Acquisition- and Reconstruction-Techniques for Sodium Quantification in the Skeletal Muscle using Sodium Magnetic Resonance Imaging (23Na MRI) - Utzschneider, Matthias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2022)
Measurement of the tissue sodium concentration (TSC) in human tissue offers important insights into pathological- and biological processes with applications in nephrology, neurology and myology. The TSC can be determined non-invasively by sodium magnetic resonance imaging (23Na MRI). The aim of this work is to accelerate 23Na MRI for TSC measurements in the skeletal muscle. Reconstruction methods and adapted acquisition techniques were investigated to reduce the acquisition time for 23Na MRI. The time saved could improve the applicability of TSC measurements for the use in the clinical environment and clinical studies. The main focus of this thesis is on compressed sensing (CS) reconstruction techniques and anisotropic acquisition techniques with reduced undersampling artifacts. Here, the acquisition time reduction is achieved by undersampling, which means measuring less data points than needed for an image with a certain resolution. This undersampling causes artifacts and decreases image quality. The acquisition sequences and reconstruction techniques used and developed in this thesis seek to remedy these deficiencies. First, 3D dictionary-learning compressed sensing (3D-DLCS) was evaluated for reconstruction of undersampled 23Na MRI data acquired with a density-adapted 3D-radial sampling sequence with a cuboid field-of-view (DA-3DPR-C). The results of the study with simulations and in vivo measurements indicated that 3D-DLCS could be applicable for a four-fold acceleration of TSC measurements in the skeletal muscle. However, the quantification error decrease was only about 10% compared to the reference. Based on these results, optimized acquisition techniques were investigated and developed for accelerated 23Na MRI. Two acquisition sequences were used as a basis: the DA-3DPR-C method and a 3D acquisition-weighted density-adapted stack-of-stars sequence (AWSOSt). Two further acquisition sequences were developed to increase the artifact incoherence: A golden ratio rotated stack-of-stars sequence based on AWSOSt and a DA-3DPR-C adaption with golden means reordered projections. Comparisons in simulations and an in vivo/vitro study showed that the quantification error is decreased and the artifact incoherence is increased by adapting acquisition trajectories using the golden angle and golden means. Additionally, three CS algorithms were compared for the adapted stack-of-stars acquisition sequence in simulations and an in vivo/vitro study: 3D-DLCS, total variation CS (TV-CS) and TV-CS with a block matching prior. In this comparison, the highest quantification error decrease of 35% was observed with 3D-DLCS for an acceleration factor of 4.1 and a measurement time of less than two minutes. The results demonstrate the impact of artifact incoherence for CS reconstruction. Furthermore, the investigations showed that applicable acquisition times are achievable by artifact-reduced acquisition and CS reconstruction. Beyond CS, deep learning (DL) based reconstruction was explored in a preliminary study. A U-Net was trained using 350 23Na MRI calf measurements. The quantification error was decreased by 64% with the U-Net compared to the reference method. These promising results indicate that DL-based reconstruction could probably be the next step for accelerated 23Na MRI.<br
Keywords: Compressed Sensing; Deep Learning; Image Reconstruction; MRI; Magnetic Resonance Imaging; Sodium

Acquisition- and Reconstruction-Techniques for Sodium Quantification in the Skeletal Muscle using Sodium Magnetic Resonance Imaging (23Na MRI) - Utzschneider, Matthias (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)
Measurement of the tissue sodium concentration (TSC) in human tissue offers important insights into pathological- and biological processes with applications in nephrology, neurology and myology. The TSC can be determined non-invasively by sodium magnetic resonance imaging (23Na MRI). The aim of this work is to accelerate 23Na MRI for TSC measurements in the skeletal muscle. Reconstruction methods and adapted acquisition techniques were investigated to reduce the acquisition time for 23Na MRI. The time saved could improve the applicability of TSC measurements for the use in the clinical environment and clinical studies. The main focus of this thesis is on compressed sensing (CS) reconstruction techniques and anisotropic acquisition techniques with reduced undersampling artifacts. Here, the acquisition time reduction is achieved by undersampling, which means measuring less data points than needed for an image with a certain resolution. This undersampling causes artifacts and decreases image quality. The acquisition sequences and reconstruction techniques used and developed in this thesis seek to remedy these deficiencies. First, 3D dictionary-learning compressed sensing (3D-DLCS) was evaluated for reconstruction of undersampled 23Na MRI data acquired with a density-adapted 3D-radial sampling sequence with a cuboid field-of-view (DA-3DPR-C). The results of the study with simulations and in vivo measurements indicated that 3D-DLCS could be applicable for a four-fold acceleration of TSC measurements in the skeletal muscle. However, the quantification error decrease was only about 10% compared to the reference. Based on these results, optimized acquisition techniques were investigated and developed for accelerated 23Na MRI. Two acquisition sequences were used as a basis: the DA-3DPR-C method and a 3D acquisition-weighted density-adapted stack-of-stars sequence (AWSOSt). Two further acquisition sequences were developed to increase the artifact incoherence: A golden ratio rotated stack-of-stars sequence based on AWSOSt and a DA-3DPR-C adaption with golden means reordered projections. Comparisons in simulations and an in vivo/vitro study showed that the quantification error is decreased and the artifact incoherence is increased by adapting acquisition trajectories using the golden angle and golden means. Additionally, three CS algorithms were compared for the adapted stack-of-stars acquisition sequence in simulations and an in vivo/vitro study: 3D-DLCS, total variation CS (TV-CS) and TV-CS with a block matching prior. In this comparison, the highest quantification error decrease of 35% was observed with 3D-DLCS for an acceleration factor of 4.1 and a measurement time of less than two minutes. The results demonstrate the impact of artifact incoherence for CS reconstruction. Furthermore, the investigations showed that applicable acquisition times are achievable by artifact-reduced acquisition and CS reconstruction. Beyond CS, deep learning (DL) based reconstruction was explored in a preliminary study. A U-Net was trained using 350 23Na MRI calf measurements. The quantification error was decreased by 64% with the U-Net compared to the reference method. These promising results indicate that DL-based reconstruction could probably be the next step for accelerated 23Na MRI.
Keywords: Compressed Sensing; Deep Learning; Image Reconstruction; MRI; Magnetic Resonance Imaging; Sodium

