Accelerated Nano-Optical Imaging through Sparse Sampling.

The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.

High-throughput determination of grain size distributions by EBSD with low-discrepancy sampling.

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high-throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high-throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low-discrepancy sampling, including on-the-fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov-Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points.

High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions.

In the past two decades, the power of mass spectrometry imaging (MSI) for the label free spatial mapping of molecules in biological systems has been substantially enhanced by the development of approaches for imaging with high spatial resolution. With the increase in the spatial resolution, the experimental throughput has become a limiting factor for imaging of large samples with high spatial resolution and 3D imaging of tissues. Several experimental and computational approaches have been recently developed to enhance the throughput of MSI. In this critical review, we provide a succinct summary of the current approaches used to improve the throughput of MSI experiments. These approaches are focused on speeding up sampling, reducing the mass spectrometer acquisition time, and reducing the number of sampling locations. We discuss the rate-determining steps for different MSI methods and future directions in the development of high-throughput MSI techniques.

Low-energy electron microscopy intensity-voltage data - Factorization, sparse sampling and classification.

Low-energy electron microscopy (LEEM) taken as intensity-voltage (I-V) curves provides hyperspectral images of surfaces, which can be used to identify the surface type, but are difficult to analyse. Here, we demonstrate the use of an algorithm for factorizing the data into spectra and concentrations of characteristic components (FSC3 ) for identifying distinct physical surface phases. Importantly, FSC3 is an unsupervised and fast algorithm. As example data we use experiments on the growth of praseodymium oxide or ruthenium oxide on ruthenium single crystal substrates, both featuring a complex distribution of coexisting surface components, varying in both chemical composition and crystallographic structure. With the factorization result a sparse sampling method is demonstrated, reducing the measurement time by 1-2 orders of magnitude, relevant for dynamic surface studies. The FSC3 concentrations are providing the features for a support vector machine-based supervised classification of the surface types. Here, specific surface regions which have been identified structurally, via their diffraction pattern, as well as chemically by complementary spectro-microscopic techniques, are used as training sets. A reliable classification is demonstrated on both example LEEM I-V data sets.

A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers.

The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations. Through a 3D convolution neural network (CNN) and stacked autoencoder framework (CSA), the data-driven CSA-NAH method can solve this problem by utilizing the information from data in each dimension. In this paper, the cylindrical translation window (CTW) is introduced to truncate and roll out the cylindrical image to compensate for the loss of circumferential features at the truncation edge. Combined with the CSA-NAH method, a cylindrical NAH method based on stacked 3D-CNN layers (CS3C) for sparse sampling is proposed, and its feasibility is verified numerically. In addition, the planar NAH method based on the Paulis-Gerchberg extrapolation interpolation algorithm (PGa) is introduced into the cylindrical coordinate system, and compared with the proposed method. The results show that, under the same conditions, the reconstruction error rate of the CS3C-NAH method is reduced by nearly 50%, and the effect is significant.

High-resolution, 3D multi-TE 1 H MRSI using fast spatiospectral encoding and subspace imaging.

PURPOSE: To develop a novel method to achieve fast, high-resolution, 3D multi-TE 1 H-MRSI of the brain. METHODS: A new multi-TE MRSI acquisition strategy was developed that integrates slab selective excitation with adiabatic refocusing for better volume coverage, rapid spatiospectral encoding, sparse multi-TE sampling, and interleaved water navigators for field mapping and calibration. Special data processing strategies were developed to interpolate the sparsely sampled data, remove nuisance signals, and reconstruct multi-TE spatiospectral distributions with high SNR. Phantom and in vivo experiments have been carried out to demonstrate the capability of the proposed method. RESULTS: The proposed acquisition can produce multi-TE 1 H-MRSI data with three TEs at a nominal spatial resolution of 3.4 × 3.4 × 5.3 mm3 in around 20 min. High-SNR brain metabolite spatiospectral reconstructions can be obtained from both a metabolite phantom and in vivo experiments by the proposed method. CONCLUSION: High-resolution, 3D multi-TE 1 H-MRSI of the brain can be achieved within clinically feasible time. This capability, with further optimizations, could be translated to clinical applications and neuroscience studies where simultaneously mapping metabolites and neurotransmitters and TE-dependent molecular spectral changes are of interest.

