Parametric Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)

Quantitative magnetic resonance (MR) parametric mapping is a promising approach for characterizing intrinsic tissue-dependent information. However, long scan time significantly hinders its widespread applications. Recently, low-rank tensor has been employed and demonstrated good performance in accelerating MR parametricmapping. In this study, we propose a novel method that uses spatial patch-based and parametric group-based low rank tensors simultaneously (SMART) to reconstruct images from highly undersampled k-space data. The spatial patch-based low-rank tensor exploits the high local and nonlocal redundancies and similarities between the contrast images in parametric mapping. The parametric group based low-rank tensor, which integrates similar exponential behavior of the image signals, is jointly used to enforce the multidimensional low-rankness in the reconstruction process. In vivo brain datasets were used to demonstrate the validity of the proposed method. Experimental results have demonstrated that the proposed method achieves 11.7-fold and 13.21-fold accelerations in two-dimensional and three-dimensional acquisitions, respectively, with more accurate reconstructed images and maps than several state-of-the-art methods. Prospective reconstruction results further demonstrate the capability of the SMART method in accelerating MR quantitative imaging.Comment: 15 pages, 12 figure

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models

Image Restoration Methods for Retinal Images: Denoising and Interpolation

Retinal imaging provides an opportunity to detect pathological and natural age-related physiological changes in the interior of the eye. Diagnosis of retinal abnormality requires an image that is sharp, clear and free of noise and artifacts. However, to prevent tissue damage, retinal imaging instruments use low illumination radiation, hence, the signal-to-noise ratio (SNR) is reduced which means the total noise power is increased. Furthermore, noise is inherent in some imaging techniques. For example, in Optical Coherence Tomography (OCT) speckle noise is produced due to the coherence between the unwanted backscattered light. Improving OCT image quality by reducing speckle noise increases the accuracy of analyses and hence the diagnostic sensitivity. However, the challenge is to preserve image features while reducing speckle noise. There is a clear trade-off between image feature preservation and speckle noise reduction in OCT. Averaging multiple OCT images taken from a unique position provides a high SNR image, but it drastically increases the scanning time. In this thesis, we develop a multi-frame image denoising method for Spectral Domain OCT (SD-OCT) images extracted from a very close locations of a SD-OCT volume. The proposed denoising method was tested using two dictionaries: nonlinear (NL) and KSVD-based adaptive dictionary. The NL dictionary was constructed by adding phases, polynomial, exponential and boxcar functions to the conventional Discrete Cosine Transform (DCT) dictionary. The proposed denoising method denoises nearby frames of SD-OCT volume using a sparse representation method and combines them by selecting median intensity pixels from the denoised nearby frames. The result showed that both dictionaries reduced the speckle noise from the OCT images; however, the adaptive dictionary showed slightly better results at the cost of a higher computational complexity. The NL dictionary was also used for fundus and OCT image reconstruction. The performance of the NL dictionary was always better than that of other analytical-based dictionaries, such as DCT and Haar. The adaptive dictionary involves a lengthy dictionary learning process, and therefore cannot be used in real situations. We dealt this problem by utilizing a low-rank approximation. In this approach SD-OCT frames were divided into a group of noisy matrices that consist of non-local similar patches. A noise-free patch matrix was obtained from a noisy patch matrix utilizing a low-rank approximation. The noise-free patches from nearby frames were averaged to enhance the denoising. The denoised image obtained from the proposed approach was better than those obtained by several state-of-the-art methods. The proposed approach was extended to jointly denoise and interpolate SD-OCT image. The results show that joint denoising and interpolation method outperforms several existing state-of-the-art denoising methods plus bicubic interpolation.4 month

MRI Excitation Pulse Design and Image Reconstruction for Accelerated Neuroimaging

