Combined imaging of potassium and sodium in human skeletal muscle tissue at 7 T.

PURPOSE: To validate the feasibility of quantitative combined potassium (39 K) and sodium (23 Na) MRI in human calf muscle tissue, as well as to evaluate the reproducibility of the apparent tissue potassium concentration (aTPC) and apparent tissue sodium concentration (aTSC) determination in healthy muscle tissue. METHODS: Quantitative 23 Na and 39 K MRI acquisition protocols were implemented on a 7 T MR system. A double-resonant 23 Na/39 K birdcage RF coil was used. Measurements of human lower leg were performed in a total acquisition time of TANa = 10:54 min/TAK = 8:06 min and using a nominal spatial resolution of 2.5 × 2.5 × 15 mm3 /7.5 × 7.5 × 30 mm3 for 23 Na/39 K MRI. Two aTSC and aTPC examinations in muscle tissue were performed during the same day on 10 healthy subjects. RESULTS: The proposed acquisition and postprocessing workflow for 23 Na and 39 K MRI data sets provided reproducible aTSC and aTPC measurements. In human calf muscle tissue, the coefficient of variation between scan and re-scan was 5.7% for both aTSC and aTPC determination. Overall, mean values of aTSC = (17 ± 1) mM and aTPC = (85 ± 5) mM were measured. Moreover, for 39 K in calf muscle tissue, T2∗ components of T2f∗ = (1.2 ± 0.2) ms and T2s∗ = (7.9 ± 0.9) ms, as well as a residual quadrupolar interaction of ωq¯ = (143 ± 17) Hz, were determined. The fraction of the fast component was f = (58 ± 4)%. CONCLUSION: Using the presented measurement and postprocessing approach, a reproducible aTSC and aTPC determination using 23 Na and 39 K MRI at 7 T in human skeletal muscle tissue is feasible in clinically acceptable acquisition durations.

Towards accelerated quantitative sodium MRI at 7 T in the skeletal muscle: Comparison of anisotropic acquisition- and compressed sensing techniques.

PURPOSE: To compare three anisotropic acquisition schemes and three compressed sensing (CS) approaches for accelerated tissue sodium concentration (TSC) quantification using 23Na MRI at 7 T. MATERIALS AND METHODS: Three anisotropic 3D-radial acquisition sequences were evaluated using simulations, phantom- and in vivo TSC measurements: An anisotropic density-adapted 3D-radial sequence (3DPR-C), a 3D acquisition-weighted density-adapted stack-of-stars sampling scheme (SOS) and a SOS approach with golden-ratio rotation (SOS-GR). Eight healthy volunteers were examined at a 7 Tesla MRI system. TSC measurements of the calf were conducted with a nominal spatial resolution of Δx = (3.0 × 3.0 × 15.0) mm3 and a field of view of (156.0 × 156.0 × 240.0) mm3 for multiple undersampling factors (USF). Three CS reconstructions were evaluated: Total variation CS (TV-CS), 3D dictionary-learning compressed sensing (3D-DLCS) and TV-CS with a block matching prior (TV-BL-CS). Results of the simulations and measurements were compared to a simulated ground truth (GT) or a fully sampled reference measurement (FS), respectively. The deviation of the mean TSC evaluated in multiple ROI (mEGT/FS) and the normalized root-mean-squared error (NRMSE) for simulations were evaluated for CS and NUFFT reconstructions. RESULTS: In simulations, the SOS-GR yielded the lowest NRMSE and mEGT (< 4%) with NUFFT for an acquisition time (TA) of less than 2 min. CS further improved the results. In simulations and measurements, the best TSC quantification results were obtained with 3D-DLCS and SOS-GR (lowest NRMSE, mEGT < 2.6% in simulations, mEGT < 10.7% for phantom measurements and mEFS < 6% in vivo) with an USF = 4.1 (TA < 2 min). TV-CS showed no or only slight improvements to NUFFT. The results of TV-BL-CS were similar to 3D-DLCS. DISCUSSION: The TA for TSC measurements could be reduced to less than 2 min by using adapted sequences such as SOS-GR and CS reconstruction approaches such as 3D-DLCS or TV-BL-CS, while the quantitative accuracy stays comparable to a fully sampled NUFFT reconstruction (approx. 8 min TA). In future, the lower TA could improve clinical applicability of TSC measurements.

Compressed sensing and the use of phased array coils in 23Na MRI: a comparison of a SENSE-based and an individually combined multi-channel reconstruction.

