Accelerating MRI by skipped phase encoding and edge deghosting (SPEED)

Improving accelerated MRI by deep learning with sparsified complex data

PURPOSE

To obtain high-quality accelerated MR images with complex-valued reconstruction from undersampled k-space data.

METHODS

The MRI scans from human subjects were retrospectively undersampled with a regular pattern using skipped phase encoding, leading to ghosts in zero-filling reconstruction. A complex difference transform along the phase-encoding direction was applied in image domain to yield sparsified complex-valued edge maps. These sparse edge maps were used to train a complex-valued U-type convolutional neural network (SCU-Net) for deghosting. A k-space inverse filtering was performed on the predicted deghosted complex edge maps from SCU-Net to obtain final complex images. The SCU-Net was compared with other algorithms including zero-filling, GRAPPA, RAKI, finite difference complex U-type convolutional neural network (FDCU-Net), and CU-Net, both qualitatively and quantitatively, using such metrics as structural similarity index, peak SNR, and normalized mean square error.

RESULTS

The SCU-Net was found to be effective in deghosting aliased edge maps even at high acceleration factors. High-quality complex images were obtained by performing an inverse filtering on deghosted edge maps. The SCU-Net compared favorably with other algorithms.

CONCLUSION

Using sparsified complex data, SCU-Net offers higher reconstruction quality for regularly undersampled k-space data. The proposed method is especially useful for phase-sensitive MRI applications.

Making Magnets More Attractive: Physics and Engineering Contributions to Patient Comfort in MRI

Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.

A kernel method for higher temporal resolution MRI using the partial separability (PS) model

The partial separability (PS) model for spatiotemporal signals has been exploited effectively for sparse (k, t)-space sampling in dynamic magnetic resonance imaging (MRI). However, the training data for defining the temporal subspace is reordered by using a projection strategy in the conventional PS model-based method, which results in a suboptimal temporal resolution imaging. To address this issue, a kernel method was presented in this work to reorder the training data to realize a higher temporal resolution MRI. Numerical simulation results show that the MRI temporal resolution could be further improved and the dynamic change of motion object could be accurately captured by the proposed method. In vivo cardiac cine MRI results demonstrate that the proposed method can reconstruct better MR images with higher temporal resolution (up to 8.4 ms per snapshot). This study may find use in ultra-high resolution dynamic MRI.

Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL

PURPOSE

k-t principle component analysis (k-t PCA) is a distinguished method for high spatiotemporal resolution dynamic MRI. To further improve the accuracy of k-t PCA, a combination with partial parallel imaging (PPI), k-t PCA/SENSE, has been tested. However, k-t PCA/SENSE suffers from long reconstruction time and limited improvement. This study aims to improve the combination of k-t PCA and PPI on both reconstruction speed and accuracy.

METHODS

A sequential combination scheme called k-t PCA GROWL (GRAPPA operator for wider readout line) was proposed. The GRAPPA operator was performed before k-t PCA to extend each readout line into a wider band, which improved the condition of the encoding matrix in the following k-t PCA reconstruction. k-t PCA GROWL was tested and compared with k-t PCA and k-t PCA/SENSE on cardiac imaging.

RESULTS

k-t PCA GROWL consistently resulted in better image quality compared with k-t PCA/SENSE at high acceleration factors for both retrospectively and prospectively undersampled cardiac imaging, with a much lower computation cost. The improvement in image quality became greater with the increase of acceleration factor.

CONCLUSION

By sequentially combining the GRAPPA operator and k-t PCA, the proposed k-t PCA GROWL method outperformed k-t PCA/SENSE in both reconstruction speed and accuracy, suggesting that k-t PCA GROWL is a better combination scheme than k-t PCA/SENSE. Magn Reson Med 77:1058-1067, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples

We introduce a method to recover a continuous domain representation of a piecewise constant two-dimensional image from few low-pass Fourier samples. Assuming the edge set of the image is localized to the zero set of a trigonometric polynomial, we show the Fourier coefficients of the partial derivatives of the image satisfy a linear annihilation relation. We present necessary and sufficient conditions for unique recovery of the image from finite low-pass Fourier samples using the annihilation relation. We also propose a practical two-stage recovery algorithm which is robust to model-mismatch and noise. In the first stage we estimate a continuous domain representation of the edge set of the image. In the second stage we perform an extrapolation in Fourier domain by a least squares two-dimensional linear prediction, which recovers the exact Fourier coefficients of the underlying image. We demonstrate our algorithm on the super-resolution recovery of MRI phantoms and real MRI data from low-pass Fourier samples, which shows benefits over standard approaches for single-image super-resolution MRI.

