Purpose
ESPIRiT is a parallel imaging method that estimates coil sensitivity maps from the auto‐calibration region (ACS). This requires choosing several parameters for the optimal map estimation. ...While fairly robust to these parameter choices, occasionally, poor selection can result in reduced performance. The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams.
Methods
By viewing ESPIRiT as a denoiser, Stein’s unbiased risk estimate (SURE) is leveraged to automatically optimize parameter selection in a data‐driven manner. The optimum parameters corresponding to the minimum true squared error, minimum SURE as derived from densely sampled, high‐resolution, and non‐accelerated data and minimum SURE as derived from ACS are compared using simulation experiments. To avoid optimizing the rank of ESPIRiT’s auto‐calibrating matrix (one of the parameters), a heuristic derived from SURE‐based singular value thresholding is also proposed.
Results
Simulations show SURE derived from the densely sampled, high‐resolution, and non‐accelerated data to be an accurate estimator of the true mean squared error, enabling automatic parameter selection. The parameters that minimize SURE as derived from ACS correspond well to the optimal parameters. The soft‐threshold heuristic improves computational efficiency while providing similar results to an exhaustive search. In‐vivo experiments verify the reliability of this method.
Conclusions
Using SURE to determine ESPIRiT parameters allows for automatic parameter selections. In‐vivo results are consistent with simulation and theoretical results.
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction, successfully generating high quality images for CT, PET and MRI. In this work, an MRI ...reconstruction algorithm, which is referred to as quantitative susceptibility mapping (QSM), has been developed using a deep neural network in order to perform dipole deconvolution, which restores magnetic susceptibility source from an MRI field map. Previous approaches of QSM require multiple orientation data (e.g. Calculation of Susceptibility through Multiple Orientation Sampling or COSMOS) or regularization terms (e.g. Truncated K-space Division or TKD; Morphology Enabled Dipole Inversion or MEDI) to solve an ill-conditioned dipole deconvolution problem. Unfortunately, they either entail challenges in data acquisition (i.e. long scan time and multiple head orientations) or suffer from image artifacts. To overcome these shortcomings, a deep neural network, which is referred to as QSMnet, is constructed to generate a high quality susceptibility source map from single orientation data. The network has a modified U-net structure and is trained using COSMOS QSM maps, which are considered as gold standard. Five head orientation datasets from five subjects were employed for patch-wise network training after doubling the training data using a model-based data augmentation. Seven additional datasets of five head orientation images (i.e. total 35 images) were used for validation (one dataset) and test (six datasets). The QSMnet maps of the test dataset were compared with the maps from TKD and MEDI for their image quality and consistency with respect to multiple head orientations. Quantitative and qualitative image quality comparisons demonstrate that the QSMnet results have superior image quality to those of TKD or MEDI results and have comparable image quality to those of COSMOS. Additionally, QSMnet maps reveal substantially better consistency across the multiple head orientation data than those from TKD or MEDI. As a preliminary application, the network was further tested for three patients, one with microbleed, another with multiple sclerosis lesions, and the third with hemorrhage. The QSMnet maps showed similar lesion contrasts with those from MEDI, demonstrating potential for future applications.
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•New QSM reconstruction, QSMnet, is developed using a deep neural network.•QSMnet generates a highly accurate QSM map close to a gold standard (COSMOS) map.•Processing time of QSMnet is only a few seconds, achieving real-time processing.•In patients, QSMnet delivers similar lesion contrasts to conventional QSM.
To evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI ...(DWI).
DWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE
) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers (
).
Pre-ENCODE provided a shorter TE
than MONO (71.0
17.7ms vs. 77.6
22.9ms) and ENCODE (71.0
17.7ms vs. 86.2
14.2ms) in 100
of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm
and in-plane spatial resolutions ranging from 1.0 to 3.0mm
. Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1
vs. 22.7
,
) and comparable to ENCODE (10.1
vs. 10.4
,
). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 0.28,1.45
mm
/s vs. 0.38 0.28,1.45
mm
/s,
) and increased in edge brain pixels (0.80 0.17,1.49
mm
/s vs. 0.70 0.18,1.48
mm
/s,
) for MONO compared to Pre-ENCODE.
