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.

Quantitative 7T sodium magnetic resonance imaging of the human brain using a 32-channel phased-array head coil: Application to patients with secondary progressive multiple sclerosis.

Apparent tissue sodium concentrations (aTSCs) determined by 23 Na brain magnetic resonance imaging (MRI) have the potential to serve as a biomarker in pathologies such as multiple sclerosis (MS). However, the quantification is hindered by the intrinsically low signal-to-noise ratio of 23 Na MRI. The purpose of this study was to improve the accuracy and reliability of quantitative 23 Na brain MRI by implementing a dedicated postprocessing pipeline and to evaluate the applicability of the developed approach for the examination of MS patients. 23 Na brain MRI measurements of 13 healthy volunteers and 17 patients with secondary progressive multiple sclerosis (SPMS) were performed at 7 T using a dual-tuned 23 Na/1 H birdcage coil with a receive-only 32-channel phased array. The aTSC values were determined for normal appearing white matter (NAWM) and normal appearing gray matter (NAGM) in healthy subjects and SPMS patients. Signal intensities were normalized using the mean cerebrospinal fluid (CSF) sodium concentration determined in 37 separate patients receiving a spinal tap for routine diagnostic purposes. Five volunteers underwent MRI examinations three times in a row to assess repeatability. Coefficients of variation (CoVs) were used to quantify the repeatability of the proposed method. aTSC values were compared regarding brain regions and subject cohort using the paired-samples Wilcoxon rank-sum test. Laboratory CSF sodium concentration did not differ significantly between patients without and with MS (p = 0.42). The proposed quantification workflow for 23 Na MRI was highly repeatable with CoVs averaged over all five volunteers of 1.9% ± 0.9% for NAWM and 2.2% ± 1.6% for NAGM. Average NAWM aTSC was significantly higher in patients with SPMS compared with the control group (p = 0.009). Average NAGM aTSC did not differ significantly between healthy volunteers and MS patients (p = 0.98). The proposed postprocessing pipeline shows high repeatability and the results can serve as a baseline for further studies establishing 23 Na brain MRI as a biomarker in diseases such as MS.

Tissue Sodium Concentration Quantification at 7.0-T MRI as an Early Marker for Chemotherapy Response in Breast Cancer: A Feasibility Study.

Background Tissue sodium concentration (TSC) is elevated in breast cancer and can determine chemotherapy response. Purpose To test the feasibility of using a sodium 23 (23Na) MRI protocol at 7.0 T for TSC quantification to predict early treatment outcomes of neoadjuvant chemotherapy in breast cancer and to determine whether those quantitative values provide additional information about efficacy. Materials and Methods Women with primary breast cancer were included in this prospective study. From July 2017 to June 2018, participants underwent 7.0-T 23Na MRI. Multichannel data sets were acquired with a density-adapted, three-dimensional radial projection reconstruction pulse sequence. Two-dimensional tumor size and TSC were evaluated before and after the first and second chemotherapy cycle, and statistical tests were performed based on the presence or absence of a pathologic complete response (pCR). Results Fifteen women with breast cancer and six healthy women were enrolled. The mean baseline tumor size in women with a pCR was 7.0 cm2 ± 5.0 (standard deviation), and the mean baseline tumor size in women without a pCR was 19.0 cm2 ± 12.0. After the first chemotherapy cycle, women with a pCR showed a reduced tumor size of 32.9% (2.3 cm2/7.0 cm2), compared with 15.3% (2.9 cm2/19.0 cm2) in those without a pCR. The areas under the receiver operating characteristic curve for tumor size reduction after the first and second chemotherapy cycle were 0.73 (95% CI: 0.09, 0.50; P = .12) and 0.93 (95% CI: 0.04, 0.60; P < .001), respectively. Women with a pCR had a mean baseline TSC of 69.4 mmol/L ± 6.1, with a reduction of 12.0% (8.3 mmol/L), whereas those without a pCR had a mean baseline TSC of 71.7 mmol/L ± 5.7, with a reduction of 4.7% (3.4 mmol/L) after the first cycle. The areas under the receiver operating characteristic curve for TSC after the first and second cycles were 0.96 (95% CI: 0.86, 1.00; P < .001) and 1.000 (95% CI: 1.00, P < .001), respectively. Conclusion Using 7.0-T MRI for tissue sodium concentration quantification to predict early treatment outcomes of neoadjuvant chemotherapy in breast cancer is feasible, with reduced tissue sodium concentration indicative of cancer response. © RSNA, 2021 Online supplemental material is available for this article.

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.