Adaptive sensitivity encoding incorporating temporal filtering (TSENSE).

A number of different methods have been demonstrated which increase the speed of MR acquisition by decreasing the number of sequential phase encodes. The UNFOLD technique is based on time interleaving of k-space lines in sequential images and exploits the property that the outer portion of the field-of-view is relatively static. The differences in spatial sensitivity of multiple receiver coils may be exploited using SENSE or SMASH techniques to eliminate the aliased component that results from undersampling k-space. In this article, an adaptive method of sensitivity encoding is presented which incorporates both spatial and temporal filtering. Temporal filtering and spatial encoding may be combined by acquiring phase encodes in an interleaved manner. In this way the aliased components are alternating phase. The SENSE formulation is not altered by the phase of the alias artifact; however, for imperfect estimates of coil sensitivities the residual artifact will have alternating phase using this approach. This is the essence of combining temporal filtering (UNFOLD) with spatial sensitivity encoding (SENSE). Any residual artifact will be temporally frequency-shifted to the band edge and thus may be further suppressed by temporal low-pass filtering. By combining both temporal and spatial filtering a high degree of alias artifact rejection may be achieved with less stringent requirements on accuracy of coil sensitivity estimates and temporal low-pass filter selectivity than would be required using each method individually. Experimental results that demonstrate the adaptive spatiotemporal filtering method (adaptive TSENSE) with acceleration factor R = 2, for real-time nonbreath-held cardiac MR imaging during exercise induced stress are presented.     

Spatially variant noise estimation in MRI: A homomorphic approach

The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods.     

Optimal design of multiple‐channel RF pulses under strict power and SAR constraints

Parallel radio frequency transmission has recently been explored as a means of tailoring the spatial response of MR excitation. In particular, parallel transmission is increasingly used to accelerate radio frequency pulses that rely on time‐varying gradient fields to achieve selectivity in multiple dimensions. The design of the underlying multiple‐channel radio frequency waveforms is mostly based on regularized least‐squares optimization in close analogy with image reconstruction in parallel imaging. However, this analogy has important limitations. Unlike image reconstruction, the design of radio frequency waveforms is subject to multiple strict constraints, which arise from technical power limits, as well as safety limits on local and global energy deposition in vivo. To optimize excitation profiles under such strict constraints, it is proposed to depart from the regularization strategy and rely on semidefinite programming instead. To render this approach fast, it is performed in a reduced search space, which is obtained by initial Lanczos iteration. The proposed algorithm is demonstrated to enable efficient pulse optimization within exactly the given constraints, including local specific absorption rate limits for multiple compartments. It is also shown that the proposed approach readily accommodates advanced forward models of the excitation process, including the effects of local off‐resonance and transverse relaxation. Magn Reson Med 63:1280–1291, 2010. © 2010 Wiley‐Liss, Inc.     

Brain imaging correlates of verbal working memory in children following traumatic brain injury

Neural correlates of working memory (WM) based on the Sternberg Item Recognition Task (SIRT) were assessed in 40 children with moderate-to-severe traumatic brain injury (TBI) compared to 41 demographically-comparable children with orthopedic injury (OI). Multiple magnetic resonance imaging (MRI) methods assessed structural and functional brain correlates of WM, including volumetric and cortical thickness measures on all children; functional MRI (fMRI) and diffusion tensor imaging (DTI) were performed on a subset of children. Confirming previous findings, children with TBI had decreased cortical thickness and volume as compared to the OI group. Although the findings did not confirm the predicted relation of decreased frontal lobe cortical thickness and volume to SIRT performance, left parietal volume was negatively related to reaction time (RT). In contrast, cortical thickness was positively related to SIRT accuracy and RT in the OI group, particularly in aspects of the frontal and parietal lobes, but these relationships were less robust in the TBI group. We attribute these findings to disrupted fronto-parietal functioning in attention and WM. fMRI results from a subsample demonstrated fronto-temporal activation in the OI group, and parietal activation in the TBI group, and DTI findings reflected multiple differences in white matter tracts that engage fronto-parietal networks. Diminished white matter integrity of the frontal lobes and cingulum bundle as measured by DTI was associated with longer RT on the SIRT. Across modalities, the cingulate emerged as a common structure related to performance after TBI. These results are discussed in terms of how different imaging modalities tap different types of pathologic correlates of brain injury and their relationship with WM.     

Temporal filtering effects in dynamic parallel MRI

Autocalibrated parallel MRI methods such as TSENSE or k‐t SENSE have been presented for dynamic imaging studies as they are able to provide images with high temporal resolution. One key element of these techniques is the temporal averaging of the undersampled raw data to obtain an unaliased image. This image represents the temporal average (also known as direct current, DC) and is used to derive the reconstruction parameters. In this work, we show that aliasing artifacts can be introduced in the DC signal obtained from the undersampled raw data. These artifacts lead to undesired temporal filtering effects when the DC signal is used for coil sensitivity calibration or when the DC signal is subtracted from the raw data. It is demonstrated that the temporal filtering effects can be reduced significantly by filtering the DC signal. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Projection-based estimation and nonuniformity correction of sensitivity profiles in phased-array surface coils

