Magnetic Resonance Spectroscopic Imaging Data Research Articles

Sensitivity encoding for fast (1) H MR spectroscopic imaging water reference acquisition.

Accurate and fast (1) H MR spectroscopic imaging (MRSI) water reference scans are important for absolute quantification of metabolites. However, the additional acquisition time required often precludes the water reference quantitation method for MRSI studies. Sensitivity encoding (SENSE) is a successful MR technique developed to reduce scan time. This study quantitatively assesses the accuracy of SENSE for water reference MRSI data acquisition, compared with the more commonly used reduced resolution technique. 2D MRSI water reference data were collected from a phantom and three volunteers at 3 Tesla for full acquisition (306 s); 2× reduced resolution (64 s) and SENSE R = 3 (56 s) scans. Water amplitudes were extracted using MRS quantitation software (TARQUIN). Intensity maps and Bland-Altman statistics were generated to assess the accuracy of the fast-MRSI techniques. The average mean and standard deviation of differences from the full acquisition were 2.1 ± 3.2% for SENSE and 10.3 ± 10.7% for the reduced resolution technique, demonstrating that SENSE acquisition is approximately three times more accurate than the reduced resolution technique. SENSE was shown to accurately reconstruct water reference data for the purposes of in vivo absolute metabolite quantification, offering significant improvement over the more commonly used reduced resolution technique.

Group Sparse Reconstruction of Multi-Dimensional Spectroscopic Imaging in Human Brain in vivo

Four-dimensional (4D) Magnetic Resonance Spectroscopic Imaging (MRSI) data combining 2 spatial and 2 spectral dimensions provides valuable biochemical information in vivo; however, its 20–40 min acquisition time is too long to be used for a clinical protocol. Data acquisition can be accelerated by non-uniformly under-sampling (NUS) the ky− t1 plane, but this causes artifacts in the spatial-spectral domain that must be removed by non-linear, iterative reconstruction. Previous work has demonstrated the feasibility of accelerating 4D MRSI data acquisition through NUS and iterative reconstruction using Compressed Sensing (CS), Total Variation (TV), and Maximum Entropy (MaxEnt) reconstruction. Group Sparse (GS) reconstruction is a variant of CS that exploits the structural sparsity of transform coefficients to achieve higher acceleration factors than traditional CS. In this article, we derive a solution to the GS reconstruction problem within the Split Bregman iterative framework that uses arbitrary transform grouping patterns of overlapping or non-overlapping groups. The 4D Echo-Planar Correlated Spectroscopic Imaging (EP-COSI) gray matter brain phantom and in vivo brain data are retrospectively under-sampled 2×, 4×, 6×, 8×, and 10___ and reconstructed using CS, TV, MaxEnt, and GS with overlapping or non-overlapping groups. Results show that GS reconstruction with overlapping groups outperformed the other reconstruction methods at each NUS rate for both phantom and in vivo data. These results can potentially reduce the scan time of a 4D EP-COSI brain scan from 40 min to under 5 min in vivo.

Short-echo three-dimensional H-1 MR spectroscopic imaging of patients with glioma at 7 Tesla for characterization of differences in metabolite levels.

The purpose of this study was to evaluate the feasibility of using a short echo time, three-dimensional H-1 magnetic resonance spectroscopic imaging (MRSI) sequence at 7 Tesla (T) to assess the metabolic signature of lesions for patients with glioma. Twenty-nine patients with glioma were studied. MRSI data were obtained using CHESS water suppression, spectrally selective adiabatic inversion-recovery pulses and automatically prescribed outer-volume-suppression for lipid suppression, and spin echo slice selection (echo time = 30 ms). An interleaved flyback echo-planar trajectory was applied to shorten the total acquisition time (∼10 min). Relative metabolite ratios were estimated in tumor and in normal-appearing white and gray matter (NAWM, GM). Levels of glutamine, myo-inositol, glycine, and glutathione relative to total creatine (tCr) were significantly increased in the T2 lesions for all tumor grades compared with those in the NAWM (P < 0.05), while N-acetyl aspartate to tCr were significantly decreased (P < 0.05). In grade 2 gliomas, level of total choline-containing-compounds to tCr was significantly increased (P = 0.0137), while glutamate to tCr was significantly reduced (P = 0.0012). The improved sensitivity of MRSI and the increased number of metabolites that can be evaluated using 7T MR scanners is of interest for evaluating patients with glioma. This study has successfully demonstrated the application of a short-echo spin-echo MRSI sequence to detect characteristic differences in regions of tumor versus normal appearing brain.

