Dataset Information

Impact of gradient imperfections on bone water quantification with UTE MRI.

ABSTRACT:

Purpose

The impact of gradient imperfections on UTE images and UTE image-derived bone water quantification was investigated at 3 T field strength.

Methods

The effects of simple gradient time delays and eddy currents on UTE images, as well as the effects of gradient error corrections, were studied with simulation and phantom experiments. The k-space trajectory was mapped with a 2D sequence with phase encoding on both spatial axes by measuring the phase of the signal in small time increments during ramp-up of the read gradient. In vivo 3D UTE images were reconstructed with and without gradient error compensation to determine the bias in bone water quantification. Finally, imaging was performed on 2 equally configured Siemens TIM Trio systems (Siemens Medical Solutions, Erlangen, Germany) to investigate the impact of such gradient imperfections on inter-scanner measurement bias.

Results

Compared to values derived from UTE images with full gradient error compensation, total bone water was found to deviate substantially with no (up to 17%) or partial (delay-only) compensation (up to 10.8%). Bound water, obtained with inversion recovery-prepared UTE, was somewhat less susceptible to gradient errors (up to 2.2% for both correction strategies). Inter-scanner comparison indicated a statistically significant bias between measurements from the 2 MR systems for both total and bound water, which either vanished or was substantially reduced following gradient error correction.

Conclusion

Gradient imperfections impose spatially dependent artifacts on UTE images, which compromise not only bone water quantification accuracy but also inter-scanner measurement agreement if left uncompensated.

SUBMITTER: Zhao X

PROVIDER: S-EPMC7348668 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor-intensive lung segmentation. PURPOSE:To evaluate a deep learning (DL) approach for automated lung segmentation to extract image-based biomarkers from functional lung imaging using 3D radial UTE oxygen-enhanced (OE) MRI. STUDY TYPE:Retrospective study aimed to evaluate a technical development. POPULATION:Forty-five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis. FIELD STRENGTH/SEQUENCE:1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence. ASSESSMENT:Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2 ) and hyperoxic (100% O2 ) conditions. Automated segmentation of the lungs using 2D convolutional encoder-decoder based DL method, and the subsequent functional quantification via adaptive K-means were compared with the results obtained from the reference method, supervised region growing. STATISTICAL TESTS:Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two-sided Wilcoxon signed-rank test for computation time, and Bland-Altman analysis on the functional measure derived from the OE images. RESULTS:The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland-Altman analysis showed nonsignificant intermethod differences of volumetric (P ? 0.12) and functional measurements (P ? 0.34) in the left and right lungs. DATA CONCLUSION:DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169-1181.