Whole-Heart 4D Flow MRI for Evaluation of Normal and Regurgitant Valvular Flow: A Quantitative Comparison Between Pseudo-Spiral Sampling and EPI Readout.

BACKGROUND: Pseudo-spiral Cartesian sampling with compressed sensing reconstruction has facilitated highly accelerated 4D flow magnetic resonance imaging (MRI) in various cardiovascular structures. However, unlike echo planar imaging (EPI)-accelerated 4D flow MRI, it has not been validated in whole-heart applications. HYPOTHESIS: Pseudo-spiral 4D flow MRI (PROUD [PROspective Undersampling in multiple Dimensions]) is comparable to EPI in robustness of valvular flow measurements and remains comparable as the undersampling factor is increased and scan time reduced. STUDY TYPE: Prospective. POPULATION: Twelve healthy subjects and eight patients with valvular regurgitation. FIELD STRENGTH/SEQUENCE: 3.0 T; PROUD and EPI 4D flow sequences, 2D flow and balanced steady-state free precession sequences. ASSESSMENT: Valvular blood flow was quantified using valve tracking. PROUD- and EPI-based measurements of aortic (AV) and pulmonary (PV) flow volumes and left and right ventricular stroke volumes were tested for agreement with 2D MRI-based measurements. PROUD reconstructions with undersampling factors (R) of 9, 14, 28, and 56 were tested for intervalve consistency (per valve, compared to the other valves) and preservation of peak velocities and E/A ratios. STATISTICAL TESTS: We used repeated measures ANOVA, Bland-Altman, Wilcoxon signed rank, and intraclass correlation coefficients. P < 0.05 was considered statistically significant. RESULTS: PROUD and EPI intervalve consistencies were not significantly different both in healthy subjects (valve-averaged mean difference [limits of agreement width]: 3.2 ± 0.8 [8.7 ± 1.1] mL/beat for PROUD, 5.5 ± 2.9 [13.7 ± 2.3] mL/beat for EPI, P = 0.07) and in patients with valvular regurgitation (2.3 ± 1.2 [15.3 ± 5.9] mL/beat for PROUD, 0.6 ± 0.6 [19.3 ± 2.9] mL/beat for EPI, P = 0.47). Agreement between EPI and PROUD was higher than between 4D flow (EPI or PROUD) and 2D MRI for forward flow, stroke volumes, and regurgitant volumes. Up to R = 28 in healthy subjects and R = 14 in patients with valvular regurgitation, PROUD intervalve consistency remained comparable to that of EPI. Peak velocities and E/A ratios were preserved up to R = 9. CONCLUSION: PROUD is comparable to EPI in terms of intervalve consistency and may be used with higher undersampling factors to shorten scan times further. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Near-Field High-Resolution SAR Imaging with Sparse Sampling Interval.

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 λ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 λ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems.

Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing.

PURPOSE: To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging. METHODS: Simultaneous QSM and metabolic imaging was first achieved using SPICE (spectroscopic imaging by exploiting spatiospectral correlation), but the QSM maps thus obtained were at relatively low-resolution (2.0 × 3.0 × 3.0 mm3 ). We overcome this limitation using an improved SPICE data acquisition method with the following novel features: 1) sampling (k, t)-space in dual densities, 2) sampling central k-space fully to achieve nominal spatial resolution of 3.0 × 3.0 × 3.0 mm3 for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm3 for QSM. To keep the scan time short, we acquired spatiospectral encodings in echo-planar spectroscopic imaging trajectories in central k-space but in CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) trajectories in outer k-space using blipped phase encodings. For data processing and image reconstruction, a union-of-subspaces model was used, effectively incorporating sensitivity encoding, spatial priors, and spectral priors of individual molecules. RESULTS: In vivo experiments were carried out to evaluate the feasibility and potential of the proposed method. In a 6-min scan, QSM maps at 1.0 × 1.0 × 1.9 mm3 resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm3 nominal resolution were obtained simultaneously. Compared with the original method, the QSM maps obtained using the new method reveal fine-scale brain structures more clearly. CONCLUSION: We demonstrated the feasibility of achieving high-resolution QSM simultaneously with metabolic imaging using a modified SPICE acquisition method. The improved capability of SPICE may further enhance its practical utility in brain mapping.

