Accelerated B 1 + $$ {\mathrm{B}}_1

PURPOSE: To investigate the impact of reduced k-space sampling on B 1 + $$ {\mathrm{B}}_1^{+} $$ mapping and the resulting impact on phase shimming and dynamic/universal parallel transmit (pTx) RF pulse design. METHODS: Channel-wise 3D B 1 + $$ {\mathrm{B}}_1^{+} $$ maps were measured at 7 T in 35 and 23 healthy subjects for the heart and prostate region, respectively. With these B 1 + $$ {\mathrm{B}}_1^{+} $$ maps, universal phase shims optimizing homogeneity and B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiency were designed for heart and prostate imaging. In addition, universal 4kT-point pulses were designed for the heart. Subsequently, individual phase shims and individual 4kT-pulses were designed based on B 1 + $$ {\mathrm{B}}_1^{+} $$ maps with different acceleration factors and tested on the original maps. The performance of the pulses was compared by evaluating their coefficients of variation (CoV), B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiencies and specific energy doses (SED). Furthermore, validation measurements were carried out for one heart and one prostate subject. RESULTS: For both organs, the universal phase shims showed significantly higher B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiencies and lower CoVs compared to the vendor provided default shim, but could still be improved with individual phase shims based on accelerated B 1 + $$ {\mathrm{B}}_1^{+} $$ maps (acquisition time = 30 s). In the heart, the universal 4kT-pulse achieved significantly lower CoVs than tailored phase shims. Tailored 4kT-pulses based on accelerated B 1 + $$ {\mathrm{B}}_1^{+} $$ maps resulted in even further reduced CoVs or a 2.5-fold reduction in SED at the same CoVs as the universal 4kT-pulse. CONCLUSION: Accelerated B 1 + $$ {\mathrm{B}}_1^{+} $$ maps can be used for the design of tailored pTx pulses for prostate and cardiac imaging at 7 T, which further improve homogeneity, B 1 + $$ {\mathrm{B}}_1^{+} $$ efficiency, or SED compared to universal pulses.

Influence of image contrasts and reconstruction methods on the classification of multiple sclerosis-like lesions in simulated sodium magnetic resonance imaging.

PURPOSE: To evaluate the classifiability of small multiple sclerosis (MS)-like lesions in simulated sodium (23 Na) MRI for different 23 Na MRI contrasts and reconstruction methods. METHODS: 23 Na MRI and 23 Na inversion recovery (IR) MRI of a phantom and simulated brain with and without lesions of different volumes (V = 1.3-38.2 nominal voxels) were simulated 100 times by adding Gaussian noise matching the SNR of real 3T measurements. Each simulation was reconstructed with four different reconstruction methods (Gridding without and with Hamming filter, Compressed sensing (CS) reconstruction without and with anatomical 1 H prior information). Based on the mean signals within the lesion volumes of simulations with and without lesions, receiver operating characteristics (ROC) were determined and the area under the curve (AUC) was calculated to assess the classifiability for each lesion volume. RESULTS: Lesions show higher classifiability in 23 Na MRI than in 23 Na IR MRI. For typical parameters and SNR of a 3T scan, the voxel normed minimal classifiable lesion volume (AUC > 0.9) is 2.8 voxels for 23 Na MRI and 19 voxels for 23 Na IR MRI, respectively. In terms of classifiability, Gridding with Hamming filter and CS without anatomical 1 H prior outperform CS reconstruction with anatomical 1 H prior. CONCLUSION: Reliability of lesion classifiability strongly depends on the lesion volume and the 23 Na MRI contrast. Additional incorporation of 1 H prior information in the CS reconstruction was not beneficial for the classification of small MS-like lesions in 23 Na MRI.

Motion-corrected 23 Na MRI of the human brain using interleaved 1 H 3D navigator images.

PURPOSE: To evaluate the feasibility of motion correction for sodium (23 Na) MRI based on interleaved acquired 3D proton (1 H) navigator images. METHODS: A 3D radial density-adapted sequence for interleaved 23 Na/1 H MRI was implemented on a 7 Tesla whole-body MRI system. The 1 H data obtained during the 23 Na acquisition were used to reconstruct 140 navigator image volumes with a nominal spatial resolution of (2.5 mm)3 and a temporal resolution of 6 s. The motion information received from co-registration was then used to correct the 23 Na image dataset, which also had a nominal spatial resolution of (2.5 mm)3 . The approach was evaluated on six healthy volunteers, whose motion during the scans had different intensities and characteristics. RESULTS: Interleaved acquisition of two nuclei did not show any relevant influence on image quality (SNR of 13.0 for interleaved versus 13.2 for standard 23 Na MRI and 176.4 for interleaved versus 178.0 for standard 1 H MRI). The applied motion correction increased the consistency between two consecutive scans for all examined volunteers and improved the image quality for all kinds of motion. The SD of the differences ranged between 2.30% and 6.96% for the uncorrected and between 2.13% and 2.67% for the corrected images. CONCLUSION: The feasibility of interleaved acquired 1 H navigator images to be used for retrospective motion correction of 23 Na images was successfully demonstrated. The approach neither affected the 23 Na image quality nor elongated the scan time and can therefore be an important tool to improve the accuracy of quantitative 23 Na MRI.

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.

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.