Calibration‐free pTx of the human heart at 7T via 3D universal pulses

Self Cite

B_{1}^{+}

‐maps.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Tailored‐NRR and UP‐SB 9,22 pulses were designed from NRR‐SB

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‐maps. The source code and the

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‐maps are provided at https://github.com/chaigner/UP_body.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, pTx in the human body is typically tailored for each subject individually based on subject‐specific

B_{1}^{+}

B_{1}^{+}

‐maps of subjects performing shallow breathing (SB) 22 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Respiration induced B1+ changes and their impact on universal and tailored 3D kT‐point parallel transmission pulses for 7T cardiac imaging

Aigner

Dietrich

Schmitter

2022

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“…First, the increased transmit field (

B_{1}^{+}

) heterogeneity in the body at UHFs, leads to an inhomogeneous flip angle (FA) distribution, including regions of complete signal cancelation within the volume of interest 24,25 . This issue has been addressed by volunteer‐tailored parallel transmission techniques (pTx), such as

B_{1}^{+}

phase shimming, 26–28 spokes, 29,30 kT‐point 24 and, recently, using calibration‐free universal pulses 31 . Another technique that does not require volunteer‐specific

B_{1}^{+}

information but provides homogeneous image without signal dropouts is the time‐interleaved acquisition of modes (TIAMO) technique 21,32 …”

Section: Introductionmentioning

confidence: 99%

Motion‐compensated fat‐water imaging for 3D cardiac MRI at ultra‐high fields

Dietrich

Aigner

Mayer

et al. 2022

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Purpose Respiratory motion‐compensated (MC) 3D cardiac fat‐water imaging at 7T. Methods Free‐breathing bipolar 3D triple‐echo gradient‐recalled‐echo (GRE) data with radial phase‐encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21–35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9–34.0 kg/m2) and volunteer tailored B1+ shimming. The bipolar‐corrected triple‐echo GRE‐RPE data were binned into different respiratory phases (self‐navigation) and were used for the estimation of non‐rigid motion vector fields (MF) and respiratory resolved (RR) maps of the main magnetic field deviations (ΔB0). RR ΔB0 maps and MC ΔB0 maps were compared to a reference respiratory phase to assess respiration‐induced changes. Subsequently, cardiac binned fat‐water images were obtained using a model‐based, respiratory motion‐corrected image reconstruction. Results The 3D cardiac fat‐water imaging at 7T was successfully demonstrated. Local respiration‐induced frequency shifts in MC ΔB0 maps are small compared to the chemical shifts used in the multi‐peak model. Compared to the reference exhale ΔB0 map these changes are in the order of 10 Hz on average. Cardiac binned MC fat‐water reconstruction reduced respiration induced blurring in the fat‐water images, and flow artifacts are reduced in the end‐diastolic fat‐water separated images. Conclusion This work demonstrates the feasibility of 3D fat‐water imaging at UHF for the entire human heart despite spatial and temporal B1+ and B0 variations, as well as respiratory and cardiac motion.

Mitigating transmit‐B₁ artifacts by predicting parallel transmission images with deep learning: A feasibility study using high‐resolution whole‐brain diffusion at 7 Tesla

Uğurbil

2022

To propose a novel deep learning (DL) approach to transmit-B 1 (B 1 + )-artifact mitigation without direct use of parallel transmission (pTx), by predicting pTx images from single-channel transmission (sTx) images. Methods:A deep encoder-decoder convolutional neural network was constructed and trained to learn the mapping from sTx to pTx images. The feasibility was demonstrated using 7 T Human-Connectome Project (HCP)-style diffusion MRI. The training dataset comprised images acquired on 5 healthy subjects using commercial Nova RF coils. Relevant hyperparameters were tuned with a nested cross-validation, and the generalization performance evaluated using a regular cross-validation.Results: Our DL method effectively improved the image quality for sTx images by restoring the signal dropout, with quality measures (including normalized root-mean-square error, peak SNR, and structural similarity index measure) improved in most brain regions. The improved image quality was translated into improved performances for diffusion tensor imaging analysis; our method improved accuracy for fractional anisotropy and mean diffusivity estimations, reduced the angular errors of principal eigenvectors, and improved the fiber orientation delineation relative to sTx images. Moreover, the final DL model trained on data of all 5 subjects was successfully used to predict pTx images for unseen new subjects (randomly selected from the 7 T HCP database), effectively recovering the signal dropout and improving color-coded fractional anisotropy maps with largely reduced noise levels. Conclusion:The proposed DL method has potential to provide images with reduced B1 + artifacts in healthy subjects even when pTx resources are inaccessible on the user side.