Bulk motion‐compensated DCE‐MRI for functional imaging of kidneys in newborns. Issue 1 (14th December 2019)

• Record Type: Journal Article
• Authors: Coll‐Font, Jaume; Afacan, Onur; Chow, Jeanne S.; Lee, Richard S.; Stemmer, Alto; Warfield, Simon K.; Kurugol, Sila
• Is Part Of: Journal of magnetic resonance imaging. Volume 52:Issue 1(2020)
• Page Start: 207
• Page End: 216
• Publication Date: 2019-12-14
• Subjects: DCE‐MRI -- motion compensation -- newborn imaging -- GRASP -- kidney imaging; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27021
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Evaluation of kidney function in newborns with hydronephrosis is important for clinical decisions. Dynamic contrast‐enhanced (DCE) MRI can provide the necessary anatomical and functional information. Golden angle dynamic radial acquisition and compressed sensing reconstruction provides sufficient spatiotemporal resolution to achieve accurate parameter estimation for functional imaging of kidneys. However, bulk motion during imaging (rigid or nonrigid movement of the subject resulting in signal dropout) remains an unresolved challenge. Purpose: To evaluate a motion‐compensated (MoCo) DCE‐MRI technique for robust evaluation of kidney function in newborns. Our method includes: 1) motion detection, 2) motion‐robust image reconstruction, 3) joint realignment of the volumes, and 4) tracer‐kinetic (TK) model fitting to evaluate kidney function parameters. Study Type: Retrospective. Subjects: Eleven newborn patients (ages <6 months, 6 female). Field Strength/Sequence: 3T; dynamic "stack‐of‐stars" 3D fast low‐angle shot (FLASH) sequence using a multichannel body‐matrix coil. Assessment: We evaluated the proposed technique in terms of the signal‐to‐noise ratio (SNR) of the reconstructed images, the presence of discontinuities in the contrast agent concentration time curves due to motion with a total variation (TV) metric and the goodness of fit of the TK model, and the standard variation of its parameters. Statistical Tests: We used a paired t ‐test to compare the MoCo and no‐MoCo results. Results: The proposed MoCo method successfully detected motion and improved the SNR by 3.3 ( P = 0.012) and decreased TV by 0.374 ( P = 0.017) across all subjects. Moreover, it decreased nRMSE of the TK model fit for the subjects with less than five isolated bulk motion events in 6 minutes (mean 1.53, P = 0.043), but not for the subjects with more frequent events or no motion ( P = 0.745 and P = 0.683). Data Conclusion: Our results indicate that the proposed MoCo technique improves the image quality and accuracy of the TK model fit for subjects who present isolated bulk motion events. Level of Evidence: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:207–216.