Metabolic imaging of small animals using MRSI is an important tool for studying basic neurobiological processes and various disease models (e.g., cancer, neurological disorders and psychiatric diseases), and for developing new therapeutic procedures and their preclinical translation.¹ The development of advanced ultrahigh field animal scanners presents new opportunities in tackling the sensitivity and resolution challenges associated with animal imaging. However, due to the restricted tradeoffs of SNR, imaging speed, and resolution for MRSI, most metabolic studies still limit to single-voxel spectroscopy or slow phase-encoding-based chemical-shift imaging schemes.^2,3 We present here a new method to achieve fast, ultrahigh-resolution 1H-MRSI on a 9.4T animal system, using subspace-based data acquisition and image reconstruction strategies. The proposed method is able to produce high-SNR, spatially resolved metabolic profiles from the rat brain with 1x1x2mm³ nominal resolution in 16 minutes.

Data Acquisition

To achieve accelerated acquisition for high-resolution MRSI, we use a special hybrid sampling strategy. Specifically, we developed a dual-density, dual-speed echo-planar spectroscopic imaging (EPSI) sequence on a 9.4T Bruker system (ParaVision 6.0.1). The sequence contains a low-bandwidth (BW), low-resolution EPSI scan to generate a set of high-SNR and spectrally fully-sampled training data, and a high-BW, high-resolution EPSI scan to generate a set of imaging data. This strategy is based on a low-dimensional subspace model that exploits the spatiospectral correlation in the spatiospectral function of interest.⁴ More specifically, the training data is for determining the subspace structure while the imaging data is for final spatiospectral reconstruction (See Image Reconstruction).

A multislice encoding scheme was used to achieve large brain coverage and avoid the truncation effects for 3D encoding due to limited encodings along the slice direction. Short-TE spin-echo acquisition was used to further improve the SNR. In addition, since most spectral components of interest for 1H-MRSI reside in 0-4ppm, the center frequency was adjusted to 2.3ppm, which enables a reduction of the sampling bandwidth due to reduced spectral bandwidth requirement.

Image Reconstruction

The nuisance water and lipid signals were first removed from both the training and imaging data, using the method in 5. The nuisance-signal-removed data, denoted as $$$s(\mathbf{k},t)$$$, was then modeled as

\begin{eqnarray*} s(\mathbf{k},t) & = & \int\left(\sum_{l_{m}=1}^{L_{m}}u_{l_{m}}(\mathbf{r})v_{l_{m}}(t)+\sum_{l_{b}=1}^{L_{b}}u_{l_{b}}(\mathbf{r})v_{l_{b}}(t)\right)e^{-i2\pi\Delta f(\mathbf{r})t}e^{-i2\pi\mathbf{k}\mathbf{r}}d\mathbf{r} + \eta(\mathbf{k},t) \\ \end{eqnarray*}

where $$$\sum_{l_m=1}^{L_m}u_{l_m}(\mathbf{r})v_{l_m}(t)$$$ and $$$\sum_{l_{b}=1}^{L_{b}}u_{l_{b}}(\mathbf{r})v_{l_{b}}(t)$$$ are subspace models for the metabolite and baseline signals, respectively, $$$L_m$$$ and $$$L_b$$$ the corresponding model orders (typically a small number), $$$\Delta f(\mathbf{r})$$$ the B₀ map and $$$\eta(\mathbf{k},t)$$$ the noise. The baseline component was incorporated for very short-echo acquisitions. This model significantly reduces the degrees-of-freedom, making high-SNR and high-resolution reconstruction possible. Specifically, $$$\left\{v_{l_m}(.)\right\}$$$ and $$$\left\{v_{l_b}(.)\right\}$$$ were estimated from the training data^4,6. Image reconstruction was then done by estimating $$$\left\{u_{l_m}(.)\right\}$$$ and $$$\left\{u_{l_{b}}(.)\right\}$$$ using the following regularized least-squares formulation (with proper discretization)

\begin{eqnarray}\hat{\mathbf{U}}_{m},\hat{\mathbf{U}}_{b} & = & \arg\underset{\mathbf{U}_{m},\mathbf{U}_{b}}\min\left\Vert \mathbf{s}-\mathcal{F}_{\Omega}\left\{ \mathbf{B}\odot\left(\mathbf{U}_{m}\mathbf{V}_{m}+\mathbf{U}_{b}\mathbf{V}_{b}\right)\right\} \right\Vert _{2}^{2}\nonumber \\ & & +\lambda_{1}\left\Vert \mathbf{D}_{w}\mathbf{U}_{m}\right\Vert _{F}^{2}+\lambda_{2}\left\Vert \mathbf{U}_{b}\mathbf{V}_b\mathbf{F}\mathbf{D}_{f}\right\Vert _{F}^{2},\label{eq:recon}\\\nonumber \end{eqnarray}

where the first term measures data fidelity, $$$\mathbf{B}$$$ models the B₀ field inhomogeneity, $$$\mathcal{F}_{\Omega}$$$ is a Fourier encoding operator and $$$\mathbf{s}$$$ contains the imaging data. The regularization terms enforce spatial edge-preserving smoothness on the metabolite signal and spectral smoothness on the baseline signal, respectively. $$$\mathbf{D}_w$$$ and $$$\mathbf{D}_f$$$ perform spatial and spectral finite differences.

Data were acquired from a healthy rat brain (using a receive-only surface coil). The imaging parameters were: FOV = 40x40mm², slice thickness = 2mm, TR/TE = 2200/15ms, the training data matrix size = 16x16, the imaging data matrix size = 40x40 (1x1mm² nominal resolution), and water suppression BW = 180Hz. The readout BWs for the training and imaging data were 100 and 200kHz, respectively. The acquisition time for the imaging data was about 16min. The B₀ map was from an EPSI scan without water suppression. The experimental setup is illustrated in Fig. 1. Four slices were acquired. $$$L_m = 8$$$ and $$$L_b=4$$$ were used for reconstruction based on SVD analysis. Figure 2 shows representative spectra from three voxels. As can be seen, the proposed method is able to produce high-SNR spectra, resolving regional differences of the metabolites. The NAA and glutamate maps (Fig. 3) also have very high quality (e.g., identifying features such as ventricle where low signal is expected). A comparison to Fourier reconstruction of the original EPSI data further demonstrates the capability of the proposed method (Fig. 4).We have developed an ultrahigh-resolution 1H-MRSI method on a 9.4T animal scanner using subspace-driven acquisition and reconstruction. In vivo results demonstrate the feasibility of the proposed method in achieving fast, high-SNR and high-resolution metabolic imaging of the rat brain. With further validation and integration with spectral quantification, the proposed method should provide a useful tool for metabolic studies on various animal models.