J Magn Reson Imaging. 2021 Feb 25. doi: 10.1002/jmri.27578. Online ahead of print.
ABSTRACT
BACKGROUND: Although contrast agents would be beneficial, they are seldom used in four-dimensional (4D) flow magnetic resonance imaging (MRI) due to potential side effects and contraindications.
PURPOSE: To develop and evaluate a deep learning architecture to generate high blood-tissue contrast in noncontrast 4D flow MRI by emulating the use of an external contrast agent.
STUDY TYPE: Retrospective.
SUBJECTS: Of 222 data sets, 141 were used for neural network (NN) training (69 with and 72 without contrast agent). Evaluation was performed on the remaining 81 noncontrast data sets.
FIELD STRENGTH/SEQUENCES: Gradient echo or echo-planar 4D flow MRI at 1.5 T and 3 T.
ASSESSMENT: A cyclic generative adversarial NN was trained to perform image translation between noncontrast and contrast data. Evaluation was performed quantitatively using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), structural similarity index (SSIM), mean squared error (MSE) of edges, and Dice coefficient of segmentations. Three observers performed a qualitative assessment of blood-tissue contrast, noise, presence of artifacts, and image structure visualization.
STATISTICAL TESTS: The Wilcoxon rank-sum test evaluated statistical significance. Kendall’s concordance coefficient assessed interobserver agreement.
RESULTS: Contrast in the regions of interest (ROIs) in the NN enhanced images increased by 88%, CNR increased by 63%, and SNR improved by 48% (all P < 0.001). The SSIM was 0.82 ± 0.01, and the MSE of edges was 0.09 ± 0.01 (range [0,1]). Segmentations based on the generated images resulted in a Dice similarity increase of 15.25%. The observers managed to differentiate between contrast MR images and our results; however, they preferred the NN enhanced images in 76.7% of cases. This percentage increased to 93.3% for phase-contrast MR angiograms created from the NN enhanced data. Visual grading scores were blood-tissue contrast = 4.30 ± 0.74, noise = 3.12 ± 0.98, and presence of artifacts = 3.63 ± 0.76. Image structures within and without the ROIs resulted in scores of 3.42 ± 0.59 and 3.07 ± 0.71, respectively (P < 0.001).
DATA CONCLUSION: The proposed approach improves blood-tissue contrast in MR images and could be used to improve data quality, visualization, and postprocessing of cardiovascular 4D flow data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
PMID:33629795 | DOI:10.1002/jmri.27578
