APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

PurposeTo improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC).MethodsAPIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment.ResultsCompared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm³ isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results.ConclusionCompared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.     

Robust GRAPPA reconstruction using sparse multi-kernel learning with least squares support vector regression

Accuracy of interpolation coefficients fitting to the auto-calibrating signal data is crucial for k-space-based parallel reconstruction. Both conventional generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction that utilizes linear interpolation function and nonlinear GRAPPA (NLGRAPPA) reconstruction with polynomial kernel function are sensitive to interpolation window and often cannot consistently produce good results for overall acceleration factors. In this study, sparse multi-kernel learning is conducted within the framework of least squares support vector regression to fit interpolation coefficients as well as to reconstruct images robustly under different subsampling patterns and coil datasets. The kernel combination weights and interpolation coefficients are adaptively determined by efficient semi-infinite linear programming techniques. Experimental results on phantom and in vivo data indicate that the proposed method can automatically achieve an optimized compromise between noise suppression and residual artifacts for various sampling schemes. Compared with NLGRAPPA, our method is significantly less sensitive to the interpolation window and kernel parameters.     

O-space with high resolution readouts outperforms radial imaging

PurposeWhile O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts.Theory and methodsA sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging.ResultsExperimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image.ConclusionsHigh resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.     

Fast flair imaging of the brain using the fast spin-echo and gradient spin-echo technique

The purpose of this study was to compare the gradient spin-echo (GRASE) to the fast spin-echo (FSE) implementation of fast fluid-attenuated inversion recovery (FLAIR) sequences for brain imaging. Thirty patients with high signal intensity lesions on T₂-weighted images were examined on a 1.5 T MR system. Scan time-minimized thin-section FLAIR-FSE and FLAIR-GRASE sequences were obtained and compared side by side. Image assessment criteria were lesion conspicuity, contrast between different types of normal tissue, image quality, and artifacts. In addition, contrast ratios and contrast-to-noise ratios were determined. Compared to FSE, the GRASE technique allowed a 17% reduction in scan time but conspicuity of small lesions in particular was significantly lower on FLAIR-GRASE images because of higher image noise and increased artifacts. Gray-white differentiation was slightly worse on FLAIR-GRASE. Physiological ferritin deposition appeared slightly darker on FLAIR-GRASE images and susceptibility artifacts were stronger. Fatty tissue was less bright with FLAIR-GRASE. With current standard hardware equipment, the GRASE technique is not an adequate alternative to FSE for the implementation of fast FLAIR sequences in routine clinical MR brain imaging.     

De-aliasing for signal restoration in Propeller MR imaging

PurposeObjects falling outside of the true elliptical field-of-view (FOV) in Propeller imaging show unique aliasing artifacts. This study proposes a de-aliasing approach to restore the signal intensities in Propeller images without extra data acquisition.Materials and methodsComputer simulation was performed on the Shepp-Logan head phantom deliberately placed obliquely to examine the signal aliasing. In addition, phantom and human imaging experiments were performed using Propeller imaging with various readouts on a 3.0 Tesla MR scanner. De-aliasing using the proposed method was then performed, with the first low-resolution single-blade image used to find out the aliasing patterns in all the single-blade images, followed by standard Propeller reconstruction. The Propeller images without and with de-aliasing were compared.ResultsComputer simulations showed signal loss at the image corners along with aliasing artifacts distributed along directions corresponding to the rotational blades, consistent with clinical observations. The proposed de-aliasing operation successfully restored the correct images in both phantom and human experiments.ConclusionThe de-aliasing operation is an effective adjunct to Propeller MR image reconstruction for retrospective restoration of aliased signals.     

Lesion load quantification on fast-FLAIR, rapid acquisition relaxation-enhanced, and gradient spin echo brain MRI scans from multiple sclerosis patients.

