Purpose

To implement a 5D (three spatial + two spectral) correlated spectroscopic imaging sequence for application to human calf.

Theory and methods

Nonuniform sampling was applied across the two phase encoded dimensions and the indirect spectral dimension of an echo planar-correlated spectroscopic imaging sequence. Reconstruction was applied that minimized the group sparse mixed ℓ2,1-norm of the data. Multichannel data were compressed using a sensitivity map-based approach with a spatially dependent transform matrix and utilized the self-sparsity of the individual coil images to simplify the reconstruction.

Results

Single channel data with 8× and 16× undersampling are shown in the calf of a diabetic patient. A 15-channel scan with 12× undersampling of a healthy volunteer was reconstructed using 5 virtual channels and compared to a fully sampled single slice scan. Group sparse reconstruction faithfully reconstructs the lipid cross peaks much better than ℓ1 minimization.

Conclusion

COSY spectra can be acquired over a 3D spatial volume with scan time under 15 min using echo planar readout with highly undersampled data and group sparse reconstruction.

The four-dimensional (4D) echo-planar correlated spectroscopic imaging (EP-COSI) sequence allows for the simultaneous acquisition of two spatial (ky, kx) and two spectral (t2, t1) dimensions in vivo in a single recording. However, its scan time is directly proportional to the number of increments in the ky and t1 dimensions, and a single scan can take 20–40 min using typical parameters, which is too long to be used for a routine clinical protocol. The present work describes efforts to accelerate EP-COSI data acquisition by application of non-uniform under-sampling (NUS) to the ky–t1 plane of simulated and in vivo EP-COSI datasets then reconstructing missing samples using maximum entropy (MaxEnt) and compressed sensing (CS). Both reconstruction problems were solved using the Cambridge algorithm, which offers many workflow improvements over other l1-norm solvers. Reconstructions of retrospectively under-sampled simulated data demonstrate that the MaxEnt and CS reconstructions successfully restore data fidelity at signal-to-noise ratios (SNRs) from 4 to 20 and 5× to 1.25× NUS. Retrospectively and prospectively 4× under-sampled 4D EP-COSI in vivo datasets show that both reconstruction methods successfully remove NUS artifacts; however, MaxEnt provides reconstructions equal to or better than CS. Our results show that NUS combined with iterative reconstruction can reduce 4D EP-COSI scan times by 75% to a clinically viable 5 min in vivo, with MaxEnt being the preferred method.

Water-suppressed MRS acquisition techniques have been the standard MRS approach used in research and for clinical scanning to date. The acquisition of a non-water-suppressed MRS spectrum is used for artefact correction, reconstruction of phased-array coil data and metabolite quantification. Here, a two-scan metabolite-cycling magnetic resonance spectroscopic imaging (MRSI) scheme that does not use water suppression is demonstrated and evaluated. Specifically, the feasibility of acquiring and quantifying short-echo (TE  = 14 ms), two-dimensional stimulated echo acquisition mode (STEAM) MRSI spectra in the motor cortex is demonstrated on a 3 T MRI system. The increase in measurement time from the metabolite-cycling is counterbalanced by a time-efficient concentric ring k-space trajectory. To validate the technique, water-suppressed MRSI acquisitions were also performed for comparison. The proposed non-water-suppressed metabolite-cycling MRSI technique was tested for detection and correction of resonance frequency drifts due to subject motion and/or hardware instability, and the feasibility of high-resolution metabolic mapping over a whole brain slice was assessed. Our results show that the metabolite spectra and estimated concentrations are in agreement between non-water-suppressed and water-suppressed techniques. The achieved spectral quality, signal-to-noise ratio (SNR) > 20 and linewidth <7 Hz allowed reliable metabolic mapping of five major brain metabolites in the motor cortex with an in-plane resolution of 10 × 10 mm2 in 8 min and with a Cramér-Rao lower bound of less than 20% using LCModel analysis. In addition, the high SNR of the water peak of the non-water-suppressed technique enabled voxel-wise single-scan frequency, phase and eddy current correction. These findings demonstrate that our non-water-suppressed metabolite-cycling MRSI technique can perform robustly on 3 T MRI systems and within a clinically feasible acquisition time.