PURPOSE: To develop a novel approach for calculating the accurate sensitivity profiles of phased-array coils, resulting in correction of nonuniform intensity in parallel MRI. MATERIALS AND METHODS: The proposed intensity-correction method estimates the accurate sensitivity profile of each channel of the phased-array coil. The sensitivity profile is estimated by fitting a nonlinear curve to every projection view through the imaged object. The nonlinear curve-fitting efficiently obtains the low-frequency sensitivity profile by eliminating the high-frequency image contents. Filtered back-projection (FBP) is then used to compute the estimates of the sensitivity profile of each channel. The method was applied to both phantom and brain images acquired from the phased-array coil. RESULTS: Intensity-corrected images from the proposed method had more uniform intensity than those obtained by the commonly used sum-of-squares (SOS) approach. With the use of the proposed correction method, the intensity variation was reduced to 6.1% from 13.1% of the SOS. When the proposed approach was applied to the computation of the sensitivity maps during sensitivity encoding (SENSE) reconstruction, it outperformed the SOS approach in terms of the reconstructed image uniformity. CONCLUSION: The proposed method is more effective at correcting the intensity nonuniformity of phased-array surface-coil images than the conventional SOS method. In addition, the method was shown to be resilient to noise and was successfully applied for image reconstruction in parallel imaging.     

Magnetic resonance imaging in hypoxic-ischaemic encephalopathy

Magnetic resonance imaging of the brain is invaluable in assessing the neonate who presents with encephalopathy. Successful imaging requires adaptations to both the hardware and sequences used for adults. Knowledge of the perinatal and postnatal details are essential for the correct interpretation of the imaging findings. Perinatal lesions are at their most obvious on conventional imaging between 1 and 2 weeks from delivery. Very early imaging is useful to guide management in ventilated neonates but abnormalities may be subtle on conventional sequences. Diffusion-weighted imaging (DWI) is clinically useful for the early identification of ischaemic tissue in the neonatal brain, the pattern of which can predict outcome. DWI may underestimate the final extent of injury, particularly basal ganglia and thalamic lesions. Serial imaging with quantification of both tissue damage and structure size provides invaluable insights into the effects of perinatal injury on the developing brain.     

Field-of-view limitations in parallel imaging.

Parallel imaging is one of the most promising developments in recent years for the acceleration of MR acquisitions. One area of practical importance where different parallel imaging methods perform differently is the manner in which they deal with aliasing in the full-FOV reconstructed image. It has been reported that sensitivity encoding (SENSE) reconstruction fails whenever the reconstructed FOV is smaller than the object being imaged. On the other hand, generalized autocalibrating partially parallel acquisition (GRAPPA) has been used successfully to reconstruct images with aliasing in the reconstructed FOV, as in conventional imaging. The disparate behavior of these methods can be easily demonstrated by a few simple illustrative examples. Additional in vivo examples using GRAPPA and modified SENSE (mSENSE) make this distinction clear. These experiments demonstrate that SENSE fails to reconstruct correct images when coil sensitivity maps are used that do not automatically account for the object size and therefore the aliasing in the reconstructed images. However, with the use of aliased high-resolution coil sensitivity maps, accurate SENSE reconstructions can be generated. On the other hand, GRAPPA produces images with an aliasing appearance that is exactly as would be expected from normal nonaccelerated acquisitions. An understanding of these effects could potentially lead to fewer operator-dependent errors, as well as a better understanding of the differences between the underlying reconstruction processes.     

Optimizing spatiotemporal sampling for k-t BLAST and k-t SENSE: application to high-resolution real-time cardiac steady-state free precession.

In k-t BLAST and k-t SENSE, data acquisition is accelerated by sparsely sampling k-space over time. This undersampling in k-t space causes the object signals to be convolved with a point spread function in x-f space (x = spatial position, f = temporal frequency). The resulting aliasing is resolved by exploiting spatiotemporal correlations within the data. In general, reconstruction accuracy can be improved by controlling the k-t sampling pattern to minimize signal overlap in x-f space. In this work, we describe an approach to obtain generally favorable patterns for typical image series without specific knowledge of the image series itself. These optimized sampling patterns were applied to free-breathing, untriggered (i.e., real-time) cardiac imaging with steady-state free precession (SSFP). Eddy-current artifacts, which are otherwise increased drastically in SSFP by the undersampling, were minimized using alternating k-space sweeps. With the synergistic combination of the k-t approach with optimized sampling and SSFP with alternating k-space sweeps, it was possible to achieve a high signal-to-noise ratio, high contrast, and high spatiotemporal resolutions, while achieving substantial immunity against eddy currents. Cardiac images are shown, demonstrating excellent image quality and an in-plane resolution of approximately 2.0 mm at >25 frames/s, using one or more receiver coils.     

Application of sensitivity-encoded echo-planar imaging for blood oxygen level-dependent functional brain imaging.

The benefits of sensitivity-encoded (SENSE) echo-planar imaging (EPI) for functional MRI (fMRI) based on blood oxygen level-dependent (BOLD) contrast were quantitatively investigated at 1.5 T. For experiments with 3.4 x 3.4 x 4.0 mm(3) resolution, SENSE allowed the single-shot EPI image acquisition duration to be shortened from 24.1 to 12.4 ms, resulting in a reduced sensitivity to geometric distortions and T(*)(2) blurring. Finger-tapping fMRI experiments, performed on eight normal volunteers, showed an overall 18% loss in t-score in the activated area, which was substantially smaller than expected based on the image signal-to-noise ratio (SNR) and g-factor, but similar to the loss predicted by a model that takes physiologic noise into account.