Sparse precontrast T1 mapping for high-resolution whole-brain DCE-MRI.

PURPOSE: To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high-resolution whole-brain dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI). METHODS: Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high-resolution whole-brain DCE-MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model-based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise-mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high-grade gliomas. RESULTS: T1 -mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T1 standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T1 bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T1 SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T1 bias was <30 ms for R ≤ 16. At R = 4, T1 SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T1 values were consistent with literature values in WM and BT. CONCLUSION: High-resolution whole-brain VFA T1 mapping is feasible with sparse sampling, supporting its use for quantitative DCE-MRI.

The neural correlates of referential communication: Taking advantage of sparse-sampling fMRI to study verbal communication with a real interaction partner.

This paper introduces an innovative functional magnetic resonance imaging (fMRI) protocol to study real verbal interactions while limiting the impact of speech-related movement artefacts. This protocol is based on a sparse sampling acquisition technique and allowed participants to complete a referential communication task with a real interaction partner. During verbal interactions, speakers adjust their verbal productions depending on their interlocutors' knowledge of the referents being mentioned. These adjustments have been linked to theory of mind (ToM), the ability to infer other's mental states. We thus sought to determine if the brain regions supporting ToM would also be activated during a referential communication task in which participants have to present movie characters that vary in their likelihood of being known by their interlocutor. This pilot study establishes that the sparse sampling strategy is a viable option to study the neural correlates of referential communication while minimizing movement artefacts. In addition, the brain regions supporting ToM were recruited during the task, though specifically for the conditions where participants could adjust their verbal productions to the interlocutor's likely knowledge of the referent. This study therefore demonstrates the feasibility and relevance of a sparse-sampling approach to study verbal interactions with fMRI, including referential communication.

Compressive sensing for polarization sensitive optical coherence tomography.

In this report, we report on the implementation of compressive sensing (CS) and sparse sampling in polarization sensitive optical coherence tomography (PS-OCT) to reduce the number of B-scans (frames consisting of an array of A-scans, where each represents a single depth profile of reflections) required for effective volumetric (3D dataset composed of an array of B-scans) PS-OCT measurements (i.e. OCT intensity, and phase retardation) reconstruction. Sparse sampling of PS-OCT is achieved through randomization of step sizes along the slow-axis of PS-OCT imaging, covering the same spatial ranges as those with equal slow-axis step sizes, but with a reduced number of B-scans. Tested on missing B-scan rates of 25%, 50% and 75%, we found CS could reconstruct reasonably good (as evidenced by a correlation coefficient >0.6) PS-OCT measurements with a maximum reduced B-scan rate of 50%, thereby accelerating and doubling the rate of volumetric PS-OCT measurements.

Non-uniform sampling in quantitative assessment of heterogeneous solid-state NMR line shapes.

Non-uniform sampling has been successfully used for solution and solid-state NMR of homogeneous samples. In the solid state, protein samples are often dominated by inhomogeneous contributions to the homogeneous line widths. In spite of different technical strategies for peak reconstruction by different methods, we validate that NUS can generally be used also for such situations where spectra are made up of complex peak shapes rather than Lorentian lines. Using the RMSD between subsampled and reconstructed data and those spectra obtained with uniform sampling for a sample comprising a wide conformational distribution, we quantitatively evaluate the identity of inhomogeneous peak patterns. The evaluation comprises Iterative Soft Thresholding (hmsIST implementation) as a method explicitly not assuming Lorentian lineshapes, as well as Sparse Multidimensional Iterative Lineshape Enhanced (SMILE) algorithm and Signal Separation Algorithm (SSA) reconstruction, which do work on the basis of Lorentian lineshape models, with different sampling densities. Even though individual peculiarities are apparent, all methods turn out principally viable to reconstruct the heterogeneously broadened peak shapes.