Combined imaging of potassium and sodium in human skeletal muscle tissue at 7 T - Gast, Lena; Volker, Stefanie; Utzschneider, Matthias; Linz, Peter; Wilferth, Tobias; Müller, Max; Kopp, Christoph; Hensel, Bernhard; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance in Medicine, 2020)
Purpose: To validate the feasibility of quantitative combined potassium (39K) and sodium (23Na) 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 23Na and 39K MRI acquisition protocols were implemented on a 7 T MR system. A double-resonant 23Na/39K 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 23Na/39K 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 23Na and 39K 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 39K in calf muscle tissue, (Formula presented.) components of (Formula presented.) = (1.2 ± 0.2) ms and (Formula presented.) = (7.9 ± 0.9) ms, as well as a residual quadrupolar interaction of (Formula presented.) = (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 23Na and 39K MRI at 7 T in human skeletal muscle tissue is feasible in clinically acceptable acquisition durations.
CRIS-Team Scopus Importer:2020-09-11
Keywords: 7 T; potassium MRI; sodium MRI; tissue potassium concentration; tissue sodium concentration; ultrahigh field strengths

Comparison of optimized intensity correction methods for 23Na MRI of the human brain using a 32-channel phased array coil at 7 Tesla - Lachner, Sebastian; Ruck, Laurent; Niesporek, Sebastian C.; Utzschneider, Matthias; Lott, Johanna; Hensel, Bernhard; Dörfler, Arnd; Uder, Michael; Nagel, Armin Michael (Zeitschrift für Medizinische Physik, 2019)
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.
CRIS-Team Scopus Importer:2019-12-31
Keywords: 7 Tesla; Intensity correction; Multi-channel array coil; Phased array coil; Receive profile; Sodium 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 - Lachner, Sebastian; Utzschneider, Matthias; Zaric, Olgica; Minarikova, Lenka; Ruck, Laurent; Zbýň, Štefan; Hensel, Bernhard; Trattnig, Siegfried; Uder, Michael; Nagel, Armin Michael (Zeitschrift für Medizinische Physik, 2020)
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 7 T. 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.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Compressed sensing; Iterative reconstruction; Multi-channel; Prior knowledge; Sensitivity encoding; Sodium MRI

Compressed sensing reconstruction of 7 Tesla23Na multi-channel breast data using 1H MRI constraint - Lachner, Sebastian; Zaric, Olgica; Utzschneider, Matthias; Minarikova, Lenka; Zbýň, Štefan; Hensel, Bernhard; Trattnig, Siegfried; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Imaging, 2019)
Purpose: To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging ( 23 Na MRI)using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS)with anatomical 1 H prior knowledge. Methods: An iterative reconstruction for 23 Na 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 1 H image. A support region is included as additional regularization. Simulated and measured 23 Na 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 23 Na MRI data sets.
CRIS-Team Scopus Importer:2019-05-14
Keywords: 7 Tesla; Compressed sensing (CS); Iterative reconstruction; Multi-channel; Prior knowledge; Sodium (

Correction to: Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing (Magnetic Resonance Materials in Physics, Biology and Medicine, (2020), 10.1007/s10334-019-00819-2) - Utzschneider, Matthias; Behl, Nicolas G.R.; Lachner, Sebastian; Gast, Lena; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Materials in Physics Biology and Medicine, 2020)
The original version of this article unfortunately contained a mistake. Title was incorrect. The corrected title is given below. Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing. The original article has been corrected.
CRIS-Team Scopus Importer:2020-02-25

Real-Time-Capable GPU-Framework for Depth-Aware Rigid 2D/3D Registration - Utzschneider, Matthias; Wang, Jian; Schaffert, Roman; Borsdorf, Anja; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.realti

T-2* Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing - Utzschneider, Matthias; Behl, Nicolas G. R.; Lachner, Sebastian; Gast, Lena; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Materials in Physics Biology and Medicine, 2020)
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.
CRIS-Team WoS Importer:2020-01-31

Towards accelerated quantitative sodium MRI at 7 T in the skeletal muscle: Comparison of anisotropic acquisition- and compressed sensing techniques - Utzschneider, Matthias; Müller, Max; Gast, Lena; Lachner, Sebastian; Behl, Nicolas G.R.; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Imaging, 2021)
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.
CRIS-Team Scopus Importer:2020-11-06
Keywords: 7 tesla; Anisotropic acquisition schemes; Compressed sensing; Image reconstruction; Skeletal muscle; Sodium (