Fourier transform nuclear magnetic resonance tomographic imaging

Nuclear Magnetic Resonance (NMR) tomographic imaging is a newly emerging, noninvasive, three-dimensional (3-D) imaging technique. Although similar to the well known X-ray Computerized Tomography (X-CT), it uses magnetic fields and RF signals to obtain anatomical information about the human body as cross-sectional images in any desired direction, and can easily discriminate between healthy and abnormal tissues. This new technique is an interdisciplinary science which encompasses the latest technologies in electrical, electronics, computers, physics, chemistry, mathematics, and medical sciences. Principles of this new technique known as "Fourier transform nuclear magnetic resonance imaging" or simply "NMR imaging" are reviewed from the physics and engineering points of view to provide basic concepts and tools, which, hopefully, will be useful for the future development of this exciting new field. Along with the review of the basic principles and methods involved in NMR tomography, computer simulations and modelings are presented to clarify the complexity of the NMR imaging method and provide an insight into the method, especially image-formation aspects and processing, the central theme of NMR tomography. In this paper, four main types of imaging methods-namely, line-scan imaging, direct Fourier-transform (Kumar-Welti-Ernst method) imaging, line-integral projection reconstruction, and plane-integral projection reconstruction, as well as the possibility of relaxation time imaging, are discussed in detail Methods of improving performance with respect to the statistical aspects of image quality and imaging times are also discussed.     

Three-dimensional display and analysis of tomographic volume images utilizing a varifocal mirror

Described is a system for the multidimensional display and analysis of tomographic images utilizing the principle of variable focal (varifocal) length optics. The display system uses a vibrating mirror in the form of an aluminized membrane stretched over a loudspeaker, coupled with a cathode ray tube (CRT) display monitor suspended face down over the mirror, plus the associated digital hardware to generate a space filling display. The mirror is made to vibrate back and forth, as a spherical cap, by exciting the loudspeaker with a 30 Hz sine wave. "Stacks" of 2-D tomographic images are displayed, one image at a time, on the CRT in synchrony with the mirror motion. Because of the changing focal length of the mirror and the integrating nature of the human eye-brain combination, the time sequence of 2-D images, displayed on the CRT face, appears as a 3-D image in the mirror. The system simplifies procedures such as: reviewing large amounts of 3-D image information, exploring volume images in three dimensions, and gaining an appreciation or understanding of three-dimensional shapes and spatial relationships. The display system facilitates operator interactivity, e.g., the user can point at structures within the volume image, remove selected image regions to more clearly visualize underlying structure, and control the orientation of brightened oblique planes through the volume.     

Three-dimensional segmentation and interpolation of magnetic resonance brain images

The authors propose a method for the 3-D reconstruction of the brain from anisotropic magnetic resonance imaging (MRI) brain data. The method essentially consists in two original algorithms both for segmentation and for interpolation of the MRI data. The segmentation process is performed in three steps. A gray level thresholding of the white and gray matter tissue is performed on the brain MR raw data. A global white matter segmentation is automatically performed with a global 3-D connectivity algorithm which takes into account the anisotropy of the MRI voxel. The gray matter is segmented with a local 3-D connectivity algorithm. Mathematical morphology tools are used to interpolate slices. The whole process gives an isotropic binary representation of both gray and white matter which are available for 3-D surface rendering. The power and practicality of this method have been tested on four brain datasets. The segmentation algorithm favorably compares to a manual one. The interpolation algorithm was compared to the shaped-based method both quantitatively and qualitatively.     

Three-dimensional display rendering acceleration using occlusion camera reference images

Volumetric three-dimensional (3-D) displays allow the user to explore a 3-D scene free of joysticks, keyboards, goggles, or trackers. For non-trivial scenes, computing and transferring a 3-D image to the display takes hundreds of seconds, which is a serious bottleneck for many applications. We propose to represent the 3-D scene with an occlusion camera reference image (OCRI). The OCRI is a compact scene representation that stores only and all scene samples that are visible from a viewing volume centered at a reference viewpoint. The OCRI enables computing and transferring the 3-D image an order of magnitude faster than when the entire scene is processed. The OCRI approach can be readily applied to several volumetric display technologies; we have tested the OCRI approach with good results on a volumetric display that creates a 3-D image by projecting 2-D scene slices onto a rotating screen.     

Three-dimensional optical tomographic imaging of breast in a humansubject

We present for the first time a full three-dimensional (3-D) reconstruction of absorption images of breast from continuous-wave (cw) measurements performed on a premenopausal woman. Our 3-D optical images clearly reveal a large primary tumor as well as a small secondary tumor in a separate location of the breast. The multiple tumors identified by our 3-D optical imaging have been confirmed by the subsequent biopsy examination of the breast. Quantitative information of the optical images obtained is provided in terms of the location, size, and absorption coefficient of the tumors.     