Fast water concentration mapping to normalize (1)H MR spectroscopic imaging.

To propose a fast and robust acquisition and post-processing pipeline that is time-compatible with clinical explorations to obtain a proton density (ρ) map used as a reference for metabolic map normalization. This allows inter-subject and inter-group comparisons of magnetic resonance spectroscopic imaging (MRSI) data and longitudinal follow-up for single subjects. A multi-echo T 2 (*) mapping sequence, the XEP sequence for B 1 (+) -mapping and Driven Equilibrium Single Pulse Observation of T 1-an optimized variable flip angle method for T 1 mapping used for both B 1 (-) -mapping and M 0 calculation-were used to determine correction factors leading to quantitative water proton density maps at 3T. Normalized metabolite maps were obtained on a phantom and nine healthy volunteers. To show the potential use of this technique at the individual level, we also explored one patient with low-grade glioma. Accurate ρ maps were obtained both on phantom and volunteers. After signal normalization with the generated ρ maps, metabolic concentrations determined by the present method differed from theory by <7% in the phantom and were in agreement with data from the literature for the healthy controls. Using these normalized metabolic values, it was possible to demonstrate in the patient with brain glioma, metabolic abnormalities in normalized N-acetyl aspartate, choline and creatine levels; illustrating the potential for direct use of this technique in clinical studies. The proposed combination of sequences provides a robust ρ map that can be used to normalize metabolic maps in clinical MRSI studies.

(1) H 2D MRSI tissue type analysis of gliomas.

To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation. Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent. Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging. LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.

MRS glucose mapping and PET joining forces: re‐evaluation of the lumped constant in the rat brain under isoflurane anaesthesia

Although numerous positron emission tomography (PET) studies with (18) F-fluoro-deoxyglucose (FDG) have reported quantitative results on cerebral glucose kinetics and consumption, there is a large variation between the absolute values found in the literature. One of the underlying causes is the inconsistent use of the lumped constants (LCs), the derivation of which is often based on multiple assumptions that render absolute numbers imprecise and errors hard to quantify. We combined a kinetic FDG-PET study with magnetic resonance spectroscopic imaging (MRSI) of glucose dynamics in Sprague-Dawley rats to obtain a more comprehensive view of brain glucose kinetics and determine a reliable value for the LC under isoflurane anaesthesia. Maps of Tmax /CMRglc derived from MRSI data and Tmax determined from PET kinetic modelling allowed to obtain an LC-independent CMRglc . The LC was estimated to range from 0.33 ± 0.07 in retrosplenial cortex to 0.44 ± 0.05 in hippocampus, yielding CMRglc between 62 ± 14 and 54 ± 11 μmol/min/100 g, respectively. These newly determined LCs for four distinct areas in the rat brain under isoflurane anaesthesia provide means of comparing the growing amount of FDG-PET data available from translational studies.

