Abstract:A reconstruction framework for the separation of signal from pyruvate and its downstream metabolites is shown. This reconstruction eliminates the need to acquire additional calibration FID acquisition and allows acceleration through compressed sensing.
“…Parallel imaging has been used with acceleration factors of up to 3.75, requiring, however, multi‐channel array coils, which are not widely available on small‐animal imaging systems. As hyperpolarized imaging lacks background signal, the resulting images are inherently sparse and therefore attractive for compressed sensing reconstruction, where acceleration factors of up to 3.8 have been reported . Spatiotemporal correlations have been exploited using k – t principal component analysis (PCA) to accelerate dynamic acquisition by an effective factor of 3.5 .…”
Section: Introductionmentioning
confidence: 99%
“…As hyperpolarized imaging lacks background signal, the resulting images are inherently sparse and therefore attractive for compressed sensing reconstruction, where acceleration factors of up to 3.8 have been reported. 18,[21][22][23][24][25][26] Spatiotemporal correlations have been exploited using k-t principal component analysis (PCA) to accelerate dynamic acquisition by an effective factor of 3.5. 27 Despite these efforts, voxel volumes smaller than 8 μL have not been realized.…”
The purpose of this work was to increase the resolution of hyperpolarized metabolic imaging of the rat heart with accelerated imaging using k–t principal component analysis (k–t PCA) and k–t compressed sensing (k–t SPARSE). Fully sampled in vivo datasets were acquired from six healthy rats after the injection of hyperpolarized [1‐13C]pyruvate. Data were retrospectively undersampled and reconstructed with either k–t PCA or k–t SPARSE. Errors of signal–time curves of pyruvate, lactate and bicarbonate were determined to compare the two reconstruction algorithms for different undersampling factors R. Prospectively undersampled imaging at 1 × 1 × 3.5‐mm3 resolution was performed with both methods in the same animals and compared with the fully sampled acquisition. k–t SPARSE was found to perform better at R < 3, but was outperformed by k–t PCA at R ≥ 4. Prospectively undersampled data were successfully reconstructed with both k–t PCA and k–t SPARSE in all subjects. No significant difference between the undersampled and fully sampled data was found in terms of signal‐to‐noise ratio (SNR) performance and metabolic quantification. Accelerated imaging with both k–t PCA and k–t SPARSE allows an increase in resolution, thereby reducing the intravoxel dephasing of hyperpolarized metabolic imaging of the rat heart.
“…Parallel imaging has been used with acceleration factors of up to 3.75, requiring, however, multi‐channel array coils, which are not widely available on small‐animal imaging systems. As hyperpolarized imaging lacks background signal, the resulting images are inherently sparse and therefore attractive for compressed sensing reconstruction, where acceleration factors of up to 3.8 have been reported . Spatiotemporal correlations have been exploited using k – t principal component analysis (PCA) to accelerate dynamic acquisition by an effective factor of 3.5 .…”
Section: Introductionmentioning
confidence: 99%
“…As hyperpolarized imaging lacks background signal, the resulting images are inherently sparse and therefore attractive for compressed sensing reconstruction, where acceleration factors of up to 3.8 have been reported. 18,[21][22][23][24][25][26] Spatiotemporal correlations have been exploited using k-t principal component analysis (PCA) to accelerate dynamic acquisition by an effective factor of 3.5. 27 Despite these efforts, voxel volumes smaller than 8 μL have not been realized.…”
The purpose of this work was to increase the resolution of hyperpolarized metabolic imaging of the rat heart with accelerated imaging using k–t principal component analysis (k–t PCA) and k–t compressed sensing (k–t SPARSE). Fully sampled in vivo datasets were acquired from six healthy rats after the injection of hyperpolarized [1‐13C]pyruvate. Data were retrospectively undersampled and reconstructed with either k–t PCA or k–t SPARSE. Errors of signal–time curves of pyruvate, lactate and bicarbonate were determined to compare the two reconstruction algorithms for different undersampling factors R. Prospectively undersampled imaging at 1 × 1 × 3.5‐mm3 resolution was performed with both methods in the same animals and compared with the fully sampled acquisition. k–t SPARSE was found to perform better at R < 3, but was outperformed by k–t PCA at R ≥ 4. Prospectively undersampled data were successfully reconstructed with both k–t PCA and k–t SPARSE in all subjects. No significant difference between the undersampled and fully sampled data was found in terms of signal‐to‐noise ratio (SNR) performance and metabolic quantification. Accelerated imaging with both k–t PCA and k–t SPARSE allows an increase in resolution, thereby reducing the intravoxel dephasing of hyperpolarized metabolic imaging of the rat heart.