The use of spin-echoes has been employed in an Echo-Planar Spectroscopic Imaging (EPSI) sequence to collect multiple phase encoded lines within a single TR in a Multi-Echo-based Echo-Planar Spectroscopic Imaging technique (MEEPSI). Despite the T₂ dependence on the amplitude of the spin-echoes, the Full Width at Half Maximum (FWHM) of the derived multi-echo Point Spread Function (PSF) is shown to decrease, indicating an improved overall spatial resolution without requiring any additional scan time. The improved spatial resolution is demonstrated in the one-dimensional (1D) spatial profiles of the N-Acetyl Aspartate (NAA) singlet along the phase encode dimension in a gray matter phantom. Although the improved spatial resolution comes at the expense of spectral resolution, it is shown in vivo that peak broadening due to T₂^* decay is more significant than the loss of resolution from using spin-echoes and therefore does not affect the ability to quantify metabolites using the LCModel fitting algorithm.

Purpose

To implement an accelerated five-dimensional (5D) echo-planar J-resolved spectroscopic imaging sequence combining 3 spatial and 2 spectral encoding dimensions and to apply the sequence in human brain.

Methods

An echo planar readout was used to acquire a single spatial and a single spectral dimension during one readout. Nonuniform sampling was applied to the two phase-encoded spatial directions and the indirect spectral dimension. Nonlinear reconstruction was used to minimize the ℓ1-norm or the total variation and included a spectral mask to enhance sparsity. Retrospective reconstructions at multiple undersamplings were performed in phantom. Ten healthy volunteers were scanned with 8× undersampling and compared to a fully sampled single slice scan.

Results

Retrospective reconstruction of fully sampled phantom data showed excellent quality at 4×, 8×, 12×, and 16× undersampling using either reconstruction method. Reconstruction of prospectively acquired in vivo scans with 8× undersampling showed excellent quality in the occipito-parietal lobes and good quality in the frontal lobe, consistent with the fully sampled single slice scan.

Conclusion

By utilizing nonuniform sampling with nonlinear reconstruction, 2D J-resolved spectra can be acquired over a 3D spatial volume with a total scan time of 20 min, which is reasonable for in vivo studies.

BACKGROUND: Although susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*-weighted magnitude images is therefore advantageous. PURPOSE: To create synthetic SWI images from clinical T2*-weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation-associated CMBs. STUDY TYPE: Retrospective. POPULATION: A total of 145 adults (87 males/58 females; 43.9 years old) with radiation-associated CMBs were used to train (16,093 patches/121 patients), validate (484 patches/4 patients), and test (2420 patches/20 patients) our networks. FIELD STRENGTH/SEQUENCE: 3D T2*-weighted, gradient-echo acquired at 3 T. ASSESSMENT: Structural similarity index (SSIM), peak signal-to-noise-ratio (PSNR), normalized mean-squared-error (nMSE), CMB counts, and line profiles were compared among magnitude, original SWI, and synthetic SWI images. Three blinded raters (J.E.V.M., M.A.M., B.B. with 8-, 6-, and 4-years of experience, respectively) independently rated and classified test-set images. STATISTICAL TESTS: Kruskall-Wallis and Wilcoxon signed-rank tests were used to compare SSIM, PSNR, nMSE, and CMB counts among magnitude, original SWI, and predicted synthetic SWI images. Intraclass correlation assessed interrater variability. P values <0.005 were considered statistically significant. RESULTS: SSIM values of the predicted vs. original SWI (0.972, 0.995, 0.9864) were statistically significantly higher than that of the magnitude vs. original SWI (0.970, 0.994, 0.9861) for whole brain, vascular structures, and brain tissue regions, respectively; 67% (19/28) CMBs detected on original SWI images were also detected on the predicted SWI, whereas only 10 (36%) were detected on magnitude images. Overall image quality was similar between the synthetic and original SWI images, with less artifacts on the former. CONCLUSIONS: This study demonstrated that deep learning can increase the susceptibility contrast present in neurovasculature and CMBs on T2*-weighted magnitude images, without residual susceptibility-induced artifacts. This may be useful for more accurately estimating CMB burden from magnitude images alone. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