Real-time motion- and B0-correction for LASER-localized spiral-accelerated 3D-MRSI of the brain at 3 T

The full potential of magnetic resonance spectroscopic imaging (MRSI) is often limited by localization artifacts, motion-related artifacts, scanner instabilities, and long measurement times.Localized adiabatic selective refocusing (LASER) provides accurate B1-insensitive spatial excitation even at high magnetic fields. Spiral encoding accelerates MRSI acquisition, and thus, enables 3D-coverage without compromising spatial resolution. Real-time position- and shim/frequency-tracking using MR navigators correct motion- and scanner instability-related artifacts. Each of these three advanced MRI techniques provides superior MRSI data compared to commonly used methods.In this work, we integrated in a single pulse sequence these three promising approaches. Real-time correction of motion, shim, and frequency-drifts using volumetric dual-contrast echo planar imaging-based navigators were implemented in an MRSI sequence that uses low-power gradient modulated short-echo time LASER localization and time efficient spiral readouts, in order to provide fast and robust 3D-MRSI in the human brain at 3T.The proposed sequence was demonstrated to be insensitive to motion- and scanner drift-related degradations of MRSI data in both phantoms and volunteers. Motion and scanner drift artifacts were eliminated and excellent spectral quality was recovered in the presence of strong movement.Our results confirm the expected benefits of combining a spiral 3D-LASER-MRSI sequence with real-time correction. The new sequence provides accurate, fast, and robust 3D metabolic imaging of the human brain at 3T. This will further facilitate the use of 3D-MRSI for neuroscience and clinical applications.

Fast image reconstruction with L2-regularization.

We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; (i) Fast Quantitative Susceptibility Mapping (QSM), (ii) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and (iii) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality. The closed-form solution developed for regularized QSM allows processing of a three-dimensional volume under 5 s, the proposed lipid suppression algorithm takes under 1 s to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 s, all running in Matlab using a standard workstation. For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality.

Resolution enhancement as a key step towards clinical implementation of Padé-optimized magnetic resonance spectroscopy for diagnostic oncology

Our overall aim is for the full potential of magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI) to be realized in oncology. This requires mathematics, without which encoded MRS data are entirely uninterpretable. Mathematics based on the conventional approach, the fast Fourier transform (FFT), and ambiguous fittings of Fourier spectra cannot fulfill the rigorous demands of oncology. It is vital to go beyond the FFT and fitting, to obtain reliable quantitative information via MRS about the metabolic content of tissue. None of the available MRS fitting algorithms could provide the clinically needed information with certainty, namely, the metabolite concentrations. Our more advanced method, the fast Pade transform (FPT) is firmly established as a stable, high-resolution processor, with which metabolite concentrations are unequivocally generated for in vitro MRS data associated with prostate, breast and ovarian cancer. Validation of the FPT has also been performed for in vivo MRS of normal human brain from clinical magnetic resonance scanners (1.5 T) as well as from 4 to 7 T scanners. The FPT successfully handles major problems hindering more widespread clinical application of MRS and MRSI, such as separation of noise from signal, resolution of very dense spectra with multiplet resonances (prostate) and overlapping metabolite resonances (breast, brain). In the present paper, we focus upon the implications of resolution enhancement by the FPT. We conclude that the manner by which the FPT achieves its high resolution accuracy dictates a major reformulation of the concept of data acquisition by encoding a small number of short transient time signals to secure good signal-noise ratio. Pade-guided MRS has distinct clinical advantages in combining improved diagnostic accuracy of MRS with shorter examination times. This shortens turn-around time for patients, making MRS efficient and cost-effective. With Pade-based quantification plus FPT-guided encoding of MRS and MRSI data, MRS can become a reliable, cost-effective tool for diagnostics and various aspects of patient care within oncology.

JSIPRO – Analysis tool for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) involves a huge number of spectra to be processed and analyzed. Several tools enabling MRSI data processing have been developed and widely used. However, the processing programs primarily focus on sophisticated spectra processing and offer limited support for the analysis of the calculated spectroscopic maps. In this paper the jSIPRO (java Spectroscopic Imaging PROcessing) program is presented, which is a java-based graphical interface enabling post-processing, viewing, analysis and result reporting of MRSI data. Interactive graphical processing as well as protocol controlled batch processing are available in jSIPRO. jSIPRO does not contain a built-in fitting program. Instead, it makes use of fitting programs from third parties and manages the data flows. Currently, automatic spectra processing using LCModel, TARQUIN and jMRUI programs are supported. Concentration and error values, fitted spectra, metabolite images and various parametric maps can be viewed for each calculated dataset. Metabolite images can be exported in the DICOM format either for archiving purposes or for the use in neurosurgery navigation systems.

