Emerging Applications of Abdominal 4D Flow MRI.
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ABSTRACT:
SUBMITTER: Roldan-Alzate A
PROVIDER: S-EPMC4977193 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
Publications
Emerging Applications of Abdominal 4D Flow MRI.
Roldán-Alzate Alejandro A Francois Christopher J CJ Wieben Oliver O Reeder Scott B SB
AJR. American journal of roentgenology 20160517 1
<h4>Objective</h4>Comprehensive assessment of abdominal hemodynamics is crucial for many clinical diagnoses but is challenged by a tremendous complexity of anatomy, normal physiology, and a wide variety of pathologic abnormalities. This article introduces 4D flow MRI as a powerful technique for noninvasive assessment of the hemodynamics of abdominal vascular territories.<h4>Conclusion</h4>Four-dimensional flow MRI provides clinicians with a more extensive and straightforward approach to evaluate ...[more]
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Project description:PurposeTo develop a convolutional neural network (CNN) for the robust and fast correction of velocity aliasing in 4D-flow MRI.MethodsThis study included 667 adult subjects with aortic 4D-flow MRI data with existing velocity aliasing (n = 362) and no velocity aliasing (n = 305). Additionally, 10 controls received back-to-back 4D-flow scans with systemically varied velocity-encoding sensitivity (vencs) at 60, 100, and 175 cm/s. The no-aliasing data sets were used to simulate velocity aliasing by reducing the venc to 40%-70% of the original, alongside a ground truth locating all aliased voxels (153 training, 152 testing). The 152 simulated and 362 existing aliasing data sets were used for testing and compared with a conventional velocity antialiasing algorithm. Dice scores were calculated to quantify CNN performance. For controls, the venc 175-cm/s scans were used as the ground truth and compared with the CNN-corrected venc 60 and 100 cm/s data sets RESULTS: The CNN required 176 ± 30 s to perform compared with 162 ± 14 s for the conventional algorithm. The CNN showed excellent performance for the simulated data compared with the conventional algorithm (median range of Dice scores CNN: [0.89-0.99], conventional algorithm: [0.84-0.94], p < 0.001, across all simulated vencs) and detected more aliased voxels in existing velocity aliasing data sets (median detected CNN: 159 voxels [31-605], conventional algorithm: 65 [7-417], p < 0.001). For controls, the CNN showed Dice scores of 0.98 [0.95-0.99] and 0.96 [0.87-0.99] for venc = 60 cm/s and 100 cm/s, respectively, while flow comparisons showed moderate-excellent agreement.ConclusionDeep learning enabled fast and robust velocity anti-aliasing in 4D-flow MRI.
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Project description:PurposeTo compare the accuracy of semiautomated flow tracking with that of semiautomated valve tracking in the quantification of mitral valve (MV) regurgitation from clinical four-dimensional (4D) flow MRI data obtained in patients with mild, moderate, or severe MV regurgitation.Materials and methodsThe 4D flow MRI data were retrospectively collected from 30 patients (21 men; mean age, 61 years ± 10 [standard deviation]) who underwent 4D flow MRI from 2006 to 2016. Ten patients had mild MV regurgitation, nine had moderate MV regurgitation, and 11 had severe MV regurgitation, as diagnosed by using semiquantitative echocardiography. The regurgitant volume (Rvol) across the MV was obtained using three methods: indirect quantification of Rvol (RvolINDIRECT), semiautomated quantification of Rvol using valve tracking (RvolVALVE), and semiautomated quantification of Rvol using flow tracking (RvolFLOW). A second observer repeated the measurements. Aortic valve flow was quantified as well to test for intervalve consistency. The Wilcoxon signed rank test, orthogonal regression, Bland-Altman analysis, and coefficients of variation were used to assess agreement among measurements and between observers.ResultsRvolFLOW was higher (median, 24.8 mL; interquartile range [IQR], 14.3-45.7 mL) than RvolVALVE (median, 9.9 mL; IQR, 6.0-16.9 mL; P < .001). Both RvolFLOW and RvolVALVE differed significantly from RvolINDIRECT (median, 19.1 mL; IQR, 4.1-47.5 mL; P = .03). RvolFLOW agreed more with RvolINDIRECT (ŷ = 0.78x + 12, r = 0.88) than with RvolVALVE (ŷ = 0.16x + 8.1, r = 0.53). Bland-Altman analysis revealed underestimation of RvolVALVE in severe MV regurgitation. Interobserver agreement was excellent for RvolFLOW (r = 0.95, coefficient of variation = 27%) and moderate for RvolVALVE (r = 0.72, coefficient of variation = 57%). Orthogonal regression demonstrated better intervalve consistency for flow tracking (ŷ = 1.2x - 13.4, r = 0.82) than for valve tracking (ŷ = 2.7x - 92.4, r = 0.67).ConclusionFlow tracking enables more accurate 4D flow MRI-derived MV regurgitation quantification than valve tracking in terms of agreement with indirect quantification and intervalve consistency, particularly in severe MV regurgitation.Supplemental material is available for this article.© RSNA, 2020.
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