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This paper proposes an automatic method for the detection of lumen contours in intravascular OCT images with guide wire shadow artifacts. This algorithm is divided into five main procedures: pre-processing, an Otsu binarization approach, an intensity curve approach, a lumen contour position correction, and image reconstruction and contour extraction. The 30 IVOCT images from six anonymous patients...
Diffuse Optical Tomography (DOT) has become an emerging non-invasive technology, and has been widely used in clinical diagnosis. Functional near-infrared (FNIR) is one of the important applications of DOT. However, FNIR is used to reconstruct two-dimensional (2D) images for the sake of good spatial and temporal resolution. In this paper we propose a multiple-input and multiple-output (MIMO) based...
This paper presents a computational system for three-dimensional reconstruction and surface extraction of the human lower limb as a new methodology of visualizing images of multifaceted ecchymosis on the lower limbs. Through standardization of image acquisition by a mechanical system, an algorithm was developed for three-dimensional and surface reconstruction based on the extraction of depth from...
Magnetic Resonance Current Density Imaging (MRCDI) is an imaging modality, which reconstructs electrical current density distribution inside a material by using Magnetic Resonance Imaging (MRI) techniques. In this study, a current source with maximum current injection capability of 224.7mA, under 1kΩ resistive load is used. Experiments are performed with a 2D uniform phantom, in which a current steering...
We report a prototype Electrical Impedance Imaging System. It is able to detect the gravity-induced changes in the distributions of perfusion and ventilation in the lung between supine and lateral decubitus positions. Impedance data were collected on healthy volunteer subjects and 3D reconstructed images were produced in real-time, 20 frames per second on site, without using averaging or a contrast...
In this study, current density (J) — based Magnetic Resonance Conductivity Tensor Imaging (MRCTI) reconstruction algorithms namely, the Anisotropic Equipotential Projection (AEPP), the Anisotropic J-Substitution (AJS) and the Anisotropic Hybrid J-Substitution (AHJS) algorithms are implemented to reconstruct conductivity tensor images of a physical phantom using a 3T magnetic resonance imaging system...
The cerebral cortex is folded into gyri and sulci in the brains of higher mammals. Quantitative study of the process by which the cortex folds during brain development is critical to a complete understanding of normal brain development and neuro-developmental disorders. In this work, we propose a new method by which to localise nonlinearities in the cortical folding process, and thereby identify regions...
Compressed sensing (CS) is a promising approach to accelerate dynamic magnetic resonance imaging (MRI). Most existing CS methods employ linear sparsifying transforms. The recent developments in non-linear or kernel-based sparse representations have been shown to outperform the linear transforms. In this paper, we present an iterative non-linear CS dynamic MRI reconstruction framework that uses the...
We introduce a fast iterative non-local shrinkage algorithm to recover MRI data from undersampled Fourier measurements. This approach is enabled by the reformulation of current non-local schemes as an alternating algorithm to minimize a global criterion. The proposed algorithm alternates between a non-local shrinkage step and a quadratic subproblem. The resulting algorithm is observed to be considerably...
Due to fundamental characteristics of MRI that limit scan speedup, sub-sampling techniques such as compressed sensing (CS) have been developed for rapid MRI. Current CS MRI approaches utilize sparsity of the image in the wavelet or other transform domains to speed-up acquisition. Another drawback of MRI is its poor signal-to-noise ratio (SNR), which is proportional to the image slice thickness. In...
Low dose positron emission tomography(PET) reconstruction remains a challenging issue for statistical PET reconstruction methods due to the low SNR of data. Due to the ill-conditioning of image reconstruction, proper prior knowledge should be incorporated to constrain the reconstruction. Since PET images are piecewise smoothing, we propose the total variational (TV) minimization based algorithm for...
Recent work on blind compressed sensing (BCS) has shown that exploiting sparsity in dictionaries that are learnt directly from the data at hand can outperform compressed sensing (CS) that uses fixed dictionaries. A challenge with BCS however is the large computational complexity during its optimization, which limits its practical use in several MRI applications. In this paper, we propose a novel optimization...
For DCE MRI applications, the images of adjacent time frames are often similar, especially when motion is minimal, in which case temporal TV is a reasonable regularization term. Temporal constraint reconstruction (TCR) has been developed to reconstruct dynamic images from undersampled k-t space data based on such prior information. However, the convergence speed of the algorithm highly depends on...
An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution...
Compressed sensing (CS) magnetic resonance imaging (MRI) enables the reconstruction of MRI images with fewer samples in k-space. One requirement is that the acquired image has a sparse representation in a known transform domain. MR angiograms are already sparse in the image domain. They can be further sparsified through finite-differences. Therefore, it is a natural application for CS-MRI. However,...
Magnetic resonance imaging (MRI) is considered a key modality for the future as it offers several advantages, including the use of non-ionizing radiation and having no known side effects on the human body, and has recently begun to serve as a key component of multi-modal neuroimaging. However, two major intrinsic problems exist: slow acquisition and intrusive acoustic noise. Parallel MRI (pMRI) techniques...
Magnetic Resonance Angiography (MRA) is a group of techniques based on Magnetic Resonance Imaging (MRI) to image blood vessels. Compressed Sensing (CS) is a mathematical framework to reconstruct MR images from sparse data to minimize the data acquisition time. Image spar-sity is the key in CS to reconstruct MR images. CS technique allows reconstruction from significantly fewer k-space samples as compared...
A new MR spectroscopic imaging method, called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has been recently proposed to enable highresolution metabolic imaging with good SNR. A key problem within the SPICE framework is image reconstruction from a very noisy and sparsely sampled dataset. This paper addresses this problem by integrating the low-rank model used in SPICE reconstruction...
Three-dimensional (3D) endocardium visualization plays an extremely important role in localization and ablation of target areas and can improve the cure rate in computer-aided cardiac surgery. In this paper, we discuss how to reconstruct and update the corresponding endocardium surface model quickly and accurately based on the sparse point cloud that is collected dynamically on the left atrial intima...
Advances in robotic surgery especially in minimally-invasive surgery (MIS) has increased the need for translating computer-vision algorithms in endoscopic imagery to support surgical decisions. While methods for stereo reconstruction have been extensively investigated for man-made environments, such an extensive and detailed study on the pros and cons of stereo reconstruction for endoscopic images...
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