Neurochemical abnormalities in unmedicated bipolar depression and mania: A 2D 1H MRS investigation

The neurobiology and neurochemistry of bipolar disorder and its different phases are poorly understood. This study investigated metabolite abnormalities in both unmedicated bipolar depression as well as mania using 2D 1H magnetic resonance spectroscopy imaging (MRSI). MRSI data were obtained from 24 unmedicated bipolar disorder (BP) subjects (12 (hypo)manic (BPM)) and 12 depressed (BPD), and 20 closely matched healthy controls. 2D 1H MRSI data were collected from a 15-mm axial slice placed along the anterior commissure-posterior commissure (AC-PC) line to measure brain metabolites bilaterally in the thalamus and also the anterior and posterior cingulate cortex (ACC and PCC). Brain Lac/Cr levels were significantly increased in the BP group as a whole compared to healthy controls. Glutamate abnormalities varied across bipolar state as well as brain region: significantly increased Glx/Cr values were found in the left thalamus in BPD, but BPM had decreased Glu/Cr and Glx/Cr levels in the PCC when compared to healthy controls and decreased Glu/Cr levels even when compared to the BPD subjects group. The findings of the study point to state-related abnormalities of oxidative and glutamate metabolism in bipolar disorder.

Data-Driven MRSI Spectral Localization Via Low-Rank Component Analysis

Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

Abstract 2668: Revealing protein biomarkers in breast tumor models by combining MRSI and MSI.

Abstract The intensity of the total choline (tCho) signal in magnetic resonance spectroscopic imaging (MRSI) of tumors is heterogeneous. Magnetic resonance spectroscopy (MRS) studies have shown an elevation of phosphocholine (PCho) and total choline-containing metabolites (tCho) in breast cancer cells and tumors. In this study, we investigated the protein changes that occur in tumor regions with tCho in a breast tumor model by combining in vivo MRSI with ex vivo mass spectrometric imaging (MSI). MDA-MB-231-HRE-tdTomato breast tumor xenografts, which express the tdTomato fluorescent protein under the control of hypoxia response elements, were grown in mice. tCho was detected by H1 MRSI in the tumors in vivo. Each tumor was cryo-sectioned into fiducially marked slices to perform MSI. On-tissue tryptic digest followed by matrix-assisted laser desorption ionization (MALDI) MSI was performed to detect the tryptic peptides. MRSI, MSI images were fused by using the fiducial markers. The tCho volume was segmented into high-tCho-containing and low-tCho-containing areas. 2499 high-tCho-containing voxels and 2500 low-tCho-containing voxels were selected randomly, followed by principal component analysis-linear discriminant analysis (PCA-DA) to classify high- and low-tCho-containing voxels through the MALDI MSI data. The MALDI MSI data between m/z 1000 and 3000 was analyzed and the loading spectrum of discriminant function (DF) was sorted to select candidate m/z peaks, which mostly contributed to the differentiation between high- and low-tCho-containing voxels. The molecular m/z peak candidates were identified by searching a peptide database with the resulting m/z values as keywords. It was possible to separate low and high tCho spectra by this analysis. Peptides from hypoxia up-regulated protein 1 (HYOU1, m/z 1047.4) and peptides from ribosomal proteins such as m/z 1066.5, 1429.7 and 2824.4 displayed the highest peaks in the DF loading spectrum obtained from MALDI MSI data. tCho was previously found increased in the hypoxic regions of breast tumor xenografts. However, the HYOU1 peak at m/z 1047.4 was negatively correlated with tCho in MRSI data. HYOU1 is up-regulated by hypoxia and associated with poor prognosis in breast cancer. The relative decrease of HYOU1 protein in regions of high tCho is currently undergoing investigation in our lab. The molecular ions at m/z 1066.5, 1429.7 and 2824.4 were identified as ribosomal proteins that positively correlated with the tCho map in MRSI. This finding suggests that increased biosynthesis of ribosomal proteins may happen in high tCho regions in breast tumors. All other identified peptides are currently undergoing further validation by ion fragmentation using MSI-based MS/MS methods. By combining MRSI with MALDI MSI, we identified for the first time some specific proteins that are differentially expressed in breast tumor regions that contain high tCho. This work was supported by NIH R01 CA134695. Citation Format: Lu Jiang, Kamila Chughtai, Tiffany Greenwood, Gert Eijkel, Ron Heeren, Kristine Glunde. Revealing protein biomarkers in breast tumor models by combining MRSI and MSI. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2668. doi:10.1158/1538-7445.AM2013-2668

Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging

To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging (MRSI). A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvolution. A spiral MRSI sequence that heavily oversamples the central k-space regions is used to acquire the MRSI data. The spatial regularization term uses the spatial supports of brain and extracranial fat regions to recover the metabolite spectra and nuisance signals at two different resolutions. Specifically, the nuisance signals are recovered at the maximum resolution to minimize spectral leakage, while the point spread functions of metabolites are controlled to obtain acceptable signal-to-noise ratio. The comparisons of the algorithm against Tikhonov regularized reconstructions demonstrates considerably reduced line-shape distortions and improved metabolite maps. The proposed sparsity constrained spectral deconvolution scheme is effective in minimizing the line-shape distortions. The dual resolution reconstruction scheme is capable of minimizing spectral leakage artifacts.

A Polynomial Fitting Improved Bayesian Reconstruction Method for Whole Brain Volumetric MRSI Metabolite Images

In this paper, a polynomial fitting improved Bayesian approach is proposed for the reconstruction of volumetric metabolite images from long echo time (TE) whole brain proton magnetic resonance spectroscopic imaging (MRSI) data. The proposed algorithm uses a modified EM (expectation maximization) algorithm that takes into account the partial volume effects contained inside a thick slice MRSI. It incorporates high resolution volumetric magnetic resonance imaging (MRI) as a priori information. It further integrates the polynomial fitting method to smooth out artificial edges before the high resolution metabolite images are reconstructed. Our proposed reconstruction method has successfully extended our existing reconstruction of two dimensional (2D) metabolite images to 3D cases. The experimental results show that resolution enhanced volumetric metabolite images are reconstructed.

SIVIC: Open-Source, Standards-Based Software for DICOM MR Spectroscopy Workflows

Quantitative analysis of magnetic resonance spectroscopic imaging (MRSI) data provides maps of metabolic parameters that show promise for improving medical diagnosis and therapeutic monitoring. While anatomical images are routinely reconstructed on the scanner, formatted using the DICOM standard, and interpreted using PACS workstations, this is not the case for MRSI data. The evaluation of MRSI data is made more complex because files are typically encoded with vendor-specific file formats and there is a lack of standardized tools for reconstruction, processing, and visualization. SIVIC is a flexible open-source software framework and application suite that enables a complete scanner-to-PACS workflow for evaluation and interpretation of MRSI data. It supports conversion of vendor-specific formats into the DICOM MR spectroscopy (MRS) standard, provides modular and extensible reconstruction and analysis pipelines, and provides tools to support the unique visualization requirements associated with such data. Workflows are presented which demonstrate the routine use of SIVIC to support the acquisition, analysis, and delivery to PACS of clinical 1H MRSI datasets at UCSF.

Prostate 3T-MR spectroscopic imaging without an endorectal surface coil using the MLEV-PRESS sequence