Application of tail vein serial microsampling for plasma or dried plasma spots in toxicokinetic assessment in rats using acetaminophen as the model compound.

In the current study, two groups of rats (five per group) were administered a single oral dose of 500 mg/kg acetaminophen. For toxicokinetic assessment, the Group 1 animals were bled via conventional sparse (two animals/time point) sublingual vein bleeding (~0.5 ml) with anesthesia, while the Group 2 animals were bled via serial tail vein microsampling (~0.075 ml) without anesthesia. All collected blood was processed for plasma. Each Group 2 plasma sample (~30 µl) was divided into 'wet' and 'dried' (dried plasma spots). All plasma samples were analyzed by LC-MS/MS for acetaminophen and its major metabolites acetaminophen glucuronide and acetaminophen sulfate. In addition, plasma and urine samples were collected for analysis of corticosterone and creatinine to assess stress levels. Comparable plasma exposure to acetaminophen and its two metabolites was observed in the plasma obtained via conventional sparse sublingual vein bleeding and serial tail vein microsampling and between the 'wet' and 'dried' plasma obtained by the latter. Furthermore, comparable corticosterone levels or corticosterone/creatinine ratios between the two groups suggested that serial microsampling without anesthesia did not increase the levels of stress as compared with conventional sampling with anesthesia, confirming the utility of microsampling for plasma or dried plasma spots in rodent toxicokinetic assessment.

A Subspace Approach to Sparse Sampling based Data Gathering in Wireless Sensor Networks.

Data gathering is an essential concern in Wireless Sensor Networks (WSNs). This paper proposes an efficient data gathering method in clustered WSNs based on sparse sampling to reduce energy consumption and prolong the network lifetime. For data gathering scheme, we propose a method that can collect sparse sampled data in each time slot with a fixed percent of nodes remaining in sleep mode. For data reconstruction, a subspace approach is proposed to enforce an explicit low-rank constraint for data reconstruction from sparse sampled data. Subspace representing spatial distributions of the WSNs data can be estimated from previous reconstructed data. Incorporating total variation constraint, the proposed reconstruction method reconstructs current time slot data efficiently. The results of experiments indicate that the proposed method can reduce the energy consumption and prolong the network lifetime with satisfying recovery accuracy.

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on both the development of algorithms to accurately reconstruct the sparsely sampled spectra and the design of sampling schedules that maximise the information contained in the sampled data. Traditionally, the reconstruction tools and algorithms have aimed at reconstructing the full spectrum and thus 'fill out the missing points' in the time-domain spectrum, although other techniques are based on multi-dimensional decomposition and extraction of multi-dimensional shapes. Also over the last decade, machine learning, deep neural networks, and artificial intelligence have seen new applications in an enormous range of sciences, including analysis of MRI spectra. As a proof-of-principle, it is shown here that simple deep neural networks can be trained to reconstruct sparsely sampled NMR spectra. For the reconstruction of two-dimensional NMR spectra, reconstruction using a deep neural network performs as well, if not better than, the currently and widely used techniques. It is therefore anticipated that deep neural networks provide a very valuable tool for the reconstruction of sparsely sampled NMR spectra in the future to come.

Reaching the sparse-sampling limit for reconstructing a single peak in a 2D NMR spectrum using iterated maps.

Many of the ubiquitous experiments of biomolecular NMR, including [Formula: see text], [Formula: see text], and CEST, involve acquiring repeated 2D spectra under slightly different conditions. Such experiments are amenable to acceleration using non-uniform sampling spectral reconstruction methods that take advantage of prior information. We previously developed one such technique, an iterated maps method (DiffMap) that we successfully applied to 2D NMR spectra, including [Formula: see text] relaxation dispersion data. In that prior work, we took a top-down approach to reconstructing the 2D spectrum with a minimal number of sparse samples, reaching an undersampling fraction that appeared to leave some room for improvement. In this study, we develop an in-depth understanding of the action of the DiffMap algorithm, identifying the factors that cause reconstruction errors for different undersampling fractions. This improved understanding allows us to formulate a bottom-up approach to finding the lowest number of sparse samples required to accurately reconstruct individual spectral features with DiffMap. We also discuss the difficulty of extending this method to reconstructing many peaks at once, and suggest a way forward.