Previous studies have addressed the issue of the usefullness of fast fluid-attenuated (fast-FLAIR), rapid acquisition relaxation-enhanced (RARE), and gradient spin echo (GRASE) sequences in small groups of patients with multiple sclerosis (MS). The aim of this study was to assess and compare the lesion volumes and the intra-rater reproducibility of such measurements using fast-FLAIR, dual echo RARE, and dual echo GRASE brain scans from a large sample of MS patients. Using a 1.5 Tesla scanner, fast-FLAIR, dual echo RARE, and dual echo GRASE scans (24 axial, 5-mm thick contiguous interleaved slices) of the brain were obtained from 50 MS patients. Total lesion loads (TLL) were assessed twice using a semi-automated local thresholding segmentation technique by the same rater from the scans obtained with the three techniques. Mean TLL were 20.3 mL for fast-FLAIR, 16.6 mL for RARE, and 17.6 mL for GRASE sequences. Mean TLL detected by the three techniques were significantly heterogeneous (p < 0.001); at post-hoc analysis, the mean lesion volume detected on fast-FLAIR images was significantly higher than that on both RARE and GRASE images (p < 0.001) and the mean TLL on GRASE scans was significantly higher than that on RARE scans (p = 0.001). The mean values of intra-observer coefficient of variation for TLL measurements were similar for the three techniques (2.69% for fast-FLAIR, 2.33% for RARE, and 2.65% for GRASE). Our results confirm that fast-FLAIR sequences detect higher lesion volumes than those detected by other magnetic resonance imaging (MRI) sequences with shorter acquisition times. However, the reproducibility of TLL measurements is comparable among fast-FLAIR, RARE, and GRASE. This suggests that when assessing MS disease burden with MRI, the choice of the pulse sequence to be used should be dictated by the clinical setting.     

A simple application of compressed sensing to further accelerate partially parallel imaging

Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.     

Magnetic Resonance Fingerprinting with short relaxation intervals

PurposeThe aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions.Material and methodsA calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T₁ and T₂ values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated.ResultsThe proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T₁ values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T₂ values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps.ConclusionsReducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially resolved MRF.     

Spiral demystified

Spiral acquisition schemes offer unique advantages such as flow compensation, efficient k-space sampling and robustness against motion that make this option a viable choice among other non-Cartesian sampling schemes. For this reason, the main applications of spiral imaging lie in dynamic magnetic resonance imaging such as cardiac imaging and functional brain imaging. However, these advantages are counterbalanced by practical difficulties that render spiral imaging quite challenging. Firstly, the design of gradient waveforms and its hardware requires specific attention. Secondly, the reconstruction of such data is no longer straightforward because k-space samples are no longer aligned on a Cartesian grid. Thirdly, to take advantage of parallel imaging techniques, the common generalized autocalibrating partially parallel acquisitions (GRAPPA) or sensitivity encoding (SENSE) algorithms need to be extended. Finally, and most notably, spiral images are prone to particular artifacts such as blurring due to gradient deviations and off-resonance effects caused by B₀ inhomogeneity and concomitant gradient fields. In this article, various difficulties that spiral imaging brings along, and the solutions, which have been developed and proposed in literature, will be reviewed in detail.     

Field camera versus phantom-based measurement of the gradient system transfer function (GSTF) with dwell time compensation

PurposeThe gradient system transfer function (GSTF) can be used to describe the dynamic gradient system and applied for trajectory correction in non-Cartesian MRI. This study compares the field camera and the phantom-based methods to measure the GSTF and implements a compensation for the difference in measurement dwell time.MethodsThe self-term GSTFs of a MR system were determined with two approaches: 1) using a dynamic field camera and 2) using a spherical phantom-based measurement with standard MR hardware. The phantom-based GSTF was convolved with a box function to compensate for the dwell time dependence of the measurement. The field camera and phantom-based GSTFs were used for trajectory prediction during retrospective image reconstruction of 3D wave-CAIPI phantom images.ResultsDifferences in the GSTF magnitude response were observed between the two measurement methods. For the wave-CAIPI sequence, this led to deviations in the GSTF predicted trajectories of 4% compared to measured trajectories, and residual distortions in the reconstructed phantom images generated with the phantom-based GSTF. Following dwell-time compensation, deviations in the GSTF magnitudes, GSTF-predicted trajectories, and resulting image artifacts were eliminated (< 0.5% deviation in trajectories).ConclusionWith dwell time compensation, both the field camera and the phantom-based GSTF self-terms show negligible deviations and lead to strong artifact reduction when they are used for trajectory correction in image reconstruction.     

Flow compensation for the fast spin echo triple-echo Dixon sequence

The fast spin echo (FSE) triple-echo Dixon (FTED) sequence uses bipolar triple-echo readout during each echo-spacing period of FSE to collect all the images necessary for Dixon water and fat separation in a single scan. In comparison to other FSE implementations of the Dixon technique, the triple echo readout used in FTED incurs minimal deadtime in the pulse sequence design and thus greatly enhances the overall scan efficiency. A potential drawback of FTED is that the time dependence of the gradient moment along the frequency encode direction becomes more complicated than in FSE and flow compensation based on the gradient moment (GM) nulling is difficult to achieve. In this work, the first order GM along the frequency encode direction of FTED was examined and two different methods to minimize the GM were proposed. The first method nulls the GM at all the locations of the refocusing radiofrequency pulses so that the Carr-Purcell Meiboom-Gill condition is always maintained. The second method minimizes the GM of the spin echo component of the FSE signal at the echo locations. The efficacy of both methods in reducing the first order GM and flow-related artefacts was demonstrated both in phantom and in images in vivo.     