The aim of this study was to determine to what extent we could identify the metabolism product peak using 3-T proton magnetic resonance spectroscopic imaging (MRSI) of the prostate gland in healthy volunteers by combining an external array coil with Malcolm Levitt composite-pulse decoupling sequence (MLEV)-point-resolved spatially localized spectroscopy (PRESS). MRSI data were obtained from the entire prostate gland in six healthy volunteers. The heights of the choline, citrate peaks and the standard deviation (SD) of the noise in each voxel were calculated. When the choline and/or citrate peak in a voxel exceeded 3 SD, the peak was clearly identified. The clear citrate peak rate in the peripheral zone (PZ) and the central gland (CG) were 78.8 and 70.3 %, respectively. The clear choline peak rate in the PZ and the CG were 55.4 and 44.9 %, respectively. In addition, the clear peak rates for both citrate and choline in the PZ and the CG were 51.8 and 38.6 %. Therefore, in the entire prostate gland, 75.2 % had a clear citrate peak, 51.1 % had a clear choline peak, and 46.3 % had both citrate and choline peaks. The citrate peak was clearly detected in 75.2 % of the voxels by this technique.

MR spectroscopic imaging: Principles and recent advances

MR spectroscopic imaging (MRSI) has become a valuable tool for quantifying metabolic abnormalities in human brain, prostate, breast and other organs. It is used in routine clinical imaging, particularly for cancer assessment, and in clinical research applications. This article describes basic principles of commonly used MRSI data acquisition and analysis methods and their impact on clinical applications. It also highlights technical advances, such as parallel imaging and newer high-speed MRSI approaches that are becoming viable alternatives to conventional MRSI methods. Although the main focus is on (1) H-MRSI, the principles described are applicable to other MR-compatible nuclei. This review of the state-of-the-art in MRSI methodology provides a framework for critically assessing the clinical utility of MRSI and for defining future technical development that is expected to lead to increased clinical use of MRSI. Future technical development will likely focus on ultra-high field MRI scanners, novel hyperpolarized contrast agents using metabolically active compounds, and ultra-fast MRSI techniques because these technologies offer unprecedented sensitivity and specificity for probing tissue metabolic status and dynamics.

Unsupervised Nosologic Imaging for Glioma Diagnosis

In this letter a novel approach to create nosologic images of the brain using magnetic resonance spectroscopic imaging (MRSI) data in an unsupervised way is presented. Different tissue patterns are identified from the MRSI data using nonnegative matrix factorization and are then coded as different primary colors (i.e. red, green, and blue) in an RGB image, so that mixed tissue regions are automatically visualized as mixtures of primary colors. The approach is useful in assisting glioma diagnosis, where several tissue patterns such as normal, tumor, and necrotic tissue can be present in the same voxel/spectrum. Error-maps based on linear least squares estimation are computed for each nosologic image to provide additional reliability information, which may help clinicians in decision making. Tests on in vivo MRSI data show the potential of this new approach.

Rapid sequential injections of hyperpolarized [1-13C]pyruvate in vivo using a sub-kelvin, multi-sample DNP polarizer

The development of hyperpolarized technology utilizing dynamic nuclear polarization (DNP) has enabled the rapid measurement of 13C metabolism in vivo with very high SNR. However, with traditional DNP equipment, consecutive injections of a hyperpolarized compound in an animal have been subject to a practical minimum time between injections governed by the polarization build-up time, which is on the order of an hour for [1-13C]pyruvate. This has precluded the monitoring of metabolic changes occurring on a faster time scale. In this study, we demonstrated the ability to acquire in vivo dynamic magnetic resonance spectroscopy (MRS) and 3D magnetic resonance spectroscopic imaging (MRSI) data in normal rats with a 5min interval between injections of hyperpolarized [1-13C]pyruvate using a prototype, sub-Kelvin dynamic nuclear polarizer with the capability to simultaneously polarize up to 4 samples and dissolve them in rapid succession. There were minimal perturbations in the hyperpolarized spectra as a result of the multiple injections, suggesting that such an approach would not confound the investigation of metabolism occurring on this time scale. As an initial demonstration of the application of this technology and approach for monitoring rapid changes in metabolism as a result of a physiological intervention, we investigated the pharmacodynamics of the anti-cancer agent dichloroacetate (DCA), collecting hyperpolarized data before administration of DCA, 1min after administration, and 6min after administration. Dramatic increases in 13C-bicarbonate were detected just 1min (as well as 6min) after DCA administration.