Spline-based cardiac motion tracking using velocity-encoded magnetic resonance imaging

This paper deals with the problem of tracking cardiac motion and deformation using velocity-encoded magnetic resonance imaging. We expand upon an earlier described method and fit a spatiotemporal motion model to measured velocity data. We investigate several different spatial elements both qualitatively and quantitatively using phantom measurements and data from human subjects. In addition, we also use optical flow estimation by the Horn–Schunk method as complementary data in regions where the velocity measurements are noisy. Our results show that it is possible to obtain good motion tracking accuracy in phantoms with relatively few spatial elements, if the type of element is properly chosen. The use of optical flow can correct some measurement artifacts but may give an underestimation of the magnitude of the deformation. In human subjects the different spatial elements perform quantitatively in a similar way but qualitative differences exists, as shown by a semiquantitative visual scoring of the different methods.      

Brain tissue classification of magnetic resonance images using partial volume modeling

This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.     

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.     

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     

Volumetric object reconstruction using the 3D-MRF model-basedsegmentation [magnetic resonance imaging]

A number of segmentation algorithms have been developed, but those algorithms are not effective on volume reconstruction because they are limited to operating only on two-dimensional (2-D) images. Here, the authors propose the volumetric object reconstruction method using the three-dimensional Markov random field (3D-MRF) model-based segmentation. The 3D-MRF model is known to be one of the most efficient ways to model spatial contextual information. The method is compared with the 2-D region growing scheme under three types of interpolation. The results show that the proposed method is better in terms of image quality than the other methods     

Adaptive denoising of event-related functional magnetic resonance imaging data using spectral subtraction

A new adaptive signal-preserving technique for noise suppression in event-related functional magnetic resonance imaging (fMRI) data is proposed based on spectral subtraction. The proposed technique estimates a parametric model for the power spectrum of random noise from the acquired data based on the characteristics of the Rician statistical model. This model is subsequently used to estimate a noise-suppressed power spectrum for any given pixel time course by simple subtraction of power spectra. The new technique is tested using computer simulations and real data from event-related fMRI experiments. The results show the potential of the new technique in suppressing noise while preserving the other deterministic components in the signal. Moreover, we demonstrate that further analysis using principal component analysis and independent component analysis shows a significant improvement in both convergence and clarity of results when the new technique is used. Given its simple form, the new method does not change the statistical characteristics of the signal or cause correlated noise to be present in the processed signal. This suggests the value of the new technique as a useful preprocessing step for fMRI data analysis.     

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.     

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.     

Seeing speech: capturing vocal tract shaping using real-time magnetic resonance imaging

In this paper real-time (RT) magnetic resonance imaging (MRI) is used to study speech production especially capturing vocal tract shaping.     

Computation of diffusion function measures in q-space using magnetic resonance hybrid diffusion imaging

The distribution of water diffusion in biological tissues may be estimated by a 3-D Fourier transform (FT) of diffusion-weighted measurements in q-space. In this study, methods for estimating diffusion spectrum measures (the zero-displacement probability, the mean-squared displacement, and the orientation distribution function) directly from the q-space signals are described. These methods were evaluated using both computer simulations and hybrid diffusion imaging (HYDI) measurements on a human brain. The HYDI method obtains diffusion-weighted measurements on concentric spheres in q-space. Monte Carlo computer simulations were performed to investigate effects of noise, q-space truncation, and sampling interval on the measures. This new direct computation approach reduces HYDI data processing time and image artifacts arising from 3-D FT and regridding interpolation. In addition, it is less sensitive to the noise and q-space truncation effects than conventional approach. Although this study focused on data using the HYDI scheme, this computation approach may be applied to other diffusion sampling schemes including Cartesian diffusion spectrum imaging.     

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.     

Correlative averaging for radial magnetic resonance imaging

Although Magnetic Resonance Imaging (MRI) has faced a dramatic increase in real-time capabilities over the last year, acceptable image quality still limits the actually achievable acquisition speed. This paper presents a motion-compensated noise filter that, on the basis of hierarchical motion estimation and edge-preserving adaptive weighted averaging, has been integrated into a segmented radial MR acquisition scheme. In several studies of moving joints, the proposed approach led to significant reductions in the noise level without introducing motion blur. The improved image quality would, in principle, allow more than double the acquisition speed, retaining the original image quality.     

