Model-Based MR Image Reconstruction WITH Ccompensation FOR THROUGH-Plane Field Inhomogeneity

To improve image quality in susceptibility-weighted MR imaging, it is important to correct for the effects of field inhomogeneity. In particular, susceptibility differences between air and tissue induce magnetic field nonuniformity; often those susceptibility effects have nonzero through-plane gradients that lead to spin dephasing across the slice within each voxel. If uncorrected, these through-plane gradients cause signal loss in the reconstructed images. Several methods exist for reconstructing MR images with compensation for field inhomogeneity, but most of these methods, even the model-based iterative ones, treat the inhomogeneity within each voxel as being a constant, thus ignoring the through-plane gradient effects. This paper describes a model-based iterative method for reconstructing MR images with compensation for field inhomogeneity that accounts for the slice profile and the through-plane gradients of the field inhomogeneity (assumed to be determined by a pre-scan). In particular, this paper describes an accelerated algorithm for implementing the forward model and its adjoint as needed in a conjugate gradient algorithm for iterative MR image reconstruction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85828/1/Fessler226.pd

Iterative Image Reconstruction in MRI with Separate Magnitude and Phase Regularization

Iterative methods for image reconstruction in MRI are useful in several applications, including reconstruction from non-Cartesian k-space samples, compensation for magnetic field inhomogeneities, and imaging with multiple receive coils. Existing iterative MR image reconstruction methods are either unregularized, and therefore sensitive to noise, or have used regularization methods that smooth the complex valued image. These existing methods regularize the real and imaginary components of the image equally. In many MRI applications, including T2*-weighted imaging as used in fMRI BOLD imaging, one expects most of the signal information of interest to be contained in the magnitude of the voxel value, whereas the phase values are expected to vary smoothly spatially. This paper proposes separate regularization of the magnitude and phase components, preserving the spatial resolution of the magnitude component while strongly regularizing the phase component. This leads to a non-convex regularized least-squares cost function. We describe a new iterative algorithm that monotonically decreases this cost function. The resulting images have reduced noise relative to conventional regularization methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85802/1/Fessler194.pd

Regularized Fieldmap Estimation in MRI

In fast MR imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard fieldmap estimation methods yield noisy fieldmaps, particularly in image regions having low spin density. This paper describes regularized methods for fieldmap estimation. These methods exploit the fact that fieldmaps are smooth functions. Efficient convergent algorithms are given even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of fieldmap estimates relative to conventional unregularized methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85872/1/Fessler218.pd

Regularized B1+ MAP Estimation in MRI

A challenge in MR imaging is that RF transmit coils produce non-uniform field strengths, so an excitation pulse will produce tip angles that vary substantially from the desired tip angle over the field of view. For parallel transmit excitation (using a coil array), it is important to have a map of the B1+ field strength (and phase) for RF pulse design. Standard B1+ map estimation methods perform poorly in image regions with low spin density. This paper describes a regularized method for B1+ map estimation using MR scans for each coil and for two or more tip angles. Using these scans and exploiting the fact that maps are generally smooth, the iterative algorithm estimates both the magnitude and phase at each coil's B1+ map. Results from both simulations and real MR data show significant improvements over conventional unregularized methods for B1 + mapping.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85868/1/Fessler227.pd

Spatial Resolution Analysis of Iterative Image Ceconstruction with Separate Regularization of Real and Imaginary par

A common method of improving the conditioning in iterative image reconstruction is to include regularization in the reconstruction algorithm. One such regularization is the roughness penalty, which when used in the algorithm encourages smoother images. For complex valued images, the roughness penalty typically penalizes equally the real and imaginary parts. The desired resolution of the reconstructed image can then be evaluated using the local impulse response. A fast algorithm to calculate it was developed for the typical roughness penalty, used for matching the regularization parameter expediently to the desired resolution. For some cases its advantageous to penalize independently the real and imaginary parts. This paper proposes a fast algorithm to calculate the local impulse response for that penalty and applies it to an fMRI reconstruction problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85888/1/Fessler220.pd

Fast, Iterative, Field-Corrected Image Reconstruction for MRI

Magnetic field inhomogeneities cause distortions in the reconstructed images for non-cartesian k-space MRI (using spirals, for example). Several noniterative methods are currently used to compensate for the off-resonance during the reconstruction, but these methods rely on the assumption of a smoothly varying field map. Recently, iterative methods have been proposed that do not rely on this assumption and have the potential to estimate undistorted field maps, but suffer from prohibitively long computation times. In this abstract we present a min-max derived, time-segmented approximation to the signal equation for MRI that, when combined with the nonuniform fast Fourier transform, provides a fast, accurate field-corrected image reconstruction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86011/1/Fessler175.pd

Minimum Out-of-Slice Error SMS RF Pulse Design with Direct Peak, Power, and In-Slice Error Constraints

No abstract available

Conjugate Phase MRI Reconstruction With Spatially Variant Sample Density Correction

A new image reconstruction method to correct for the effects of magnetic field inhomogeneity in non-Cartesian sampled magnetic resonance imaging (MRI) is proposed. The conjugate phase reconstruction method, which corrects for phase accumulation due to applied gradients and magnetic field inhomogeneity, has been commonly used for this case. This can lead to incomplete correction, in part, due to the presence of gradients in the field inhomogeneity function. Based on local distortions to the k-space trajectory from these gradients, a spatially variant sample density compensation function is introduced as part of the conjugate phase reconstruction. This method was applied to both simulated and experimental spiral imaging data and shown to produce more accurate image reconstructions. Two approaches for fast implementation that allow the use of fast Fourier transforms are also described. The proposed method is shown to produce fast and accurate image reconstructions for spiral sampled MRI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85978/1/Fessler52.pd

Fast joint reconstruction of dynamic R2∗R_2^* and field maps in functional MRI.

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing T2 * -weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct R2 * maps which are quantifiable and have comparable BOLD contrast as T2* -weighted images. However, conventional R2 * mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs R2 * and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both R2 * and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for T2 * -weighted imaging the resulting R2 * maps performed comparably to T2 * -weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86002/1/Fessler23.pd

Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146889/1/mrm27461_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146889/2/mrm27461.pd