Spatiospectral CorrElation Research Articles

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

To develop a practical method to enable 3D T1 mapping of brain metabolites. Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data. The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter. We successfully demonstrated the feasibility of 3D metabolite T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

Unsupervised multi-level spatio-spectral fusion transformer for hyperspectral image super-resolution

Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) is widely used for HSI super-resolution. Recent works still face problems in exploring global spatio-spectral correlation and lack effective utilization of multi-level features of the inputs (i.e., HR-MSI and LR-HSI), which results in a lack of similarity between the reconstruction and the inputs, ultimately causing significant spatio-spectral distortion. To solve these problems, we design an Unsupervised Multi-level Spatio-spectral Fusion Transformer (UMSFT). In UMSFT, a novel multi-level features fusion strategy is constructed, which fuses the hierarchy features of the inputs via proposed Spatio-spectral Feature Fusion Attention Blocks (S2F-FAB) in a level-by-level manner, thereby fully exploring the interaction between the hierarchical features. The S2F-FAB is specially designed for HSI-MSI fusion consisting of two components: (1) a spatial fusion module (Spa-FM) is designed for spatial domain fusion, and its output is set as Values (V) of a transformer; (2) a novel spectral feature cross attention (Spe- FCA) formulates the features of LR-HSI and HR-MSI as Queries (Q) and Keys (K), respectively, and achieves spectral domain fusion by applying attention mechanism along the spectral dimension. Incorporating spatial detail reconstruction and spectral feature integration into the attention mechanism, the S2F-FAB efficiently exploits the spatio-spectral correlation between target HR-HSI and inputs. Experimental results on three public datasets and our real-world images show the superiority of our method as compared with eleven state-of-the-art methods. Codes will be available at https://github.com/Caoxuheng/HIFtool.

Learning a 3D-CNN and Transformer prior for hyperspectral image super-resolution

To address the ill-posed problem of hyperspectral image super-resolution (HSISR), a commonly employed technique is to design a regularization term based on the prior information of hyperspectral images (HSIs) to effectively constrain the objective function. Traditional model-based methods that rely on manually crafted priors are insufficient in fully characterizing the properties of HSIs. Learning-based methods usually use a convolutional neural network (CNN) to learn the implicit priors of HSIs. However, the learning ability of CNN is limited, it only considers the spatial characteristics of the HSIs and ignores the spectral characteristics, and convolution is not effective for long-range dependency modeling. There is still a lot of room for improvement. In this paper, we propose a novel HSISR method that leverages the Transformer architecture instead of the CNN to learn the prior of HSIs. Specifically, we employ the proximal gradient algorithm to solve the HSISR model and simulate the iterative solution process using an unfolding network. The self-attention layer of the Transformer enables global spatial interaction, while a 3D-CNN is added behind the Transformer layers to better capture the spatio-spectral correlation of HSIs. Both quantitative and visual results on three widely used HSI datasets and the real-world dataset demonstrate that the proposed method achieves a considerable gain compared to all the mainstream algorithms including the most competitive conventional methods and the recently proposed deep learning-based methods. The source code and trained models are made publicly available at https://github.com/qingma2016/3DT-Net.

Hyperspectral compressive wavefront sensing

Abstract Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.

Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks

Near Infrared hyperspectral imaging (HSI) offers a fast and non-destructive method for seed quality assessment through combining spectroscopy and imaging. Recently, convolutional neural networks (CNN) have shown to be promising tools for red-green-blue (RGB) image or spectral cereal classification. This paper describes the design and implementation of deep CNN models capable of utilizing both the spatial and spectral dimension of HSI data simultaneously for analysis of bulk grain samples with densely packed kernels. Classification of eight grain samples, including six different wheat varieties, were used as a test case. The study shows that the CNN architecture ResNet, originally designed for RGB images, can be adapted to use the full spatio-spectral dimension of the HSI data through adding a linear down sample layer prior to the conventional ResNet architecture. Using traditional spectral pre-processing methods before passing the data to the CNN does not improve the classification accuracy of the networks, while a channel-wise image standardization improves the accuracy significantly. The modified ResNet applied to the full spatio-spectral dimension has a classification accuracy of up to 99.75 ± 0.02%, outperforming both purely spectral (86.5 ± 0.1%) and purely spatial (98.70 ± 0.01%) based methods in terms of accuracy, indicating that utilizing spatio-spectral correlation can improve sample classification, but also that grain classification is primarily solved using spatial information. The findings reported in this paper demonstrate how CNN networks can be designed to leverage spatio-spectral information in hyperspectral data. The combination of HSI and spatio-spectral CNN networks shows a possible method for fast prediction of bulk grain quality parameters where both spectral and spatial properties of the grains are important.

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive components and spatiospectral parameters predetermined by manufacturers. In this paper, we demonstrate an end-to-end snapshot hyperspectral imaging technique and build a physics-informed dual attention neural network with multimodal learning. By modeling the 3D spectral cube reconstruction procedure and solving that compressive-imaging inverse problem, the hyperspectral volume can be directly recovered from only one scene RGB image. Spectra features and camera spectral sensitivity are jointly leveraged to retrieve the multiplexed spatiospectral correlations and realize hyperspectral imaging. With the help of integrated attention mechanism, useful information supplied by disparate modal components is adaptively learned and aggregated to make our network flexible for variable imaging systems. Results show that the proposed method is ultra-faster than the traditional scanning method, and 3.4 times more precise than the existing hyperspectral imaging convolutional neural network. We provide theory for network design, demonstrate training process, and present experimental results with high accuracy. Without bulky benchtop setups and strict experimental limitations, this simple and effective method offers great potential for future spectral imaging applications such as pathological digital stain, computational imaging and virtual/augmented reality display, etc.

Intrinsic spatial chirp of subcycle terahertz pulsed beams

We present an analysis of spatial chirp in subcycle pulsed beams corroborated by spatiotemporal electro-optic sampling measurements of terahertz radiation. Modeling the subcycle pulsed beam as a superposition of monochromatic Gaussian beams, free-space propagation is shown to directly lead to lateral spatial chirp with the spectrum on-axis substantially bluer than the overall energy spectrum. The two-transverse $+$ one-temporal dimensional profile of terahertz subcycle pulsed beams from organic crystals DSTMS and OH1 are measured and compared with observed spatiospectral correlations consistent with the model.

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

To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging. 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. 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. 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.

PACO ASDI: an algorithm for exoplanet detection and characterization in direct imaging with integral field spectrographs

Context.Exoplanet detection and characterization by direct imaging both rely on sophisticated instruments (adaptive optics and coronagraph) and adequate data processing methods. Angular and spectral differential imaging (ASDI) combines observations at different times and a range of wavelengths in order to separate the residual signal from the host star and the signal of interest corresponding to off-axis sources.Aims.Very high contrast detection is only possible with an accurate modeling of those two components, in particular of the background due to stellar leakages of the host star masked out by the coronagraph. Beyond the detection of point-like sources in the field of view, it is also essential to characterize the detection in terms of statistical significance and astrometry and to estimate the source spectrum.Methods.We extend our recent methodPACO, based on local learning of patch covariances, in order to capture the spectral and temporal fluctuations of background structures. From this statistical modeling, we build a detection algorithm and a spectrum estimation method:PACO ASDI. The modeling of spectral correlations proves useful both in reducing detection artifacts and obtaining accurate statistical guarantees (detection thresholds and photometry confidence intervals).Results.An analysis of several ASDI datasets from the VLT/SPHERE-IFS instrument shows thatPACO ASDIproduces very clean detection maps, for which setting a detection threshold is statistically reliable. Compared to other algorithms used routinely to exploit the scientific results of SPHERE-IFS, sensitivity is improved and many false detections can be avoided. Spectrally smoothed spectra are also produced byPACO ASDI. The analysis of datasets with injected fake planets validates the recovered spectra and the computed confidence intervals.Conclusions.PACO ASDIis a high-contrast processing algorithm accounting for the spatio-spectral correlations of the data to produce statistically-grounded detection maps and reliable spectral estimations. Point source detections, photometric and astrometric characterizations are fully automatized.

