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Accelerated Four-Dimensional Rosette J-resolved Spectroscopic Imaging (4D ROSE-JRESI) with semi-LASER Localization: A pilot study
Ajin Joy1, Uzay Emir2,3, Paul M. Macey4, and M. Albert Thomas1
1Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, 2School of Health Sciences, Purdue University, West Lafayette, IN, United States, 3Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States, 4School of Nursing and Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States

Synopsis

Keywords: Data Acquisition, Brain

Undersampling spatial and spectral dimensions is essential to achieve clinically feasible scan times in multi-dimensional spectroscopic imaging. Sampling pattern of rosette trajectory has higher incoherence than that of the other non-Cartesian trajectories like spiral and radial, and can achieve higher compressed-sensing reconstruction performance. While rosette spectroscopic imaging has been attempted for 2D (2 spatial+1 spectral) and 3D (3 spatial+1 spectral) spectroscopic imaging, it has thus far not been shown for J-resolved spectroscopic imaging. In this pilot study, we implemented a rosette echo-planar J-resolved spectroscopic imaging (ROSE-JRESI) sequence and studied the feasibility of high acceleration factors for clinically feasible runtimes.

Introduction

MR spectroscopy (MRS) is an efficient biochemical tool for non-invasively analyzing metabolite and lipid concentrations in human tissues (1-4). Compared to one dimensional spectrum, two-dimensional spectral variant resolves peak information along an additional spectral dimension which helps to disperse the spectrum better (1-4). However, acquisition of MRSI after adding the 2nd spectral encoding can increase the total acquisition time significantly. Even though k-space-weighted and average-weighted schemes have been used to shorten the total duration of MRSI, echo-planar spectroscopic imaging (EPSI) showed further acceleration of the total acquisition duration (5,6). Undersampling the spatial and 2nd spectral dimension is essential to achieve clinically feasible scan times. Compressed sensing (CS) based reconstruction techniques are known to be capable of recovering the signal depending on the signal sparsity and incoherent sampling patterns (7). However, non-cartesian J-resolved spectroscopic imaging has not been attempted so far. It has been reported that the sampling pattern of the rosette trajectory has higher incoherence than that of the other non-Cartesian trajectories like spiral and radial, and can thus achieve higher CS reconstruction performance (8). While rosette spectroscopic imaging has been attempted for 2D (2 spatial+1 spectral) and 3D (3 spatial+1 spectral) spectroscopic imaging (9-10), it has thus far not been shown for J-resolved spectroscopic imaging. In this pilot study, we implemented a four-dimensional (4D) rosette echo-planar J-resolved spectroscopic imaging (4D ROSE-JRESI) sequence and studied the feasibility of high acceleration factors for clinically feasible runtimes.

Materials and Methods

The 4D ROSE-JRESI sequence was designed based on the rosette trajectory for two spatial dimensions as described in (11) and was implemented on a Siemens 3T clinical scanner. Data from a brain phantom containing metabolites at physiological concentrations was acquired. A 68-year-old healthy volunteer was recruited with IRB approval for the acquisition of in vivo brain data. Phantom scans were acquired with a 32×32 matrix size, a 24×24×2cm3 slab using volumetric semi-LASER localization, TE=28.6ms, TR=1.5s, and a spectral width of 1250 Hz, spatially interleaved 16 petals, with 512 t2 points, 64 t1 points and 2 averages. It has been reported that number of petals = number of pixels in one dimension of reconstructed image is sufficient for adequate reconstruction (12). The phantom data were retrospectively undersampled at 4x (4x spatial (8 petals)), 8x (4x spatial and 2x t1 (32 t1 points)) and 12x (4x spatial and 3x t1 (22 t1 points)) to study the effect of higher undersampling. The volunteer scan was acquired with 8 petals and 22 non-uniformly sampled t1 points with decaying exponential sampling density, for a total of approx. 12x acceleration. All other parameters were kept the same as mentioned earlier. Time for one average was 4 minutes, 30 seconds including 4 dummy preparation scans and 8 minutes, 54 seconds for 2 averages. CS reconstruction using Perona-Malik (PM) (13-16) and non-uniform FFT (nuFFT) (17) was used to estimate the missing samples of k-space. ProFit quantitation was used to quantify the resulting spectra (18).