“…The reconstruction requires fewer echoes than an EPSI sequence but it demands prior knowledge of the location of the peaks in the NMR spectrum and a B 0 map to successfully separate the individual frequency components. The method has been successfully used with Cartesian trajectory gradient echo sequences for two-dimensional [135] and three-dimensional [136] single time-point imaging, spiral [137] and Echo-Planar Imaging (EPI) readout trajectories [138] for 2D dynamic imaging, and as a single-shot spin-echo sequence with multiple Cartesian readouts [122] for high resolution two-dimensional imaging. The latter approach showed an over 30-fold reduction in imaging time compared to a conventional single-point CSI sequence.…”
Until recently, molecular imaging using magnetic resonance (MR) has been limited by the modality’s low sensitivity, especially with non-proton nuclei. The advent of hyperpolarized (HP) MR overcomes this limitation by substantially enhancing the signal of certain biologically important probes through a process known as external nuclear polarization, enabling real-time assessment of tissue function and metabolism. The metabolic information obtained by HP MR imaging holds significant promise in the clinic, where it could play a critical role in disease diagnosis and therapeutic monitoring. This review will provide a comprehensive overview of the developments made in the field of hyperpolarized MR, including advancements in polarization techniques and delivery, probe development, pulse sequence optimization, characterization of healthy and diseased tissues, and the steps made towards clinical translation.
“…Compressed sensing has been recently demonstrated for hyperpolarized 13 C MRSI as a way to reduce the number of excitations needed per image, and therefore reduce the consumption of hyperpolarization per completed image [24][25][26] ; it is also regularly used to reconstruct 13 C images with higher spatial definition than would be available if using uniform k-space sampling. 24,27,28 Still, even with this aid, the fact that both the sensitivity and the image reconstruction demand a minimum number of values to be sampled close to the center of k-space, puts a limit to the overall acceleration that can be exercised in dDNP MRSI. An alternative way of reducing the number of phase encodes without losing spatial resolution arises if assuming that metabolic signals are spatially uniform within certain physiological compartments.…”
Purpose
The purpose of the study was to develop an approach for improving the resolution and sensitivity of hyperpolarized 13C MRSI based on a priori anatomical information derived from featured, water‐based 1H images.
Methods
A reconstruction algorithm exploiting 1H MRI for the redefinition of the 13C MRSI anatomies was developed, based on a modification of the spectroscopy with linear algebraic modeling (SLAM) principle. To enhance 13C spatial resolution and reduce spillover effects without compromising SNR, this model was extended by endowing it with a search allowing smooth variations in the 13C MR intensity within the targeted regions of interest.
Results
Experiments were performed in vitro on enzymatic solutions and in vivo on rodents, based on the administration of 13C‐enriched hyperpolarized pyruvate and urea. The spectral images reconstructed for these substrates and from metabolic products based on predefined 1H anatomical compartments using the new algorithm, compared favorably with those arising from conventional Fourier‐based analyses of the same data. The new approach also delivered reliable kinetic 13C results, for the kind of processes and timescales usually targeted by hyperpolarized MRSI.
Conclusion
A simple, flexible strategy is introduced to boost the sensitivity and resolution provided by hyperpolarized 13C MRSI, based on readily available 1H MR information.
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