Accelerating PS model-based dynamic cardiac MRI using compressed sensing

High spatiotemporal resolution MRI is a challenging topic in dynamic MRI field. Partial separability (PS) model has been successfully applied to dynamic cardiac MRI by exploiting data redundancy. However, the model requires substantial preprocessing data to accurately estimate the model parameters before image reconstruction. Since compressed sensing (CS) is a potential technique to accelerate MRI by reducing the number of acquired data, the combination of PS and CS, named as Stepped-SparsePS, was introduced to accelerate the preprocessing data acquisition of PS in this work. The proposed Stepped-SparsePS method sequentially reconstructs a set of aliased dynamic images in each channel based on PS model and then the final dynamic images from the aliased images using CS. The results from numerical simulations and in vivo experiments demonstrate that Stepped-SparsePS could significantly reduce data acquisition time while preserving high spatiotemporal resolution.

Improving image quality for skipped phase encoding and edge deghosting (SPEED) by exploiting several sparsifying transforms

PURPOSE

To improve the image quality of skipped phase encoding and edge deghosting (SPEED) by exploiting several sparsifying transforms.

METHODS

The SPEED technique uses a skipped phase encoding (PE) step to accelerate MRI scan. Previously, a difference transform (DT) along PE direction is used to obtain sparse ghosted-edge maps, which were modeled by a double layer ghost model and was then deghosted by a least square error solution. In this work, it is hypothesized that enhanced sparsity, and thus improved image quality may be achievable with other sparsifying transforms, including discrete wavelet transform (DWT), discrete cosine transform (DCT), DWT combined with DT, and DCT combined with DT.

RESULTS

For images of human subjects, SPEED with DWT or DCT can yield higher image quality than DT only, especially for those images with low contrast. Reconstruction error can be further reduced if DWT or DCT are combined with DT.

CONCLUSION

Image sparsity can be enhanced with more advanced transforms, leading to higher reconstruction quality in SPEED imaging that is desirable for practical MRI applications.

Application of basic physics principles to clinical neuroradiology: differentiating artifacts from true pathology on MRI

OBJECTIVE

This article outlines artifactual findings commonly encountered in neuroradiologic MRI studies and offers clues to differentiate them from true pathology on the basis of their physical properties. Basic MR physics concepts are used to shed light on the causes of these artifacts.

CONCLUSION

MRI is one of the most commonly used techniques in neuroradiology. Unfortunately, MRI is prone to image distortion and artifacts that can be difficult to identify. Using the provided case illustrations, practical clues, and relevant physical applications, radiologists may devise algorithms to troubleshoot these artifacts.

Accelerated MRI by SPEED with generalized sampling schemes

PURPOSE

To enhance the fast imaging technique of skipped phase encoding (PE) and edge deghosting (SPEED) for more general sampling options, and thus more flexibility in implementations and applications.

METHODS

SPEED uses skipped PE steps to accelerate MRI scan. Previously, the PE skip size was chosen from prime numbers only. This restriction has been relaxed in this study to allow choice of any integers rather than merely prime numbers. Various sampling patterns were studied under all possible combinations of PE skip size and PE shifts. A criterion based on the rank values of ghost phasor matrices was introduced to evaluate SPEED reconstruction.

RESULTS

The reconstruction quality was found to correlate with the rank value of the ghost phasor matrix and the skipped PE size N. A low-rank value indicates a singular matrix that causes failure of the SPEED reconstruction. Composite numbers combined with appropriately chosen PE shifts yielded satisfactory reconstruction results.

CONCLUSION

With properly chosen PE shifts, it was found that any integers, including both prime numbers and composite numbers, could be used as PE skip size for SPEED. This finding allows much more flexible data acquisition options that may lead to more freedom in practical implementations and applications.