PURPOSE: To implement and to evaluate a compressed sensing (CS) reconstruction algorithm based on the sensitivity encoding (SENSE) combination scheme (CS-SENSE), used to reconstruct sodium magnetic resonance imaging (23Na MRI) multi-channel breast data sets. METHODS: In a simulation study, the CS-SENSE algorithm was tested and optimized by evaluating the structural similarity (SSIM) and the normalized root-mean-square error (NRMSE) for different regularizations and different undersampling factors (USF=1.8/3.6/7.2/14.4). Subsequently, the algorithm was applied to data from in vivo measurements of the healthy female breast (n=3) acquired at 7T. Moreover, the proposed CS-SENSE algorithm was compared to a previously published CS algorithm (CS-IND). RESULTS: The CS-SENSE reconstruction leads to an increased image quality for all undersampling factors and employed regularizations. Especially if a simple 2nd order total variation is chosen as sparsity transformation, the CS-SENSE reconstruction increases the image quality of highly undersampled data sets (CS-SENSE: SSIMUSF=7.2=0.234, NRMSEUSF=7.2=0.491 vs. CS-IND: SSIMUSF=7.2=0.201, NRMSEUSF=7.2=0.506). CONCLUSION: The CS-SENSE reconstruction supersedes the need of CS weighting factors for each channel as well as a method to combine single channel data. The CS-SENSE algorithm can be used to reconstruct undersampled data sets with increased image quality. This can be exploited to reduce total acquisition times in 23Na MRI.

Correction to: Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing.

The original version of this article unfortunately contained a mistake. Title was incorrect.

Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing.

OBJECTIVE: To accelerate tissue sodium concentration (TSC) quantification of skeletal muscle using 23Na MRI and 3D dictionary-learning compressed sensing (3D-DLCS). MATERIALS AND METHODS: Simulations and in vivo 23Na MRI examinations of calf muscle were performed with a nominal spatial resolution of [Formula: see text]. Fully sampled and three undersampled 23Na MRI data sets (undersampling factors (USF) = 3, 4.4, 6.7) were evaluated. Ten healthy subjects were examined on a 3 Tesla MRI system. Results of the simulation study and the in vivo measurements were compared to the ground truth (GT) and the fully sampled fast Fourier transform (NUFFT) reconstruction, respectively. RESULTS: Reconstruction results of simulated data with optimized 3D-DLCS yielded a lower deviation (< 4%) from the GT than results of the NUFFT reconstruction (> 5%) and a lower standard deviation (SD). For in vivo measurements, a TSC of [Formula: see text] was observed. The mean deviation from the reference is lower for the undersampled 3D-DLCS reconstructions (3.4%) than for NUFFT reconstructions (4.6%). SD is reduced using 3D-DLCS. Compared to a fully sampled NUFFT reconstruction, acquisition time could be reduced by a factor of 4.4 while maintaining similar quantitative accuracy. DISCUSSION: The optimized 3D-DLCS reconstruction enables accelerated TSC measurements with high quantification accuracy.

Comparison of optimized intensity correction methods for 23Na MRI of the human brain using a 32-channel phased array coil at 7 Tesla.

PURPOSE: To correct for the non-homogeneous receive profile of a phased array head coil in sodium magnetic resonance imaging (23Na MRI). METHODS: 23Na MRI of the human brain (n = 8) was conducted on a 7T MR system using a dual-tuned quadrature 1H/23Na transmit/receive birdcage coil, equipped with a 32-channel receive-only array. To correct the inhomogeneous receive profile four different methods were applied: (1) the uncorrected phased array image and an additionally acquired birdcage image as reference image were low-pass filtered and divided by each other. (2) The second method substituted the reference image by a support region. (3) By averaging the individually calculated receive profiles, a universal sensitivity map was obtained and applied. (4) The receive profile was determined by a pre-scanned large uniform phantom. The calculation of the sensitivity maps was optimized in a simulation study using the normalized root-mean-square error (NRMSE). All methods were evaluated in phantom measurements and finally applied to in vivo 23Na MRI data sets. The in vivo measurements were partial volume corrected and for further evaluation the signal ratio between the outer and inner cerebrospinal fluid compartments (CSFout:CSFin) was calculated. RESULTS: Phantom measurements show the correction of the intensity profile applying the given methods. Compared to the uncorrected phased array image (NRMSE = 0.46, CSFout:CSFin = 1.71), the quantitative evaluation of simulated and measured intensity corrected human brain data sets indicates the best performance utilizing the birdcage image (NRMSE = 0.39, CSFout:CSFin = 1.00). However, employing a support region (NRMSE = 0.40, CSFout:CSFin = 1.17), a universal sensitivity map (NRMSE = 0.41, CSFout:CSFin = 1.05) or a pre-scanned sensitivity map (NRMSE = 0.42, CSFout:CSFin = 1.07) shows only slightly worse results. CONCLUSION: Acquiring a birdcage image as reference image to correct for the receive profile demonstrates the best performance. However, when aiming to reduce acquisition time or for measurements without existing birdcage coil, methods that use a support region as reference image, a universal or a pre-scanned sensitivity map provide good alternatives for correction of the receive profile.

Compressed sensing reconstruction of 7 Tesla 23Na multi-channel breast data using 1H MRI constraint.

PURPOSE: To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (23Na MRI) using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS) with anatomical 1H prior knowledge. METHODS: An iterative reconstruction for 23Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV(2)), adopted by anatomical weighting factors (AnaWeTV(2)) obtained from a high-resolution 1H image. A support region is included as additional regularization. Simulated and measured 23Na multi-channel data sets (n = 3) of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4) were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction. RESULTS: Compared with a conventional TV(2), the AnaWeTV(2) reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8) gridding reconstructed data set. CONCLUSION: Especially for high USFs, the proposed algorithm allows improved image quality for multi-channel 23Na MRI data sets.