Accelerating 2D NMR relaxation dispersion experiments using iterated maps.

NMR relaxation dispersion experiments play a central role in exploring molecular motion over an important range of timescales, and are an example of a broader class of multidimensional NMR experiments that probe important biomolecules. However, resolving the spectral features of these experiments using the Fourier transform requires sampling the full Nyquist grid of data, making these experiments very costly in time. Practitioners often reduce the experiment time by omitting 1D experiments in the indirectly observed dimensions, and reconstructing the spectra using one of a variety of post-processing algorithms. In prior work, we described a fast, Fourier-based reconstruction method using iterated maps according to the Difference Map algorithm of Veit Elser (DiffMap). Here we describe coDiffMap, a new reconstruction method that is based on DiffMap, but which exploits the strong correlations between 2D data slices in a pseudo-3D experiment. We apply coDiffMap to reconstruct dispersion curves from an [Formula: see text] relaxation dispersion experiment, and demonstrate that the method provides fast reconstructions and accurate relaxation curves down to very low numbers of sparsely-sampled data points.

Importance of time-ordered non-uniform sampling of multi-dimensional NMR spectra of Aß1-42 peptide under aggregating conditions.

Although the order of the time steps in which the non-uniform sampling (NUS) schedule is implemented when acquiring multi-dimensional NMR spectra is of limited importance when sample conditions remain unchanged over the course of the experiment, it is shown to have major impact when samples are unstable. In the latter case, time-ordering of the NUS data points by the normalized radial length yields a reduction of sampling artifacts, regardless of the spectral reconstruction algorithm. The disadvantage of time-ordered NUS sampling is that halting the experiment prior to its completion will result in lower spectral resolution, rather than a sparser data matrix. Alternatively, digitally correcting for sample decay prior to reconstruction of randomly ordered NUS data points can mitigate reconstruction artifacts, at the cost of somewhat lower sensitivity. Application of these sampling schemes to the Alzheimer's amyloid beta (Aß1-42) peptide at an elevated concentration, low temperature, and 3 kbar of pressure, where approximately 75% of the peptide reverts to an NMR-invisible state during the collection of a 3D 15N-separated NOESY spectrum, highlights the improvement in artifact suppression and reveals weak medium-range NOE contacts in several regions, including the C-terminal region of the peptide.

Simultaneous metabolic and functional imaging of the brain using SPICE.

PURPOSE: To enable simultaneous high-resolution mapping of brain function and metabolism. METHODS: An encoding scheme was designed for interleaved acquisition of functional MRI (fMRI) data in echo volume imaging trajectories and MR spectroscopic imaging (MRSI) data in echo-planar spectroscopic imaging trajectories. The scheme eliminates water and lipid suppression and utilizes free induction decay signals to encode both functional and metabolic information with ultrashort TE, short TR, and sparse sampling of k,t -space. A subspace-based image reconstruction method was introduced for processing both the fMRI and MRSI data. The complementary information in the fMRI and MRSI data sets was also utilized to improve image reconstruction in the presence of intrascan head motion, field drift, and tissue susceptibility changes. RESULTS: In-vivo experimental results were obtained from healthy human subjects in resting-state fMRI/MRSI experiments. In these experiments, the proposed method was able to simultaneously acquire metabolic and functional information from the brain in high resolution. For scans of 6.5 minutes, we achieved 3.0 × 3.0 × 1.8 mm3 spatial resolution for fMRI, 1.9 × 2.5 × 3.0 mm3 nominal spatial resolution for MRSI, and 1.9 × 1.9 × 1.8 mm3 nominal spatial resolution for quantitative susceptibility maps. CONCLUSION: This work demonstrates the feasibility of simultaneous high-resolution mapping of brain function and metabolism with improved spatial resolution and synergistic image reconstruction.