Virtual non-contrast enhanced magnetic resonance imaging (VNC-MRI)

PurposeApplication of contrast agents (CA) is widely used in various clinical fields like oncology. Similar to approaches used in computed tomography, virtual non-contrast enhanced (VNC) images can be generated with the goal to supersede true non-contrast enhanced (TNC) images.MethodsIn MRI a T1-mapping sequence with variable flip angle (VFA) was used to acquire two images with different image contrast at the same time. To generate VNC images postprocessing based on this technique, an image-space based material decomposition algorithm was used. The inverse of a sensitivity matrix, consisting of intensity values for both VFA images and every material respectively, was used to determine the three material fractions and to calculate the final VNC images. The technique was tested on a 3 T scanner using a phantom and two in-vivo scans of patients with glioma and glioblastoma respectively. In all these cases the required six values were manually derived from the respective material or the background from both VFA images.ResultsPostprocessing results of the phantom show that the chosen materials can be separated and visualized individually and unwanted materials can be suppressed. In the VNC images of in-vivo scans the signal of the CA is removed successfully.ConclusionIt was shown that VNC images that match the visual impression of the TNC images can be generated, resulting in possibly reduced scan times and avoided mismatches due to movement of the patient.     

Singular Value Decomposition Using Jacobi Algorithm in pMRI and CS

Parallel magnetic resonance imaging (pMRI) and compressed sensing (CS) have been recently used to accelerate data acquisition process in MRI. Matrix inversion (for rectangular matrices) is required to reconstruct images from the acquired under-sampled data in various pMRI algorithms (e.g., SENSE, GRAPPA) and CS. Singular value decomposition (SVD) provides a mechanism to accurately estimate pseudo-inverse of a rectangular matrix. This work proposes the use of Jacobi SVD algorithm to reconstruct MR images from the acquired under-sampled data both in pMRI and in CS. The use of Jacobi SVD algorithm is proposed in advance MRI reconstruction algorithms, including SENSE, GRAPPA, and low-rank matrix estimation in L + S model for matrix inversion and estimation of singular values. Experiments are performed on 1.5T human head MRI data and 3T cardiac perfusion MRI data for different acceleration factors. The reconstructed images are analyzed using artifact power and central line profiles. The results show that the Jacobi SVD algorithm successfully reconstructs the images in SENSE, GRAPPA, and L + S algorithms. The benefit of using Jacobi SVD algorithm for MRI image reconstruction is its suitability for parallel computation on GPUs, which may be a great help in reducing the image reconstruction time.     

一种结合并行成像和压缩感知的快速磁共振成像新方法

压缩感知（CS）技术和并行成像技术（主要是SENSE技术、GRAPPA技术等）都能通过减少k空间数据的采集量来加快磁共振成像速度,目前已有一些将两种方法相结合进一步加速磁共振成像速度的方法（例如CS-GRAPPA）.本文针对数据采集和重建这两方面对现有CS-GRAPPA方法进行了改进,采集方式上采用了局部等间隔采集模板以满足GRAPPA重建的要求,并对采集模板进行随机放置以满足CS重建的要求;数据重建时,根据自动校正数据估算GRAPPA算法中欠采行的重建误差,并利用误差的大小确定在CS算法中保真的程度.不同磁共振图像重建实验的结果表明：与现有方法相比,本文方法能够更好地保留原有图像细节并有效减少伪影.     

颅脑梯度回波成像:呼吸伪影和导航回波矫正

梯度回波序列是磁共振成像中常用的脉冲序列,然而梯度回波对主磁场波动非常敏感,呼吸等生理运动引起的信号波动会导致图像伪影.该文报道了采用导航回波技术获取呼吸运动导致的局部磁场波动,用以矫正图像回波中随时间变化的相位波动,并将该技术应用于三维多回波梯度回波成像和T2*定量图研究.研究结果显示:矫正前,相位波动幅度随回波时间增长而增大,模图和T2*定量图在相位编码方向有明显伪影,并且男女呼吸伪影水平有显著性差异;矫正后,相位波动幅度大幅下降,图像伪影水平有显著性下降.     

