Improved signal-to-noise performance of MultiNet GRAPPA 1 H FID MRSI reconstruction with semi-synthetic calibration data

PURPOSE

To further develop MultiNet GRAPPA, a neural-network-based reconstruction, for lower SNR proton MRSI (¹ H MRSI) data using adapted undersampling schemes and improved training sets.

METHODS

¹ H FID-MRSI data and an anatomical image for GRAPPA reconstruction were acquired in two slices in the human brain (n = 6) at 7T. MRSI data were retrospectively undersampled for a 4×, 6×, and 7× acceleration rate. Signal-to-noise, relative error (RE) between accelerated and fully sampled metabolic maps, RMS of the lipid artifacts, and fitting reliability were compared across acceleration rates, to the fully sampled data, and with different kinds and amounts of training images.

RESULTS

Training with semi-synthetic images resulted in higher SNR and lower lipid RMS relative to training with acquired images from one or several subjects. SNR increased with the number of semi-synthetic training images and the 4× accelerated data retains ∼30% more SNR than other accelerated data. Spectra reconstructed with 20 semi-synthetic averages retained ∼100% more SNR and had ∼5% lower lipid RMS than those reconstructed with the center k-space points of one image as was originally proposed for very high SNR MRSI data and had higher fitting reliability. The metabolite RE was lowest when training with 20-semi-synthetic training images and highest when training with the center k-space points of one image.

CONCLUSION

MultiNet GRAPPA is feasible with lower SNR ¹ H MRSI data if 20-semi-synthetic training images are used at a 4× acceleration rate. This acceleration rate provided the best trade-off between scan time and spectral SNR.

MetaBridge: An Integrative Multi-Omics Tool for Metabolite-Enzyme Mapping

MetaBridge is a web-based tool designed to facilitate the integration of metabolomics with other "omics" data types such as transcriptomics and proteomics. It uses data from the MetaCyc metabolic pathway database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to map metabolite compounds to directly interacting upstream or downstream enzymes in enzymatic reactions and metabolic pathways. The resulting list of enzymes can then be integrated with transcriptomics or proteomics data via protein-protein interaction networks to perform integrative multi-omics analyses. MetaBridge was developed to be intuitive and easy to use, requiring little to no prior computational experience. The protocols described here detail all steps involved in the use of MetaBridge, from preparing input data and performing metabolite mapping to utilizing the results to build a protein-protein interaction network. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Mapping metabolite data using MetaCyc identifiers Basic Protocol 2: Mapping metabolite data using KEGG identifiers Support Protocol 1: Converting compound names to HMDB IDs Support Protocol 2: Submitting mapped genes produced by MetaBridge for protein-protein interaction (PPI) network construction.

Dynamic 2D and 3D mapping of hyperpolarized pyruvate to lactate conversion in vivo with efficient multi-echo balanced steady-state free precession at 3 T

The aim of this study was to acquire the transient MRI signal of hyperpolarized tracers and their metabolites efficiently, for which specialized imaging sequences are required. In this work, a multi-echo balanced steady-state free precession (me-bSSFP) sequence with Iterative Decomposition with Echo Asymmetry and Least squares estimation (IDEAL) reconstruction was implemented on a clinical 3 T positron-emission tomography/MRI system for fast 2D and 3D metabolic imaging. Simulations were conducted to obtain signal-efficient sequence protocols for the metabolic imaging of hyperpolarized biomolecules. The sequence was applied in vitro and in vivo for probing the enzymatic exchange of hyperpolarized [1-¹³ C]pyruvate and [1-¹³ C]lactate. Chemical shift resolution was achieved using a least-square, iterative chemical species separation algorithm in the reconstruction. In vitro, metabolic conversion rate measurements from me-bSSFP were compared with NMR spectroscopy and free induction decay-chemical shift imaging (FID-CSI). In vivo, a rat MAT-B-III tumor model was imaged with me-bSSFP and FID-CSI. 2D metabolite maps of [1-¹³ C]pyruvate and [1-¹³ C]lactate acquired with me-bSSFP showed the same spatial distributions as FID-CSI. The pyruvate-lactate conversion kinetics measured with me-bSSFP and NMR corresponded well. Dynamic 2D metabolite mapping with me-bSSFP enabled the acquisition of up to 420 time frames (scan time: 180-350 ms/frame) before the hyperpolarized [1-¹³ C]pyruvate was relaxed below noise level. 3D metabolite mapping with a large field of view (180 × 180 × 48 mm³ ) and high spatial resolution (5.6 × 5.6 × 2 mm³ ) was conducted with me-bSSFP in a scan time of 8.2 seconds. It was concluded that Me-bSSFP improves the spatial and temporal resolution for metabolic imaging of hyperpolarized [1-¹³ C]pyruvate and [1-¹³ C]lactate compared with either of the FID-CSI or EPSI methods reported at 3 T, providing new possibilities for clinical and preclinical applications.

