The originally published version of this article contained a typographical error. In the text under the subheading "Dynamic contrast-enhanced MRI method, post-processing, and MR-GFR calculation" and in Table 1 the intravenous injection rate of gadobutrol was incorrectly listed as 0.2 mL/s.

BACKGROUND: Current methods to estimate glomerular filtration rate (GFR) have shortcomings. Estimates based on serum creatinine are known to be inaccurate in the chronically ill and during acute changes in renal function. Gold standard methods such as inulin and 99mTc diethylenetriamine pentaacetic acid (DTPA) require blood or urine sampling and thus can be difficult to perform in children. Motion-robust radial volumetric interpolated breath-hold examination (VIBE) dynamic contrast-enhanced MRI represents a novel tool for estimating GFR that has not been validated in children.

OBJECTIVE: The purpose of our study was to determine the feasibility and accuracy of GFR measured by motion-robust radial VIBE dynamic contrast-enhanced MRI compared to estimates by serum creatinine (eGFR) and 99mTc DTPA in children.

MATERIALS AND METHODS: We enrolled children, 0-18 years of age, who were undergoing both a contrast-enhanced MRI and nuclear medicine 99mTc DTPA glomerular filtration rate (NM-GFR) within 2 weeks of each other. Enrolled children consented to an additional 6-min dynamic contrast-enhanced MRI scan using the motion-robust high spatiotemporal resolution prototype dynamic radial VIBE sequence (Siemens, Erlangen, Germany) at 3 tesla (T). The images were reconstructed offline with high temporal resolution ( 3 s/volume) using compressed sensing image reconstruction including regularization in temporal dimension to improve image quality and reduce streaking artifacts. Images were then automatically post-processed using in-house-developed software. Post-processing steps included automatic segmentation of kidney parenchyma and aorta using convolutional neural network techniques and tracer kinetic model fitting using the Sourbron two-compartment model to calculate the MR-based GFR (MR-GFR). The NM-GFR was compared to MR-GFR and estimated GFR based on serum creatinine (eGFR) using Pearson correlation coefficient and Bland-Altman analysis.

RESULTS: Twenty-one children (7 female, 14 male) were enrolled between February 2017 and May 2018. Data from six of these children were not further analyzed because of deviations from the MRI protocol. Fifteen patients were analyzed (5 female, 10 male; average age 5.9 years); the method was technically feasible in all children. The results showed that the MR-GFR correlated with NM-GFR with a Pearson correlation coefficient (r-value) of 0.98. Bland-Altman analysis (i.e. difference of MR-GFR and NM-GFR versus mean of NM-GFR and MR-GFR) showed a mean difference of -0.32 and reproducibility coefficient of 18 with 95% confidence interval, and the coefficient of variation of 6.7% with values between -19 (-1.96 standard deviation) and 18 (+1.96 standard deviation). In contrast, serum creatinine compared with NM-GFR yielded an r-value of 0.73. Bland-Altman analysis (i.e. difference of eGFR and NM-GFR versus mean of NM-GFR and eGFR) showed a mean difference of 2.9 and reproducibility coefficient of 70 with 95% confidence interval, and the coefficient of variation of 25% with values between -67 (-1.96 standard deviation) and 73 (+1.96 standard deviation).

CONCLUSION: MR-GFR is a technically feasible and reliable method of measuring GFR when compared to the reference standard, NM-GFR by serum 99mTc DTPA, and MR-GFR is more reliable than estimates based on serum creatinine.

BACKGROUND AND PURPOSE: Geometric distortions resulting from large pose changes reduce the accuracy of motion measurements and interfere with the ability to generate artifact-free information. Our goal is to develop an algorithm and pulse sequence to enable motion-compensated, geometric distortion compensated diffusion-weighted MRI, and to evaluate its efficacy in correcting for the field inhomogeneity and position changes, induced by large and frequent head motions.

METHODS: Dual echo planar imaging (EPI) with a blip-reversed phase encoding distortion correction technique was evaluated in five volunteers in two separate experiments and compared with static field map distortion correction. In the first experiment, dual-echo EPI images were acquired in two head positions designed to induce a large field inhomogeneity change. A field map and a distortion-free structural image were acquired at each position to assess the ability of dual-echo EPI to generate reliable field maps and enable geometric distortion correction in both positions. In the second experiment, volunteers were asked to move to multiple random positions during a diffusion scan. Images were reconstructed using the dual-echo correction and a slice-to-volume registration (SVR) registration algorithm. The accuracy of SVR motion estimates was compared to externally measured ground truth motion parameters.

RESULTS: Our results show that dual-echo EPI can produce slice-level field maps with comparable quality to field maps generated by the reference gold standard method. We also show that slice-level distortion correction improves the accuracy of SVR algorithms as slices acquired at different orientations have different levels of distortion, which can create errors in the registration process.

CONCLUSIONS: Dual-echo acquisitions with blip-reversed phase encoding can be used to generate slice-level distortion-free images, which is critical for motion-robust slice to volume registration. The distortion corrected images not only result in better motion estimates, but they also enable a more accurate final diffusion image reconstruction.

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.