Measuring nonconstant flow in magnetic resonance imaging

An attempt is made to quantify rapid flow using magnetic resonance imaging techniques. An analysis is presented in which it is assumed that constant velocity gradients are present. A deconvolution scheme which can remove the blurring from motion with acceleration is developed. This allows improved resolution and velocity determination. Computer experiments were performed on simulated data, where the velocity drops from 100 cm/s to 50 cm/s over a distance of 5 cm. In noise-free data, velocities were recovered to within 2% of the lower velocity and, for data with 5% white noise, to within 6%     

An efficient method for dynamic magnetic resonance imaging

Many magnetic resonance imaging applications require the acquisition of a time series of images. In conventional Fourier transform based imaging methods, each of these images is acquired independently so that the temporal resolution possible is limited by the number of spatial encodings (or data points in the Fourier space) collected, or one has to sacrifice spatial resolution for temporal resolution. Here, a generalized series based imaging technique is proposed to address this problem. This technique makes use of the fact that, in most time-sequential imaging problems, the high-resolution image morphology does not change from one image to another, and it improves imaging efficiency (and temporal resolution) over the conventional Fourier imaging methods by eliminating the repeated encodings of this stationary information. Additional advantages of the proposed imaging technique include a reduced number of radio frequency (RF) pulses for data collection, and thus lower RF power deposition. This method should prove useful for a variety of dynamic imaging applications, including dynamic studies of contrast agents and functional brain imaging.     

Iterative RF pulse refinement for magnetic resonance imaging

Selective RF pulses are needed for many applications in magnetic resonance imaging (MRI). The waveform required to produce a desired excitation profile is, to first-order, its Fourier transform. This approximation is most valid for small tip angles and the quality and accuracy of such excitations decreases with increasing tip angle. Since large-tip-angle excitations are required in most types of imaging, a better synthesis technique is necessary. While a variety of analytical and numerical synthesis techniques based on solution of the Bloch equations are available, these techniques fail to consider the effect of the physical scanner hardware and are often accompanied by computational complexity. We present a technique for selective RF pulse refinement which uses real-time feedback techniques in lieu of a solution to the Bloch equations. Physical experiments are conducted to demonstrate the effectiveness of this algorithm and an extension to pulses of 90 degrees is investigated.     

Wavelet-based Rician noise removal for magnetic resonance imaging

It is well known that magnetic resonance magnitude image data obey a Rician distribution. Unlike additive Gaussian noise, Rician "noise" is signal-dependent, and separating signal from noise is a difficult task. Rician noise is especially problematic in low signal-to-noise ratio (SNR) regimes where it not only causes random fluctuations, but also introduces a signal-dependent bias to the data that reduces image contrast. This paper studies wavelet-domain filtering methods for Rician noise removal. We present a novel wavelet-domain filter that adapts to variations in both the signal and the noise.     

Realistic analytical phantoms for parallel magnetic resonance imaging

The quantitative validation of reconstruction algorithms requires reliable data. Rasterized simulations are popular but they are tainted by an aliasing component that impacts the assessment of the performance of reconstruction. We introduce analytical simulation tools that are suited to parallel magnetic resonance imaging and allow one to build realistic phantoms. The proposed phantoms are composed of ellipses and regions with piecewise-polynomial boundaries, including spline contours, Bézier contours, and polygons. In addition, they take the channel sensitivity into account, for which we investigate two possible models. Our analytical formulations provide well-defined data in both the spatial and k-space domains. Our main contribution is the closed-form determination of the Fourier transforms that are involved. Experiments validate the proposed implementation. In a typical parallel magnetic resonance imaging reconstruction experiment, we quantify the bias in the overly optimistic results obtained with rasterized simulations-the inverse-crime situation. We provide a package that implements the different simulations and provide tools to guide the design of realistic phantoms.     

Rapid polarizing field cycling in magnetic resonance imaging

We describe the electronics for controlling the independently pulsed polarizing coil in a prepolarized magnetic resonance imaging (PMRI) system and demonstrate performance with free induction decay measurements and in vivo imaging experiments. A PMRI scanner retains all the benefits of acquiring MRI data at low field, but with the higher signal of the polarizing field. Rapidly and efficiently ramping the polarizing coil without disturbing the data acquisition is one of the major challenges of PMRI. With our modular hardware design, we successfully ramp the 0.4-T polarizing coil of a wrist-sized PMRI scanner at up to 100 T/s without causing image artifacts or otherwise degrading data acquisition.