Estimation of glomerular filtration rate in a pediatric population using non-contrast kidney phase contrast magnetic resonance imaging.

BACKGROUND: Glomerular filtration rate (GFR) is a key measure of kidney function but often inaccurately ascertained by serum creatinine and cystatin C in pediatrics. In this pilot trial, we evaluated the relationship between GFR calculated by using phase-contrast MRI (PC-MRI) biomarkers and GFR by 125I-iothalamate clearance in youth undergoing bone marrow transplantation (BMT). METHODS: A total of twenty-one pediatric BMT candidates (8-21 years of age) were recruited for a research kidney PC-MRI. After completion of 125I-iothalamate clearance, same-day PC-MRI measurements were completed of the kidney circulation without a gadolinium-based contrast agent. MRI included a non-contrast balanced-SSFP-triggered angiography to position ECG-gated breath-held 2D PC-MRI flow measurements (1.2 × 1.2 × 6 mm3). A multivariate model of MRI biomarkers estimating GFR (GFR-MRI) was selected using the elastic net approach. RESULTS: The GFR-MRI variables selected by elastic net included average heart rate during imaging (bpm), peak aorta flow below the kidney artery take-offs (ml/s), average kidney artery blood flow, average peak kidney vein blood flow, and average kidney vein blood flow (ml/s). The GFR-MRI model demonstrated strong agreement with GFR by 125I-iothalamate (R2 = 0.65), which was stronger than what was observed with eGFR by the full age spectrum and Chronic Kidney Disease in Children under 25 (CKiD U25) approaches. CONCLUSION: In this pilot study, noninvasive GFR-MRI showed strong agreement with gold standard GFR in youth scheduled for BMT. Further work is needed to evaluate whether non-contrast GFR-MRI holds promise to become a superior alternative to eGFR and GFR by clearance techniques. A higher resolution version of the Graphical abstract is available as Supplementary information.

Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi-Site, Multi-Vendor, and Multi-Label Dense U-Net.

BACKGROUND: Automated segmentation using convolutional neural networks (CNNs) have been developed using four-dimensional (4D) flow magnetic resonance imaging (MRI). To broaden usability for congenital heart disease (CHD), training with multi-institution data is necessary. However, the performance impact of heterogeneous multi-site and multi-vendor data on CNNs is unclear. PURPOSE: To investigate multi-site CNN segmentation of 4D flow MRI for pediatric blood flow measurement. STUDY TYPE: Retrospective. POPULATION: A total of 174 subjects across two sites (female: 46%; N = 38 healthy controls, N = 136 CHD patients). Participants from site 1 (N = 100), site 2 (N = 74), and both sites (N = 174) were divided into subgroups to conduct 10-fold cross validation (10% for testing, 90% for training). FIELD STRENGTH/SEQUENCE: 3 T/1.5 T; retrospectively gated gradient recalled echo-based 4D flow MRI. ASSESSMENT: Accuracy of the 3D CNN segmentations trained on data from single site (single-site CNNs) and data across both sites (multi-site CNN) were evaluated by geometrical similarity (Dice score, human segmentation as ground truth) and net flow quantification at the ascending aorta (Qs), main pulmonary artery (Qp), and their balance (Qp/Qs), between human observers, single-site and multi-site CNNs. STATISTICAL TESTS: Kruskal-Wallis test, Wilcoxon rank-sum test, and Bland-Altman analysis. A P-value <0.05 was considered statistically significant. RESULTS: No difference existed between single-site and multi-site CNNs for geometrical similarity in the aorta by Dice score (site 1: 0.916 vs. 0.915, P = 0.55; site 2: 0.906 vs. 0.904, P = 0.69) and for the pulmonary arteries (site 1: 0.894 vs. 0.895, P = 0.64; site 2: 0.870 vs. 0.869, P = 0.96). Qs site-1 medians were 51.0-51.3 mL/cycle (P = 0.81) and site-2 medians were 66.7-69.4 mL/cycle (P = 0.84). Qp site-1 medians were 46.8-48.0 mL/cycle (P = 0.97) and site-2 medians were 76.0-77.4 mL/cycle (P = 0.98). Qp/Qs site-1 medians were 0.87-0.88 (P = 0.97) and site-2 medians were 1.01-1.03 (P = 0.43). Bland-Altman analysis for flow quantification found equivalent performance. DATA CONCLUSION: Multi-site CNN-based segmentation and blood flow measurement are feasible for pediatric 4D flow MRI and maintain performance of single-site CNNs. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Differentiation of Myositis-Induced Models of Bacterial Infection and Inflammation with T2-Weighted, CEST, and DCE-MRI.

We used T2 relaxation, chemical exchange saturation transfer (CEST), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to assess whether bacterial infection can be differentiated from inflammation in a myositis-induced mouse model. We measured the T2 relaxation time constants, %CEST at 5 saturation frequencies, and area under the curve (AUC) from DCE-MRI after maltose injection from infected, inflamed, and normal muscle tissue models. We applied principal component analysis (PCA) to reduce dimensionality of entire CEST spectra and DCE signal evolutions, which were analyzed using standard classification methods. We extracted features from dimensional reduction as predictors for machine learning classifier algorithms. Normal, inflamed, and infected tissues were evaluated with H&E and gram-staining histological studies, and bacterial-burden studies. The T2 relaxation time constants and AUC of DCE-MRI after injection of maltose differentiated infected, inflamed, and normal tissues. %CEST amplitudes at -1.6 and -3.5 ppm differentiated infected tissues from other tissues, but these did not differentiate inflamed tissue from normal tissue. %CEST amplitudes at 3.5, 3.0, and 2.5 ppm, AUC of DCE-MRI for shorter time periods, and relative Ktrans and kep values from DCE-MRI could not differentiate tissues. PCA and machine learning of CEST-MRI and DCE-MRI did not improve tissue classifications relative to traditional analysis methods. Similarly, PCA and machine learning did not further improve tissue classifications relative to T2 MRI. Therefore, future MRI studies of infection models should focus on T2-weighted MRI and analysis of T2 relaxation times.