Pre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE
than MONO and ENCODE.
Oscillatory neural dynamics play an important role in the coordination of large-scale brain networks. High-level cognitive processes depend on dynamics evolving over hundreds of milliseconds, so ...measuring neural activity in this frequency range is important for cognitive neuroscience. However, current noninvasive neuroimaging methods are not able to precisely localize oscillatory neural activity above 0.2 Hz. Electroencephalography and magnetoencephalography have limited spatial resolution, whereas fMRI has limited temporal resolution because it measures vascular responses rather than directly recording neural activity. We hypothesized that the recent development of fast fMRI techniques, combined with the extra sensitivity afforded by ultra-high-field systems, could enable precise localization of neural oscillations. We tested whether fMRI can detect neural oscillations using human visual cortex as a model system. We detected small oscillatory fMRI signals in response to stimuli oscillating at up to 0.75 Hz within single scan sessions, and these responses were an order of magnitude larger than predicted by canonical linear models. Simultaneous EEG–fMRI and simulations based on a biophysical model of the hemodynamic response to neuronal activity suggested that the blood oxygen level-dependent response becomes faster for rapidly varying stimuli, enabling the detection of higher frequencies than expected. Accounting for phase delays across voxels further improved detection, demonstrating that identifying vascular delays will be of increasing importance with higher-frequency activity. These results challenge the assumption that the hemodynamic response is slow, and demonstrate that fMRI has the potential to map neural oscillations directly throughout the brain.
Purpose
To implement the time‐resolved relaxometry PEPTIDE technique into a diffusion acquisition to provide self‐navigated, distortion‐ and blurring‐free diffusion imaging that is robust to motion, ...while simultaneously providing T2 and T2∗ mapping.
Theory and Methods
The PEPTIDE readout was implemented into a spin‐echo diffusion acquisition, enabling reconstruction of a time‐series of T2‐ and T2∗‐weighted images, free from conventional echo planar imaging (EPI) distortion and blurring, for each diffusion‐encoding. Robustness of PEPTIDE to motion and shot‐to‐shot phase variation was examined through a deliberate motion‐corrupted diffusion experiment. Two diffusion‐relaxometry in vivo brain protocols were also examined: (1)1 × 1 × 3 mm3 across 32 diffusion directions in 20 min, (2)1.5 × 1.5 × 3.0 mm3 across 6 diffusion‐weighted images in 3.4 min. T2, T2∗, and diffusion parameter maps were calculated from these data. As initial exploration of the rich diffusion‐relaxometry data content for use in multi‐compartment modeling, PEPTIDE data were acquired of a gadolinium‐doped asparagus phantom. These datasets contained two compartments with different relaxation parameters and different diffusion orientation properties, and T2 relaxation variations across these diffusion directions were explored.
Results
Diffusion‐PEPTIDE showed the capability to provide high quality diffusion images and T2 and T2∗ maps from both protocols. The reconstructions were distortion‐free, avoided potential resolution losses exceeding 100% in equivalent EPI acquisitions, and showed tolerance to nearly 30° of rotational motion. Expected variation in T2 values as a function of diffusion direction was observed in the two‐compartment asparagus phantom (P < .01), demonstrating potential to explore diffusion‐PEPTIDE data for multi‐compartment modeling.
Conclusions
Diffusion‐PEPTIDE provides highly robust diffusion and relaxometry data and offers potential for future applications in diffusion‐relaxometry multi‐compartment modeling.