3-D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising.

In this article, we propose an alternating directional 3-D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge-structural spatiospectral correlation and global correlation along spectrum (GCS). Specifically, 3-D convolution is utilized to extract structural spatiospectral correlation in an HSI, while a quasi-recurrent pooling function is employed to capture the GCS. Moreover, the alternating directional structure is introduced to eliminate the causal dependence with no additional computation cost. The proposed model is capable of modeling spatiospectral dependence while preserving the flexibility toward HSIs with an arbitrary number of bands. Extensive experiments on HSI denoising demonstrate significant improvement over the state-of-the-art under various noise settings, in terms of both restoration accuracy and computation time. Our code is available at https://github.com/Vandermode/QRNN3D.

Dynamic hyperpolarized 13 C MR spectroscopic imaging using SPICE in mouse kidney at 9.4 T.

This study aims to investigate the feasibility of dynamic hyperpolarized 13 C MR spectroscopic imaging (MRSI) using the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) technique and an estimation of the spatially resolved conversion constant rate (kpl ). An acquisition scheme comprising a single training dataset and several imaging datasets was proposed considering hyperpolarized 13 C circumstances. The feasibility and advantage of the scheme were investigated in two parts: (a) consistency of spectral basis over time and (b) accuracy of the estimated kpl . The simulations and in vivo experiments support accurate kpl estimation with consistent spectral bases. The proposed method was implemented in an enzyme phantom and via in vivo experiments. In the enzyme phantom experiments, spatially resolved homogeneous kpl maps were observed. In the in vivo experiments, normal diet (ND) mice and high-fat diet (HFD) mice had kpl (s-1 ) values of medullar (ND: 0.0119 ± 0.0022, HFD: 0.0195 ± 0.0005) and cortical (ND: 0.0148 ±0.0023, HFD: 0.0224 ±0.0054) regions which were higher than vascular (ND: 0.0087 ±0.0013, HFD: 0.0132 ±0.0050) regions. In particular, the kpl value in the medullar region exhibited a significant difference between the two diet groups. In summary, the feasibility of using modified SPICE for dynamic hyperpolarized 13 C MRSI was demonstrated via simulations and in vivo experiments. The consistency of spectral bases over time and the accuracy of the estimated kpl values validate the proposed acquisition scheme, which comprises only a single training dataset. The proposed method improved the spatial resolution of dynamic hyperpolarized 13 C MRSI, which could be used for kpl estimation using high signal-to-noise ratio spectral bases.

A multichannel subspace approach with signal presence probability for speech enhancement

For the last few decades, speech enhancement based on microphone arrays has primarily utilized prior information about system models, e.g., array geometry and source location. However, estimation of the time delay to align microphone inputs is largely affected by reverberation and microphone mismatch. Preprocessing time aligning, e.g., fixed beamforming (the first branch of the generalized sidelobe canceller), is not desirable in general applications. Recently, interest has shifted to linear filtering, which works with only second-order statistics of noisy input and estimated noise. This paper proposes a linear filter design based on a multichannel subspace approach for speech enhancement. The contribution of the proposed multichannel subspace methods is threefold. First, a linear filter is applied to the multichannel frequency domain using a spatiospectral correlation matrix. Next, three types of multichannel signal presence probability (MC-SPP) are derived in the subspace domain. Third, incorporating the MC-SPPs into the gain modification of the linear filter achieves further improved noise reduction performance. Of the gain modifications, the proposed gain modification with subspace probability related to the eigenvector corresponding to the maximum eigenvalue realized the best noise reduction performance. The evaluation on average improved the proposed subspace-based methods by approximately 4 dB in overall SNR while maintaining a similar cepstral distance measured over the minimum variance distortionless response with the state-of-the-art relative transfer function estimation in adverse noisy environments.