Parallel imaging with nonlinear reconstruction using variational penalties

A new approach based on nonlinear inversion for autocalibrated parallel imaging with arbitrary sampling patterns is presented. By extending the iteratively regularized Gauss-Newton method with variational penalties, the improved reconstruction quality obtained from joint estimation of image and coil sensitivities is combined with the superior noise suppression of total variation and total generalized variation regularization. In addition, the proposed approach can lead to enhanced removal of sampling artifacts arising from pseudorandom and radial sampling patterns. This is demonstrated for phantom and in vivo measurements.

High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging

Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter-design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high-pass GRAPPA (hp-GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp-GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA.

Simplified skipped phase encoding and edge deghosting (SPEED) for imaging sparse objects with applications to MRA

The fast imaging method named skipped phase encoding and edge deghosting (SPEED) has been demonstrated to reduce scan time considerably with typical magnetic resonance imaging data. In this work, SPEED is simplified with improved efficiency to accelerate the scan of sparse objects; we refer to this method as S-SPEED. S-SPEED partially samples k-space into two interleaved data sets, each with the same skip size of N but a different relative shift in phase encoding. The sampled data are then Fourier transformed into two ghosted images with N aliasing ghosts. Given the sparseness of signal distribution, the ghosted images are simply modeled with a single-layer structure, analogous to that used in maximum-intensity projection. With an algorithm based on a least-square-error solution, a deghosted image is solved, and a residual map is output for quality control. S-SPEED can be generalized to include more layers with additional acquisitions for refined results. Without differential filtering and full central k-space sampling, S-SPEED reduces scan time further and achieves more straightforward reconstruction, as compared with SPEED. In this work, S-SPEED is applied to accelerate magnetic resonance angiography (MRA) by taking advantage of the sparse nature of MRA data. With sparse phantom data and in vivo phase contrast MRA data, S-SPEED is demonstrated to achieve satisfactory results with an acceleration factor of 5.5 using a single coil.

Correction for geometric distortion and N/2 ghosting in EPI by phase labeling for additional coordinate encoding (PLACE)

Echo-planar imaging (EPI) is vulnerable to geometric distortion and N/2 ghosting. These artifacts can be analyzed with an intuitive k-t space tool, and here we propose a simple method for their correction. In a slightly modified additional EPI acquisition, we sample the k-t space with a shift in k(y) by adding a small area to the phase-encoding (PE) gradient. Physically, the added gradient area creates a relative phase ramp across the object and directly encodes the undistorted original y-coordinate of each voxel into a phase difference between two distorted complex images, in a method called "phase labeling for additional coordinate encoding" (PLACE). The phase information is then used to map the mismapped signals back to their original locations for geometric and intensity correction. Smoothing of expanded complex data matrix effectively reduces noise in the differential phase map and allows subpixel warping. The two acquired images can also be averaged to effectively suppress the N/2 ghost. Efficient correction for both artifacts can be achieved with three acquisitions. These acquisitions can also serve as reference scans to correct for geometric distortion and/or N/2 ghost artifacts on all images in a time series. The technique was successfully demonstrated in phantom and animal studies.

Highly accelerated MRI by skipped phase encoding and edge deghosting with array coil enhancement (SPEED-ACE)

The fast MRI method of skipped phase encoding and edge deghosting (SPEED) is further developed with array coil enhancement, and thus is termed SPEED-ACE. In SPEED-ACE, k space is sparsely sampled with skipped phase encoding at every Nth step using a set of receiver coils simultaneously, similar to SENSE, leading to sensitivity-weighted images with up to N layers of overlapping aliasing ghosts. The ghosted images are edge enhanced by a differential filter to yield ghosted edge maps, in which the ghost overlapping layers are greatly reduced since the sparseness of edges reduces the chance of ghost overlapping. Typical ghosted edge maps can be adequately modeled with a double-layer structure. By using data from at least three coils through least-square-error minimization, a deghosted edge map is obtained and inverse-filtered into a final deghosted image. In this way, SPEED-ACE partially samples k space with a skip size of N by using multiple receiver coils in parallel, and obtains a fairly good deghosted image with an undersampling factor of N. SPEED-ACE is not limited to the double-layer ghost model, but can be generalized to include more layers of ghosts for more flexible and improved performance. As a new parallel imaging method, SPEED-ACE was tested using in vivo data to demonstrate the possibility of achieving undersampling factors even greater than the number of receiver coils, which is so far not achievable by other parallel imaging methods.