Diffusion‐weighted imaging, a contrast unique to MRI, is used for assessment of tissue microstructure in vivo. However, this exquisite sensitivity to finer scales far above imaging resolution comes ...at the cost of vulnerability to errors caused by sources of motion other than diffusion motion. Addressing the issue of motion has traditionally limited diffusion‐weighted imaging to a few acquisition techniques and, as a consequence, to poorer spatial resolution than other MRI applications. Advances in MRI imaging methodology have allowed diffusion‐weighted MRI to push to ever higher spatial resolution. In this review we focus on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion‐weighted MRI.
One of the latest approaches to high‐resolution diffusion tensor imaging (DTI), gSlider, uses simultaneous multi‐slab coupled with RF encoding to resolve the resolution within slab, and parallel imaging with blipped controlled aliasing to unalias the simultaneous slab. Scans were acquired on a 3 T Siemens Connectome scanner (Gmax = 300 mT/m, Smax = 200 T/m/s) with custom 64‐channel receiver coil at a resolution of 660 μm isotropic, using four averages of 64 diffusion encoding directions at a b‐value of 1500 s/mm2 and a scan time of 100 min.
Echo planar time‐resolved imaging (EPTI) Wang, Fuyixue; Dong, Zijing; Reese, Timothy G. ...
Magnetic resonance in medicine,
June 2019, Letnik:
81, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Purpose
To develop an efficient distortion‐ and blurring‐free multi‐shot EPI technique for time‐resolved multiple‐contrast and/or quantitative imaging.
Methods
EPI is a commonly used sequence but ...suffers from geometric distortions and blurring. Here, we introduce a new multi‐shot EPI technique termed echo planar time‐resolved imaging (EPTI), which has the ability to rapidly acquire distortion‐ and blurring‐free multi‐contrast data set. The EPTI approach performs encoding in ky‐t space and uses a new highly accelerated spatio–temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0‐informed parallel imaging reconstruction, hundreds of “time‐resolved” distortion‐ and blurring‐free images at different TEs across the EPI readout window can be created at sub‐millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self‐estimation and correction of shot‐to‐shot B0 variations was developed. Simultaneous multi‐slice acquisition was also incorporated to further improve the acquisition efficiency.
Results
We evaluated EPTI under varying simulated acceleration factors, B0‐inhomogeneity, and shot‐to‐shot B0 variations to demonstrate its ability to provide distortion‐ and blurring‐free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient‐ and spin‐echo EPTI for quantitative mapping of T2, T2*, proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole‐brain in 28 s (0.8 s/slice), and (2) a gradient‐echo EPTI, for multi‐echo and quantitative T2* fMRI at 2 × 2 × 3 mm3 whole‐brain at a 3.3 s temporal resolution.
Conclusion
EPTI is a new approach for multi‐contrast and/or quantitative imaging that can provide fast acquisition of distortion‐ and blurring‐free images at multiple TEs.
Purpose
Wave‐CAIPI is a novel acquisition approach that enables highly accelerated 3D imaging. This paper investigates the combination of Wave‐CAIPI with LORAKS‐based reconstruction (Wave‐LORAKS) to ...enable even further acceleration.
Methods
LORAKS is a constrained image reconstruction framework that can impose spatial support, smooth phase, sparsity, and/or parallel imaging constraints. LORAKS requires minimal prior information, and instead uses the low‐rank subspace structure of the raw data to automatically learn which constraints to impose and how to impose them. Previous LORAKS implementations addressed 2D image reconstruction problems. In this work, several recent advances in structured low‐rank matrix recovery were combined to enable large‐scale 3D Wave‐LORAKS reconstruction with improved quality and computational efficiency. Wave‐LORAKS was investigated by retrospective subsampling of two fully sampled Wave‐encoded 3D MPRAGE datasets, and comparisons were made against existing Wave reconstruction approaches.
Results
Results show that Wave‐LORAKS can yield higher reconstruction quality with 16×‐accelerated data than is obtained by traditional Wave‐CAIPI with 9×‐accerated data.
Conclusions
There are strong synergies between Wave encoding and LORAKS, which enables Wave‐LORAKS to achieve higher acceleration and more flexible sampling compared to Wave‐CAIPI.
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