Results

Figure 1 shows the localization image, rosette trajectory with 16 petals, extracted voxel from reconstructed data at 2x, 4x, 8x and 12x acceleration factors and different metabolite maps at multiple acceleration factors. The 2D spectrum, fit and residual based on ProFit quantitation for the voxels in Fig. 1(c) are shown in Fig. 2 along with a bar chart showing comparison of metabolite ratios with respect to Creatine 3.0 (Cre3.0) across multiple voxels from different acceleration factors. In-vivo data was reconstructed using nuFFT based PM. Sagittal and axial localization images, as well as the reconstructed metabolite maps of Cre 3.0, Creatine 3.9 (Cre 3.9), myo-Inositol (mI), N-acetylaspartate (NAA), choline (Cho) and Glx (Glutamine + Glutamate) overlaid on the localization image are shown in Figure 3. A multi-voxel spectrum covering all the voxels within the VOI (white box in the localization image) is shown in Figure 4. An extracted spectrum from the reconstructed in-vivo data and its ProFit quantitation are shown in Figure 5.

Discussion

Rosette based J-resolved 4D spectroscopic imaging was implemented and tested using phantom and in-vivo datasets. The metabolite maps in Fig 1 shows reconstructed maps devoid without visible undersampling artifacts. Visible artifacts outside VOI was observed when only 8 petals were acquired corresponding to an 8x acceleration along kx and ky. It was observed that a better approach for 8x acceleration is 4x along spatial and 2x along t1 dimensions for reconstruction without any visible artefacts. Increasing acceleration beyond 12x tend to introduce more artifacts, most of the metabolite ratios stayed within relatively same range across different acceleration factors until 12x. Total NAA (tNAA), aspartate (Asp) and alanine (Ala) ratios where slightly overestimated while while mI, lactate (Lac) and Cre3.9 ratios were slightly underestimated at higher accelerations. Glx ratio on the other hand was slightly reduced at 8x while slightly increased at 12x as shown in Fig. 2.

Conclusion

Due to the higher incoherence level of sampling patterns based on rosette trajectories, rosette based spectroscopic imaging sequence has the potential for highly accelerated acquisitions. A rosette based 4D J-resolved Spectroscopic Imaging sequence was implemented and the feasibly of 12x acceleration is demonstrated in this work. However, further optimization and validation using a larger cohort of in-vivo datasets is needed.

Acknowledgements

Authors acknowledge grants support from National Institute of Health (5R21MH125349-02 and 5R01HL135562-04).

References

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Figures

Fig 1: (a) Axial localization image (b) 2-spatial rosette trajectory with 16 petals (c) voxels extracted from accelerated and CS reconstructed 4D ROSE-JRESI phantom data. (d) Metabolite maps from accelerated and reconstructed 4D ROSE-JRESI phantom data.

Fig 2: (a) bar chart showing comparison of metabolite ratios with respect to Creatine 3.0 (Cre3.0) across multiple voxels from different acceleration factors (b) 2D spectrum, fit and residual based on Profit quantitation for the voxels in Fig. 1(c). 16 petals with 64 t1 points were used for 2x, 8 petals with 64 t1 points were used for 4x, 8 petals with 32 t1 points were used for 8x, and 8 petals with 22 t1 points were used for 12x acceleration for a matrix size of 32×32.

Fig 3: Sagittal and axial localization images from 68-year-old healthy volunteer are shown in first and second panels from left to right in top row. Remaining panels show the reconstructed metabolite maps of Cre 3.0, Cre 3.9, mI, NAA, Cho and Glx overlaid on the localization image.

Fig 4: Multi-voxel spectrum covering all voxels within the VOI (white box in the localization image in Fig 3).

Fig 5: ProFit quantitation of an extracted spectrum from the reconstructed in-vivo data. Top left panel shows the extracted spectrum. Bottom left panel shows the result of ProFit fitting along the F1=0 point. Panel on the right side shows the result of ProFit algorithm depicting 2D spectrum, ifs fit and residual.

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)
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DOI: https://doi.org/10.58530/2023/4651