Excitation pulse design and image reconstruction are two important topics in MR research for enabling faster imaging. On the pulse design side, selective excitations that confine signals to be within a small region-of-interest (ROI) instead of the full imaging field-of-view (FOV) can be used to reduce sampling density in the k-space, which is a direct outcome of the change in the underlying Nyquist sampling rate. On the reconstruction side, besides improving imaging algorithms’ ability to restore images from less data, another objective is to reduce the reconstruction time, particularly for dynamic imaging applications. This dissertation focuses on these two perspectives: Chapter II is devoted to the excitation pulse design. Specifically, we exploit auto-differentiation frameworks that automatically apply the chain rule on complicated computations. We derived and developed a computationally efficient Bloch-simulator and its explicit Bloch simulation Jacobian operations using such frameworks. This simulator can yield numerical derivatives with respect to pulse RF and gradient waveforms given arbitrary sub-differentiable excitation objective functions. The method does not rely on the small-tip approximation, and is accurate as long as the Bloch simulation can correctly model the spin movements due to the excitation pulses. In particular, we successfully applied this pulse design approach for jointly designing RF and gradient waveforms for 3D spatially tailored large-tip excitation objectives. The auto-differentiable pulse design method can yield superior 3D spatially tailored excitation profiles that are useful for inner volume (IV) imaging, where one attempts to image a volumetric ROI at high spatiotemporal resolution without aliasing from signals outside the IV (i.e., outer volume). In Chapter III, we propose and develop a novel steady-state IV imaging strategy which suppresses aliasing by saturating the outer volume (OV) magnetizations via a 3D tailored OV excitation pulse that is followed by a signal crusher gradient. This saturation based strategy can substantially suppress the unwanted aliasing for common steady-state imaging sequences. By eliminating the outer volume signals, one can configure acquisitions for a reduced FOV to shorten the scanning time and increase spatiotemporal resolution for applications such as dynamic imaging. In dynamic imaging (e.g., fMRI), where a time series is to be reconstructed, non-iterative reconstruction algorithms may offer savings in overall reconstruction time. Chapter IV focuses on non-iterative image reconstruction, specifically, extending the GRAPPA algorithm to general non-Cartesian acquisitions. We analyzed the formalism of conventional GRAPPA reconstruction coefficients, generalized it to non-Cartesian scenarios by using properties of the Fourier transform, and obtained an efficient non-Cartesian GRAPPA algorithm. The algorithm attains reconstruction quality that can rival classical iterative imaging methods such as conjugate gradient SENSE and SPIRiT. In summary, this dissertation has proposed and developed multiple methods for accelerating MR imaging, from pulse design to reconstruction. While devoted to neuroimaging, the proposed methods are general and should also be useful for other applications.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168085/1/tianrluo_1.pd

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

Joint SAR imaging and multi-feature decomposition from 2-D under-sampled data via low-rankness plus sparsity priors

In this paper, we introduce a multi-feature decomposition approach to the problem of synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range and azimuth directions. Conventional SAR image formation methods may produce images that are not appropriate for interpretation tasks such as segmentation and automatic target recognition. We deal with this problem using an efficient joint SAR image reconstruction-decomposition framework in which features of interest are enhanced and decomposed simultaneously. Unlike conventional methods, our proposed framework provides multiple segment images along with a composite SAR image. In the composite image not only the resolution is improved but also both the speckle and sidelobe artifacts are reduced. In the decomposed images, different components can be roughly attributed to different potential segments, which facilitate the subsequent interpretation tasks such as shape-based recognition or region segmentation. Moreover, these decomposed images contain easier-to-segment regions rather than taking the entire scene for segmenting the feature of interest. By formulating the SAR image reconstruction as a low-rank plus multi-feature decomposition problem, the optimization problem is solved based on the alternating direction method of multipliers. Using combined dictionaries, multiple transform-sparse components are represented efficiently by a linear combination of multiple sparsifying matrices associated with the features of interest in the scene. Our proposed method jointly reconstructs and decomposes different pieces of the imaged SAR scene, in particular the low-rank part of the background and sparsely represented features of interest, from under-sampled observed data. Using extensive experimental results we show the effectiveness of the proposed method on both synthetic and real SAR images