Three-dimensional imaging methods based on multiview images

Three-dimensional imaging methods, based on parallaxes as their depth cues, can be classified into the stereoscopic providing binocular parallax only, and multiview providing both binocular and motion parallaxes. In these methods, the parallaxes are provided by creating a viewing zone with use of either a special optical eyeglasses or a special optical plate as their viewing zone-forming optics. For the stereoscopic image generations, either the eyeglasses or the optical plate can be employed, but for the multiview the optical plate or the eyeglasses with a tracking device. The stereoscopic image pair and the multiview images are presented either simultaneously or as a time sequence with use of projectors or display panels. For the case of multiview images, they can also be presented as two images at a time according to the viewer's movements. The presence of the viewing zone-forming optics often causes undesirable problems, such as appearance of moire/spl acute/ fringes, image quality deterioration, depth reversion, limiting viewing regions, low image brightness, image blurring, and inconveniences of wearing.     

Three-dimensional holographic image sensing and Integral imaging display

In this paper, we propose three-dimensional (3-D) holographic sensing, and computational/optical 3-D integral imaging reconstruction. We demonstrate experimentally that through the integral imaging technique, it is possible to reconstruct a full 3-D scene which has been obtained by digital holograms. Three-dimensional color objects can also be displayed optically in 3-D without convergence-accommodation conflict using a microlens array, and a two-dimensional (2-D) display panel illuminated by incoherent light. The proposed approach takes advantages of high resolution holographic sensing and robust 3-D integral imaging visualization.     

Neuromagnetic localization using magnetic resonance images

Ionic flow associated with neural activation of the brain produces a magnetic field, called the neuromagnetic field, that can be measured outside the head using a highly sensitive superconducting quantum interference device (SQUID)-based neuromagnetometer. Under certain conditions, the sources producing the neuromagnetic field can be localized from a sampling of the neuromagnetic field. Neuromagnetic measurements alone, however, do not contain sufficient information to visualize brain structure. Thus, it is necessary to combine neuromagnetic localization with an anatomical imaging technique such as magnetic resonance imaging (MRI) to visualize both function and anatomy in vivo. Using experimentally measured human neuromagnetic fields and magnetic resonance images, the authors have developed a technique to register accurately these two modalities and have applied the registration procedure to portray the spatiotemporal distribution of neural activity evoked by auditory stimulation.     

Interframe coding of magnetic resonance images

Presents a new interframe coding method for medical images, in particular magnetic resonance (MR) images. Until now, attempts in using interframe redundancies for coding MR images have been unsuccessful. The authors believe that the main reason for this is twofold: unsuitable interframe estimation models and the thermal noise inherent in magnetic resonance imaging (MRI). The interframe model used here is a continuous affine mapping based on (and optimized by) deforming triangles. The inherent noise of MRI is dealt with by using a median filter within the estimation loop. The residue frames are quantized with a zero-tree wavelet coder, which includes arithmetic entropy coding. This particular method of quantization allows for progressive transmission, which aside from avoiding buffer control problems is very attractive in medical imaging applications.     

Analysis of dynamic magnetic resonance images

Dynamic magnetic resonance (MR) imaging with contrast agents is a very promising technique for studying tissue perfusion in vivo. A temporal series of magnetic resonance images of the same slice are acquired following the injection of a contrast agent into the blood stream. The image intensity depends on the local concentration of the contrast agent, so that tissue perfusion can be studied by the image series. A new method of analyzing such series is described here. Nonparametric linear regression is used for modeling the image intensity along the series on a pixel by pixel basis. After modeling, some relevant quantities describing the time series are obtained and displayed as images. Due to its flexibility, this approach is preferred to parametric modeling when pathology is present since this can induce a wide spread of patterns for the pixel image intensity along time. Results of the application of the method to series of dynamic magnetic resonance images from ischaemic rat brains after the injection of the susceptibility agent Sprodiamide Inj. (Dy-DTPA-BMA) are shown and compared to results from a related known method.     

Electromagnetics in magnetic resonance imaging

Magnetic resonance imaging (MRI) is a powerful new imaging method, which produces cross-sectional tomographic and three-dimensional images similar to those of x-ray computed tomography (CT). However, rather than relying on harmful ionizing radiation, MRT is based on the interaction between RF fields and certain atomic nuclei in the body, when they are in the presence of a strong magnetic field. An MRI system is one of the few complete systems in which the design relies heavily upon a knowledge of electromagnetics. We give a tutorial on the electromagnetic analysis and design of three key components of an MRI system, namely, the magnet, the gradient coil, and the radiofrequency (RF) coil. We also discuss the analysis and characterization of the interactions of RF electromagnetic fields with biological subjects     

Spread spectrum magnetic resonance imaging

We propose a novel compressed sensing technique to accelerate the magnetic resonance imaging (MRI) acquisition process. The method, coined spread spectrum MRI or simply s(2)MRI, consists of premodulating the signal of interest by a linear chirp before random k-space under-sampling, and then reconstructing the signal with nonlinear algorithms that promote sparsity. The effectiveness of the procedure is theoretically underpinned by the optimization of the coherence between the sparsity and sensing bases. The proposed technique is thoroughly studied by means of numerical simulations, as well as phantom and in vivo experiments on a 7T scanner. Our results suggest that s(2)MRI performs better than state-of-the-art variable density k-space under-sampling approaches.     

