Hisamoto Moriguchi

Iterative Next-Neighbor Regridding (INNG): improved reconstruction from nonuniformly sampled k-space data using rescaled matrices.

The reconstruction of MR images from nonrectilinearly sampled data is complicated by the fact that the inverse 2D Fourier transform (FT) cannot be performed directly on the acquired k‐space data set. k‐Space gridding is commonly used because it is an efficient reconstruction method. However, conventional gridding requires optimized density compensation functions (DCFs) to avoid profile distortions. Oftentimes, the calculation of optimized DCFs presents an additional challenge in obtaining an accurately gridded reconstruction. Another type of gridding algorithm, the block uniform resampling (BURS) algorithm, often requires singular value decomposition (SVD) regularization to avoid amplification of data imperfections, and under some conditions it is difficult to adjust the regularization parameters. In this work, new reconstruction algorithms for nonuniformly sampled k‐space data are presented. In the newly proposed algorithms, high‐quality reconstructed images are obtained from an iterative reconstruction that is performed using matrices scaled to sizes greater than that of the target image matrix. A second version partitions the sampled k‐space region into several blocks to avoid limitations that could result from performing multiple 2D‐FFTs on large data matrices. The newly proposed algorithms are a simple alternative approach to previously proposed optimized gridding algorithms. Magn Reson Med 51:343–352, 2004.

Bunched phase encoding (BPE): A new fast data acquisition method in MRI

A new fast data acquisition method, “Bunched Phase Encoding” (BPE), is presented. In conventional rectilinear data acquisition, only a readout gradient (and no phase encoding gradient) is applied when k‐space data are acquired. Reduction of the number of phase encoding lines by increasing the phase encoding step size often leads to aliasing artifacts. Papouliss generalized sampling theory asserts that in some cases aliasing artifact‐free signals can be reconstructed even if the Nyquist criterion is violated in some regions of the Fourier domain. In this study, Papouliss theoretical construct is exploited to reduce the number of acquired phase encoding lines. To achieve this, k‐space data are sampled along a “zigzag” trajectory during each readout; samples are acquired at a sampling frequency higher than that of the normal rectilinear acquisition. The total number of TR cycles and, hence, the total scan time can be reduced. The resultant signal‐to‐noise ratio (SNR) often varies across the reconstructed image when using the BPE technique, and the image SNR depends on the reconstruction method. This work is comparable to a gradient based version of parallel imaging. Evidence suggests it may serve as the basis for new opportunities for fast data acquisition in MRI. Magn Reson Med, 2006.

Dixon Techniques in Spiral Trajectories With Off-Resonance Correction: A New Approach for Fat Signal Suppression Without Spatial-Spectral RF Pulses

Spiral imaging has recently gained acceptance in MR applications requiring rapid data acquisition. One of the main disadvantages of spiral imaging, however, is blurring artifacts that result from off‐resonance effects. Spatial‐spectral (SPSP) pulses are commonly used to suppress those spins that are chemically shifted from water and lead to off‐resonance artifacts. However, SPSP pulses may produce nonuniform fat signal suppression or unwanted water signal suppression when applied in the presence of B0 field inhomogeneities. Dixon techniques have been developed as methods for water–fat signal decomposition in rectilinear sampling schemes since they can produce unequivocal water–fat signal decomposition even in the presence of B0 inhomogeneities. This article demonstrates that three‐point and two‐point Dixon techniques can be extended to conventional spiral and variable‐density spiral data acquisitions for unambiguous water–fat decomposition with off‐resonance blurring correction. In the spiral three‐point Dixon technique, water–fat signal decomposition and image deblurring are performed based on the frequency maps that are directly derived from the acquired images. In the spiral two‐point Dixon technique, several predetermined frequencies are tested to create a frequency map. The newly proposed techniques can achieve more effective and more uniform fat signal suppression when compared to the conventional spiral acquisition method with SPSP pulses. Magn Reson Med 50: 915–924, 2003.

Zigzag sampling for improved parallel imaging.

Conventional Cartesian parallel MRI methods are limited to the sensitivity variations provided by the underlying receiver coil array in the dimension in which the data reduction is carried out, namely, the phase‐encoding directions. However, in this work an acquisition strategy is presented that takes advantage of sensitivity variations in the readout direction, thus improving the parallel imaging reconstruction process. This is achieved by employing rapidly oscillating phase‐encoding gradients during the actual readout. The benefit of this approach is demonstrated in vivo using various zigzag‐shaped gradient trajectory designs. It is shown that zigzag type sampling, in analogy to CAIPIRINHA, modifies the appearance of aliasing in 2D and 3D imaging, thereby utilizing additional sensitivity variations in the readout direction directly resulting in improved parallel imaging reconstruction performance. Magn Reson Med 60:474–478, 2008.