It has been shown that density-weighted (DW) k-space sampling with spiral and conventional phase encoding trajectories reduces spatial side lobes in magnetic resonance spectroscopic imaging (MRSI). In this study, we propose a new concentric ring trajectory (CRT) for DW-MRSI that samples k-space with a density that is proportional to a spatial, isotropic Hanning window. The properties of two different DW-CRTs were compared against a radially equidistant (RE) CRT and an echo-planar spectroscopic imaging (EPSI) trajectory in simulations, phantoms and in vivo experiments. These experiments, conducted at 7 T with a fixed nominal voxel size and matched acquisition times, revealed that the two DW-CRT designs improved the shape of the spatial response function by suppressing side lobes, also resulting in improved signal-to-noise ratio (SNR). High-quality spectra were acquired for all trajectories from a specific region of interest in the motor cortex with an in-plane resolution of 7.5 × 7.5 mm2 in 8 min 3 s. Due to hardware limitations, high-spatial-resolution spectra with an in-plane resolution of 5 × 5 mm2 and an acquisition time of 12 min 48 s were acquired only for the RE and one of the DW-CRT trajectories and not for EPSI. For all phantom and in vivo experiments, DW-CRTs resulted in the highest SNR. The achieved in vivo spectral quality of the DW-CRT method allowed for reliable metabolic mapping of eight metabolites including N-acetylaspartylglutamate, γ-aminobutyric acid and glutathione with Cramér-Rao lower bounds below 50%, using an LCModel analysis. Finally, high-quality metabolic mapping of a whole brain slice using DW-CRT was achieved with a high in-plane resolution of 5 × 5 mm2 in a healthy subject. These findings demonstrate that our DW-CRT MRSI technique can perform robustly on MRI systems and within a clinically feasible acquisition time.

The overlap of metabolites is a major limitation in one-dimensional (1D) spectral-based single-voxel MRS and multivoxel-based MRSI. By combining echo planar spectroscopic imaging (EPSI) with a two-dimensional (2D) J-resolved spectroscopic (JPRESS) sequence, 2D spectra can be recorded in multiple locations in a single slice of prostate using four-dimensional (4D) echo planar J-resolved spectroscopic imaging (EP-JRESI). The goal of the present work was to validate two different non-linear reconstruction methods independently using compressed sensing-based 4D EP-JRESI in prostate cancer (PCa): maximum entropy (MaxEnt) and total variation (TV). Twenty-two patients with PCa with a mean age of 63.8 years (range, 46-79 years) were investigated in this study. A 4D non-uniformly undersampled (NUS) EP-JRESI sequence was implemented on a Siemens 3-T MRI scanner. The NUS data were reconstructed using two non-linear reconstruction methods, namely MaxEnt and TV. Using both TV and MaxEnt reconstruction methods, the following observations were made in cancerous compared with non-cancerous locations: (i) higher mean (choline + creatine)/citrate metabolite ratios; (ii) increased levels of (choline + creatine)/spermine and (choline + creatine)/myo-inositol; and (iii) decreased levels of (choline + creatine)/(glutamine + glutamate). We have shown that it is possible to accelerate the 4D EP-JRESI sequence by four times and that the data can be reliably reconstructed using the TV and MaxEnt methods. The total acquisition duration was less than 13 min and we were able to detect and quantify several metabolites.