Superpixel based recursive least-squares method for lossless compression of hyperspectral images

Filtering based compression methods have become a popular research topic in lossless compression of hyperspectral images. Recursive least squares (RLS) based prediction methods provide better decorrelation performance among the filtering based methods. In this paper, two superpixel segmentation based RLS methods, namely SuperRLS and B-SuperRLS, are investigated for lossless compression of hyperspectral images. The proposed methods present a novel parallelization approach for RLS based prediction method. In the first step of SuperRLS, superpixel segmentation is applied to hyperspectral image. Afterwards, hyperspectral image is partitioned into multiple small regions according to the superpixel boundaries. Each region is predicted with RLS method in parallel, and prediction residuals are encoded via arithmetic encoder. Additionally, superpixel based prediction approach provides region of interest compression capability. B-SuperRLS, which is bimodal version of SuperRLS, evaluates both spectral and spatio-spectral correlations for prediction. The performance of the proposed methods are exhaustively analysed in terms of superpixel number, input vector length and number of parallel nodes, used in the prediction. Experimental results show that the proposed parallel architecture dramatically reduces the computation time, and achieves lower bit-rate performances among the state-of-the-art methods.

High resolution hyperpolarized 13 C MRSI using SPICE at 9.4T.

To test the feasibility of using the SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) technique, which uses the partial separability of spectroscopic data, for high resolution hyperpolarized (HP) 13 C spectroscopic imaging. Numerical simulations were performed to investigate the impact of transient HP signals on SPICE reconstruction. Furthermore, spectroscopic imaging exams from SPICE and conventional EPSI (echo-planar spectroscopic imaging) were simulated for comparison. For in vivo experiments, HP 13 C SPICE was performed in a mouse kidney by means of the injection of HP [1-13 C] pyruvate at 9.4T. The variation of lactate/pyruvate from the simulated SPICE was less than 4% under various factors that affect the transient HP signal, suggesting that the impact is negligible. We found that while HP 13 C EPSI was limited to the low signal-to-noise ratio (SNR) of lactate, these limitations were mitigated through HP 13 C SPICE, facilitating the improved SNR of lactate and the distinction of tissues. Acquisition of a high resolution HP 13 C spectroscopic image was possible for the in vivo experiments. With the fine structural information, the acquired image showed higher signal of pyruvate and lactate in the renal cortices than in the medullas, which is known to be attributed to higher activity of lactate dehydrogenase. The feasibility of HP 13 C SPICE was investigated. Simulation studies were conducted and in vivo experiments were performed in the mouse kidney at 9.4T. Results confirmed that a high resolution HP 13 C spectroscopic image with adequate spectral resolution can be obtained. Magn Reson Med 80:703-710, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Simultaneous QSM and metabolic imaging of the brain using SPICE.

To map brain metabolites and tissue magnetic susceptibility simultaneously using a single three-dimensional 1 H-MRSI acquisition without water suppression. The proposed technique builds on a subspace imaging method called spectroscopic imaging by exploiting spatiospectral correlation (SPICE), which enables ultrashort echo time (TE)/short pulse repetition time (TR) acquisitions for 1 H-MRSI without water suppression. This data acquisition scheme simultaneously captures both the spectral information of brain metabolites and the phase information of the water signals that is directly related to tissue magnetic susceptibility variations. In extending this scheme for simultaneous QSM and metabolic imaging, we increase k-space coverage by using dual density sparse sampling and ramp sampling to achieve spatial resolution often required by QSM, while maintaining a reasonable signal-to-noise ratio (SNR) for the spatiospectral data used for metabolite mapping. In data processing, we obtain high-quality QSM from the unsuppressed water signals by taking advantage of the larger number of echoes acquired and any available anatomical priors; metabolite spatiospectral distributions are reconstructed using a union-of-subspaces model. In vivo experimental results demonstrate that the proposed method can produce susceptibility maps at a resolution higher than 1.8 × 1.8 × 2.4 mm3 along with metabolite spatiospectral distributions at a nominal spatial resolution of 2.4 × 2.4 × 2.4 mm3 from a single 7-min MRSI scan. The estimated susceptibility values are consistent with those obtained using the conventional QSM method with 3D multi-echo gradient echo acquisitions. This article reports a new capability for simultaneous susceptibility mapping and metabolic imaging of the brain from a single 1 H-MRSI scan, which has potential for a wide range of applications. Magn Reson Med 79:13-21, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