Over-discretized SENSE reconstruction and B0 correction for accelerated non-lipid-suppressed 1 H FID MRSI of the human brain at 9.4 T

The aim of this work was to use post-processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4 T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B₀ correction of spatially over-discretized magnetic resonance spectroscopic imaging (MRSI) data. Since MRSI scans suffer from long scan duration, investigating different acceleration techniques has recently been the focus of several studies. Due to strong B₀ inhomogeneity and strict specific absorption rate (SAR) limitations at ultra-high fields, the use of a low-SAR sequence combined with an acceleration technique that is compatible with dynamic B₀ shim updating is preferable. Hence, in this study, a non-lipid-suppressed ultra-short T_E and T_R¹ H free induction decay MRSI sequence is combined with an in-plane SENSE acceleration technique to obtain high-resolution metabolite maps in a clinically feasible scan time. One of the major issues in applying parallel imaging techniques to non-lipid-suppressed MRSI is the presence of strong lipid aliasing artifacts, which if not thoroughly resolved will hinder the accurate quantification of the metabolites of interest. To achieve a more robust reconstruction, an over-discretized SENSE reconstruction (with direct control over the shape of the spatial response function) was combined with an over-discretized B₀ correction. This method is compared with conventional SENSE reconstruction for seven acceleration schemes on four healthy volunteers. The over-discretized method consistently outperformed conventional SENSE, resulting in an average of 23 ± 1.2% higher signal-to-noise ratio and 8 ± 2.9% less error in the fitting of the N-acetylaspartate signal over a whole brain slice. The highest achievable acceleration factor with the proposed technique was determined to be 4. Finally, using the over-discretized method, high-resolution (97 μL nominal voxel size) metabolite maps can be acquired in 3.75 min at 9.4 T. This enables the acquisition of high-resolution metabolite maps with more spatial coverage at ultra-high fields.

Compressed sensing for high-resolution nonlipid suppressed 1 H FID MRSI of the human brain at 9.4T

PURPOSE

The aim of this study was to apply compressed sensing to accelerate the acquisition of high resolution metabolite maps of the human brain using a nonlipid suppressed ultra-short TR and TE ¹ H FID MRSI sequence at 9.4T.

METHODS

X-t sparse compressed sensing reconstruction was optimized for nonlipid suppressed ¹ H FID MRSI data. Coil-by-coil x-t sparse reconstruction was compared with SENSE x-t sparse and low rank reconstruction. The effect of matrix size and spatial resolution on the achievable acceleration factor was studied. Finally, in vivo metabolite maps with different acceleration factors of 2, 4, 5, and 10 were acquired and compared.

RESULTS

Coil-by-coil x-t sparse compressed sensing reconstruction was not able to reliably recover the nonlipid suppressed data, rather a combination of parallel and sparse reconstruction was necessary (SENSE x-t sparse). For acceleration factors of up to 5, both the low-rank and the compressed sensing methods were able to reconstruct the data comparably well (root mean squared errors [RMSEs] ≤ 10.5% for Cre). However, the reconstruction time of the low rank algorithm was drastically longer than compressed sensing. Using the optimized compressed sensing reconstruction, acceleration factors of 4 or 5 could be reached for the MRSI data with a matrix size of 64 × 64. For lower spatial resolutions, an acceleration factor of up to R∼4 was successfully achieved.

CONCLUSION

By tailoring the reconstruction scheme to the nonlipid suppressed data through parameter optimization and performance evaluation, we present high resolution (97 µL voxel size) accelerated in vivo metabolite maps of the human brain acquired at 9.4T within scan times of 3 to 3.75 min.