Black-blood T2* mapping with delay alternating with nutation for tailored excitation

PurposeTo develop a black-blood T2* mapping method using a Delay Alternating with Nutation for Tailored Excitation (DANTE) preparation combined with a multi-echo gradient echo (GRE) readout (DANTE-GRE).Materials and methodsSimulations of the Bloch equation for DANTE-GRE were performed to optimize sequence parameters. After optimization, the sequence was applied to a phantom scan and to neck and lower extremity scans conducted on 12 volunteers at 3 T using DANTE-GRE, Motion-Sensitized Driven Equilibrium (MSDE)-GRE, and multi-echo GRE. T2* values were measured using an offset model. Statistical analyses were conducted to compare the T2* values between the three sequences.ResultsSimulation results showed that blood suppression can be achieved with various DANTE parameter adjustments. T2* maps acquired by DANTE-GRE were consistent and comparable to those acquired with multi-echo GRE in phantom experiments. In the in vivo experiments, DANTE-GRE was more comparable to multi-echo GRE than MSDE-GRE regarding the measurement of muscle T2* values.ConclusionDue to its high signal intensity retention and effective blood signal suppression, DANTE-GRE allows for robust and accurate T2* quantification, superior to that of MSDE-GRE, while overcoming blood flow artifacts associated with traditional multi-echo GRE.     

Noise estimation in parallel MRI: GRAPPA and SENSE

Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. SENSE and GRAPPA are the most popular reconstruction methods proposed in order to suppress the artifacts created by this subsampling. The reconstruction process carried out by both methods yields to a variance of noise value which is dependent on the position within the final image. Hence, the traditional noise estimation methods – based on a single noise level for the whole image – fail. In this paper we propose a novel methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. In both cases, some additional information must be known beforehand: the sensitivity maps of each receiver coil in the SENSE case and the reconstruction coefficients for GRAPPA.     

Fast triple-spin-echo Dixon (FTSED) sequence for water and fat imaging

A number of ‘Dixon’ techniques based on fast spin echo (FSE) sequence have been proposed and successfully used in many branches of medicine. Some require only one scan, but most of them need multiple scans and long scan times. This article describes a new fast triple-spin-echo Dixon (FTSED) technique suitable for ultra-high field MRI, in which three specific time shifts are introduced in the echo train; thus, three images with defined water-fat phase-differences (0, π, 2π) are encoded in the phase of the acquired images without extreme restrictions upon the echo duration. The water and fat images are then calculated by iterative least-squares estimation method. The sequence was successfully implemented at a 9.4 T ultra-high field MRI system and tested on a phantom and a rat.     

Solving the problem of concomitant gradients in ultra-low-field MRI

In ultra-low-field magnetic resonance imaging (ULF MRI), spin precession is detected typically in magnetic fields of the order of 10-100 μT. As in conventional high-field MRI, the spatial origin of the signals can be encoded by superposing gradient fields on a homogeneous main field. However, because the main field is weak, gradient field amplitudes become comparable to it. In this case, the concomitant gradients forced by Maxwell's equations cause the assumption of linearly varying field gradients to fail. Thus, image reconstruction with Fourier transformation would produce severe image artifacts. We propose a direct linear inversion (DLI) method to reconstruct images without limiting assumptions about the gradient fields. We compare the quality of the images obtained using the proposed reconstruction method and the Fourier reconstruction. With simulations, we show how the reconstruction errors of the methods depend on the strengths of the concomitant gradients. The proposed approach produces nearly distortion-free images even when the main field reaches zero.     

Effects of static and radiofrequency magnetic field inhomogeneity in ultra-high field magnetic resonance imaging

To characterize the severe static (B(0)) and radiofrequency (B(1)) magnetic field inhomogeneity in ultra-high field (> or =7 T) magnetic resonance imaging, gradient echo (GE) and spin echo (SE) images of in vivo and postmortem human brains were acquired. The B(0) and B(1) inhomogeneity were experimentally mapped and/or numerically simulated, and correlated with the image artifacts. Whereas B(0) inhomogeneity affects predominantly GE images near air/tissue interfaces, B(1) inhomogeneity affects SE images more severely and shows non-intuitive patterns. Mapping of the B(0) and B(1) inhomogeneity is important in characterizing image artifacts. This will help develop better B(0) and B(1) inhomogeneity correction methods.