PurposeTo develop data acquisition and image reconstruction methods to enable high‐resolution 1H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace‐based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation).Theory and MethodsSPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging /echo‐planar spectroscopic imaging sequence for two‐dimensional (2D) MRSI and a dual‐density, dual‐speed echo‐planar spectroscopic imaging sequence for three‐dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high‐resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge‐preserving regularization.ResultsPhantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high‐SNR spatiospectral distributions with an approximately 3 mm nominal in‐plane resolution from a 10‐min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in‐plane and 4 mm through‐plane resolution in about 25 min.ConclusionSpecial data acquisition and reconstruction methods have been developed for high‐resolution 1H‐MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE. Magn Reson Med 76:1059–1070, 2016. © 2015 Wiley Periodicals, Inc.

Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

This paper proposes a technique for reducing impulse noise from corrupted hyperspectral images. We exploit the spatiospectral correlation present in hyperspectral images to sparsify the datacube. Since impulse noise is sparse, denoising is framed as an l1-norm regularized l1-norm data fidelity minimization problem. We derive an efficient split Bregman-based algorithm to solve the same. Experiments on real datasets show that our proposed technique, when compared with state-of-the-art denoising algorithms, yields better results.

Improved image reconstruction for subspace-based spectroscopic imaging using non-quadratic regularization.

A new MR spectroscopic imaging method, called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has been recently proposed to enable high-resolution metabolic imaging with good SNR. A key problem within the SPICE framework is image reconstruction from a very noisy and sparsely sampled dataset. This paper addresses this problem by integrating the low-rank model used in SPICE reconstruction with a non-quadratic regularization. An efficient primal-dual based algorithm is described to solve the associated optimization problem. The proposed method has been validated using both simulation and phantom studies and is expected to enhance the unprecedented capability of SPICE for high-resolution metabolic imaging.

A subspace approach to high‐resolution spectroscopic imaging

To accelerate spectroscopic imaging using sparse sampling of (k,t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio. The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high-dimensional spectroscopic signals reside in a very low-dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high-resolution spatiospectral distributions with good signal-to-noise ratio. More specifically, a hybrid chemical shift imaging/echo-planar spectroscopic imaging pulse sequence is proposed for sparse sampling of (k,t)-space, and a low-rank model-based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction. The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two-dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal-to-noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal-to-noise ratio compared to the accelerated echo-planar spectroscopic imaging experiments. The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible.

Stochastic modelling of Rice fading channels with temporal, spatial and spectral correlation

A statistical characterisation of a small-scale fading mobile radio channel is presented and a fading simulator for the simulation of multiple input-multiple output multicarrier systems is introduced. The derivation of the equivalent lowpass channel as a function of time, transmit and receive antenna positions and subcarrier frequency, leads to a tapped delay-line model with time, space and frequency-selective taps. The taps are modelled as a sum of a deterministic line-of-sight or dominant scattered path and a zero-mean Gaussian part composed of a number of unresolvable scattered paths, and are therefore Rice fading. The Gaussian parts can have the desired temporal and spatiospectral correlations generated by time-correlation shaping filtering and a space-frequency correlation transformation, respectively. The structure and details of the simulator are presented, and the simulator is shown to achieve good accuracy while retaining a reasonable computational complexity.