Block regional off-resonance correction (BRORC): a fast and effective deblurring method for spiral imaging.

One primary disadvantage of spiral imaging is blurring artifact due to off‐resonance effects. The conventional frequency segmented off‐resonance correction method that is performed over the entire image is computationally intense due to the large number of fast Fourier transforms (FFTs) required. Here, a new fast off‐resonance correction method, block regional off‐resonance correction (BRORC), is presented. In this method, off‐resonance correction proceeds block‐by‐block through the reconstructed image with FFTs performed on matrices that are smaller than the full image matrix. The BRORC algorithm is typically several times more computationally efficient than the conventional off‐resonance correction algorithm. Additional computational reductions can be expected for the BRORC if only specific image regions require deblurring. The newly proposed off‐resonance correction method offers significant speed advantages and equivalent image quality when compared to conventional off‐resonance correction methods. Magn Reson Med 50:643–648, 2003.

Using the GRAPPA operator and the generalized sampling theorem to reconstruct undersampled non-Cartesian data

As expected from the generalized sampling theorem of Papoulis, the use of a bunched sampling acquisition scheme in conjunction with a conjugate gradient (CG) reconstruction algorithm can decrease scan time by reducing the number of phase‐encoding lines needed to generate an unaliased image at a given resolution. However, the acquisition of such bunched data requires both modified pulse sequences and high gradient performance. A novel method of generating the “bunched” data using self‐calibrating GRAPPA operator gridding (GROG), a parallel imaging method that shifts data points by small distances in k‐space (with Δk usually less than 1.0, depending on the receiver coil) using the GRAPPA operator, is presented here. With the CG reconstruction method, these additional “bunched” points can then be used to reconstruct an image with reduced artifacts from undersampled data. This method is referred to as GROG‐facilitated bunched phase encoding (BPE), or GROG‐BPE. To better understand how the patterns of bunched points, maximal blip size, and number of bunched points affect the reconstruction quality, a number of simulations were performed using the GROG‐BPE approach. Finally, to demonstrate that this method can be combined with a variety of trajectories, examples of images with reduced artifacts reconstructed from undersampled in vivo radial, spiral, and rosette data are shown. Magn Reson Med, 2009.

Keyhole Dixon method for faster, perceptually equivalent fat suppression

To reduce the acquisition time associated with the two‐point Dixon fat suppression technique by combining a keyhole in‐phase (Water + Fat) k‐space data set with a full out‐of‐phase (Water – Fat) k‐space data set and optimizing the keyhole size with a perceptual difference model.

Fast Spiral two‐point Dixon technique using block regional off‐resonance correction

The Spiral two‐point Dixon (Spiral 2PD) technique has recently been proposed as a method for unambiguous water–fat decomposition in spiral imaging. It also corrects for off‐resonance blurring artifacts using only two data sets. In the Spiral 2PD technique, several predetermined off‐resonance frequencies are tested to both separate water and fat signals and deblur the decomposed images. Unfortunately, the algorithm is computationally quite intensive since the range of tested frequencies must be set sufficiently large to span the full range of anticipated B0 variation over the scanned objects. The block regional off‐resonance correction (BRORC) algorithm corrects for off‐resonance blurring artifacts block by block through the reconstructed image and usually provides several times higher computational efficiency than the conventional frequency‐segmented off‐resonance correction algorithm. This work shows that both water–fat decomposition and blurring artifact correction can be performed block by block using two spiral images with different TEs and that this new technique (BRORC‐Spiral2PD technique) significantly improves the computational efficiency of other Spiral 2PD algorithms, opening new opportunities for spiral imaging. Magn Reson Med 52:1342–1350, 2004.

Optimization of noisy nonuniform sampling and image reconstruction for fast MRI using a human vision model

We are developing clinical magnetic resonance imaging (MRI) strategies using spiral acquisition techniques that sample k-space nonuniformly. These methods require a regridding process. Multiple regridding and reconstruction algorithms have been proposed, and we use a perceptual difference model (PDM) to optimize them. We acquired sixteen in vivo MR brain images and simulated reconstruction from a spiral k-space trajectory. Regridding was done by the conventional method of Jackson et al., the block uniform resampling algorithm (BURS), and a newly developed method named matrix rescaling. Each of 16 reference images was reconstructed with multiple parameter sets resulting in a total of over 800 different images. The spiral MR images were compared to the original, fully sampled image using a PDM. Of the three reconstruction methods, the conventional and high-level matrix rescaling methods produce high quality images, but the latter method executed much faster. BURS worked only in extremely low- noise instances, making it often inappropriate. We also demonstrated the effect of display parameters, such as grayscale windowing on image quality. We believe that the PDM techniques provide a promising tool for the evaluation of MR image quality that can aid the engineering design process.