Feature-directed spiral coding of magnetic resonance images

The authors investigate the encoding of magnetic resonance (MR) images of the human body using various lossless techniques, and presents a new form of spiral encoding. The algorithm used relies partially on the overall shape of the bounding contour of the image in achieving the compression and uses a traditional run-based technique combined with an adaptive Huffman coder to encode the complete image. Comparisons are made between the feature-directed spiral encoding and the traditional paths; the latter include the scanning pattern associated with the normal raster scanned display and the path for a display that could be used in following a linearised quadtree encoding. The new method tracks the `greater' contour of the overall image and, once the path has been established and tuples recorded, the inner contours are automatically generated. The process is repeated for each of the inner contours with a reducing radius towards the centre. The results are given for the various techniques in terms of compression ratios. The new spiralling method achieves an approximate 5.29% saving over the traditional techniques and also gives structure to the compressed image     

Adaptive fuzzy segmentation of magnetic resonance images

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.     

Three-dimensional integral imaging display system based on negative lens array

An integral imaging display system based on negative lens array in real mode is proposed in this letter. Compared with the conventional integral imaging system with positive lens array, the negative one has a huge advantage on viewing angle. The minimum viewing angle of the imaging display system based on negative lens array is the maximum viewing angle of the positive one with the same parameters. And the imaging display system based on negative lens array can enhance the viewing angle to 180° with special parameters. Other parameters, such as resolution and depth-of-field, are the same in both systems. Another advantage is that the proposed imaging display system based on negative lens array can fill the image area gap between 0 and 2f. The feasibility of our proposed method is experimentally proved. This work has been supported by the National Natural Science Foundation of China (No.11474169) and the Hebei Provincial Science Foundation for Youths (No.F2016402115). E-mail:XXJiao@hebeu.edu.cn      

Homodyne detection in magnetic resonance imaging

Magnetic detection of complex images in magnetic resonance imaging (MRI) is immune to the effects of incidental phase variations, although in some applications information is lost or images are degraded. It is suggested that synchronous detection or demodulation can be used in MRI systems in place of magnitude detection to provide complete suppression of undesired quadrature components, to preserve polarity and phase information, and to eliminate the biases and reduction in signal-to-noise ratio (SNR) and contrast in low SNR images. The incidental phase variations in an image are removed through the use of a homodyne demodulation reference, which is derived from the image or the object itself. Synchronous homodyne detection has been applied to the detection of low SNR images, the reconstruction of partial k-space images, the simultaneous detection of water and lipid signals in quadrature, and the preservation of polarity in inversion-recovery images.     

Topology-preserving tissue classification of magnetic resonance brain images

This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations.     

Reconstruction of magnetic resonance images using one-dimensional techniques

Whenever DFT (discrete Fourier transform) processing of a multidimensional discrete signal is required, one can apply either a multidimensional FFT (fast Fourier transform) algorithm, or a single-dimension FFT algorithm, both using the same number of points. That is, the dimensions of a "multidimensional" signal, and of its spectrum, are a matter of choice. Every multidimensional sequence is completely equivalent to a one-dimensional function in both "time" and "frequency" domains. This statement applied to MRI (magnetic resonance imaging) explains why one can reconstruct the slice by using either one-dimensional or two-dimensional methods, as it is already done in echo planar methods. In the commonly used spin warp methods, the image can be also reconstructed by either one- or two-dimensional processing. However, some artifacts in the images reconstructed from the original "zig-zag" echo planar trajectory, are shown to be due to the wrong dimensionality of the FFT applied.     

Improved model-based magnetic resonance spectroscopic imaging

Model-based techniques have the potential to reduce the artifacts and improve resolution in magnetic resonance spectroscopic imaging, without sacrificing the signal-to-noise ratio. However, the current approaches have a few drawbacks that limit their performance in practical applications. Specifically, the classical schemes use less flexible image models that lead to model misfit, thus resulting in artifacts. Moreover, the performance of the current approaches is negatively affected by the magnetic field inhomogeneity and spatial mismatch between the anatomical references and spectroscopic imaging data. In this paper, we propose efficient solutions to overcome these problems. We introduce a more flexible image model that represents the signal as a linear combination of compartmental and local basis functions. The former set represents the signal variations within the compartments, while the latter captures the local perturbations resulting from lesions or segmentation errors. Since the combined set is redundant, we obtain the reconstructions using sparsity penalized optimization. To compensate for the artifacts resulting from field inhomogeneity, we estimate the field map using alternate scans and use it in the reconstruction. We model the spatial mismatch as an affine transformation, whose parameters are estimated from the spectroscopy data.