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Abstract PurposeTo demonstrate speech‐production real‐time MRI (RT‐MRI) using a contemporary 0.55T system, and to identify opportunities for improved performance compared with conventional field strengths. MethodsExperiments were performed on healthy adult volunteers using a 0.55T MRI system with high‐performance gradients and a custom 8‐channel upper airway coil. Imaging was performed using spiral‐based balancedSSFPand gradient‐recalled echo (GRE) pulse sequences using a temporal finite‐difference constrained reconstruction. Speech‐production RT‐MRI was performed with three spiral readout durations (8.90, 5.58, and 3.48 ms) to determine trade‐offs with respect to articulator contrast, blurring, banding artifacts, and overall image quality. ResultsBoth spiral GRE and bSSFP captured tongue boundary dynamics during rapid consonant‐vowel syllables. Although bSSFP provided substantially higher SNR in all vocal tract articulators than GRE, it suffered from banding artifacts at TR > 10.9 ms. Spiral bSSFP with the shortest readout duration (3.48 ms, TR = 5.30 ms) had the best image quality, with a 1.54‐times boost in SNR compared with an equivalent GRE sequence. Longer readout durations led to increased SNR efficiency and blurring in both bSSFP and GRE. ConclusionHigh‐performance 0.55T MRI systems can be used for speech‐production RT‐MRI. Spiral bSSFP can be used without suffering from banding artifacts in vocal tract articulators, provide better SNR efficiency, and have better image quality than what is typically achieved at 1.5 T or 3 T.
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Deep learning‐based Accelerated and Noise‐Suppressed Estimation (DANSE) of quantitative Gradient‐Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties
Abstract The purpose of the current study was to introduce a Deep learning‐based Accelerated and Noise‐Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular‐specific,R2t*, and hemodynamic‐specific,R2’, metrics of quantitative gradient‐recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation ofR2t*andR2’maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B0) magnetic field inhomogeneities directly from the gradient‐recalled echo (GRE) magnitude images. TheR2t*andR2’maps for training were generated by means of a voxel‐by‐voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B0‐inhomogeneities contributions to multigradient‐echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise‐suppression feature of DANSE is especially prominent for data with low signal‐to‐noise ratio (SNR ~ 50–100), where DANSE‐generatedR2t*andR2’maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time‐dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.
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- Award ID(s):
- 2043134
- PAR ID:
- 10386237
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- NMR in Biomedicine
- Volume:
- 36
- Issue:
- 5
- ISSN:
- 0952-3480
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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