Brain asymmetries from mid- to late life and hemispheric brain age.

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants.

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing.

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Healthy Kidney Segmentation in the Dce-Mr Images Using a Convolutional Neural Network and Temporal Signal Characteristics.

Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments-even if performed by experts-is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions-cortex, medulla, and pelvis-based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms-support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.

Magnetic resonance imaging biomarkers for chronic kidney disease: a position paper from the European Cooperation in Science and Technology Action PARENCHIMA.

Functional renal magnetic resonance imaging (MRI) has seen a number of recent advances, and techniques are now available that can generate quantitative imaging biomarkers with the potential to improve the management of kidney disease. Such biomarkers are sensitive to changes in renal blood flow, tissue perfusion, oxygenation and microstructure (including inflammation and fibrosis), processes that are important in a range of renal diseases including chronic kidney disease. However, several challenges remain to move these techniques towards clinical adoption, from technical validation through biological and clinical validation, to demonstration of cost-effectiveness and regulatory qualification. To address these challenges, the European Cooperation in Science and Technology Action PARENCHIMA was initiated in early 2017. PARENCHIMA is a multidisciplinary pan-European network with an overarching aim of eliminating the main barriers to the broader evaluation, commercial exploitation and clinical use of renal MRI biomarkers. This position paper lays out PARENCHIMA's vision on key clinical questions that MRI must address to become more widely used in patients with kidney disease, first within research settings and ultimately in clinical practice. We then present a series of practical recommendations to accelerate the study and translation of these techniques.

Workflow sensitivity of post-processing methods in renal DCE-MRI.

OBJECTIVE: Estimation of renal filtration using dynamic contrast-enhanced imaging (DCE-MRI) requires a series of analysis steps. The possible number of distinct post-processing chains is large and grows rapidly with increasing number of processing steps or options. In this study we introduce a framework for systematic evaluation of the post-processing chains. The framework is later used to highlight the workflow processing chain sensitivity towards accuracy in estimation of glomerular filtration rate (GFR). METHODS: Twenty healthy volunteers underwent DCE-MRI examinations as well as iohexol clearance for reference GFR measurements. In total, 692 different combinations of post-processing steps were explored for analysis, including options for kidney segmentation, B1 inhomogeneity correction, placement of arterial input function, gadolinium concentration estimation as well as handling of motion-corrupted volumes and breathing motion. The evaluation of various processing chains is presented using a classification tree framework and random forest ensemble learning. RESULTS: Among the processing steps subject to testing, methods for calculating the gadolinium concentration as well as B1 inhomogeneity correction had the largest impact on accuracy of GFR estimations. Different segmentation methods did not play an important role in the post-processing of the MR data except from one processing chain where the automated segmentation outperformed the manual segmentation. CONCLUSION: The proposed classification trees were efficiently used as a statistical tool for visualization and communication of results to distinguish between important and less influential processing steps in renal DCE-MRI. We also identified several crucial factors in the processing chain.

Dynamic contrast-enhanced MRI measurement of renal function in healthy participants.

Background High repeatability, accuracy, and precision for renal function measurements need to be achieved to establish renal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as a clinically useful diagnostic tool. Purpose To investigate the repeatability, accuracy, and precision of DCE-MRI measured renal perfusion and glomerular filtration rate (GFR) using iohexol-GFR as the reference method. Material and Methods Twenty healthy non-smoking volunteers underwent repeated DCE-MRI and an iohexol-GFR within a period of 10 days. Single-kidney (SK) MRI measurements of perfusion (blood flow, Fb) and filtration (GFR) were derived from parenchymal intensity time curves fitted to a two-compartment filtration model. The repeatability of the SK-MRI measurements was assessed using coefficient of variation (CV). Using iohexol-GFR as reference method, the accuracy of total MR-GFR was determined by mean difference (MD) and precision by limits of agreement (LoA). Results SK-Fb (MR1, 345 ± 84; MR2, 371 ± 103 mL/100 mL/min) and SK-GFR (MR1, 52 ± 14; MR2, 54 ± 10 mL/min/1.73 m2) measurements achieved a repeatability (CV) in the range of 15-22%. With reference to iohexol-GFR, MR-GFR was determined with a low mean difference but high LoA (MR1, MD 1.5 mL/min/1.73 m2, LoA [-42, 45]; MR2, MD 6.1 mL/min/1.73 m2, LoA [-26, 38]). Eighty percent and 90% of MR-GFR measurements were determined within ± 30% of the iohexol-GFR for MR1 and MR2, respectively. Conclusion Good repeatability of SK-MRI measurements and good agreement between MR-GFR and iohexol-GFR provide a high clinical potential of DCE-MRI for renal function assessment. A moderate precision in MR-derived estimates indicates that the method cannot yet be used in clinical routine.

Quantification of Single-Kidney Function and Volume in Living Kidney Donors Using Dynamic Contrast-Enhanced MRI.

OBJECTIVE: The objective of our study was to investigate whether dynamic contrast-enhanced MRI (DCE-MRI) can detect differences and potential adaption in single-kidney parenchymal volume, blood flow, glomerular filtration rate (GFR), and filtration fraction in the remaining kidney of healthy donors compared with nondonors. Further, we evaluated the agreement in donor GFRs measured using DCE-MRI versus serum clearance of iohexol. SUBJECTS AND METHODS: Twenty living kidney donors and 20 healthy control subjects underwent DCE-MRI and iohexol GFR. Renal parenchymal volume was assessed from maximum-signal-intensity maps. Single-kidney MRI measurements of blood flow and GFR were derived from parenchymal signal intensity-time curves fitted to a two-compartment filtration model. The Student t test, Pearson correlation coefficient, mean differences, and limits of agreement were applied to analyze MRI measurements between groups and agreement with iohexol GFR. RESULTS: MRI findings showed significantly higher blood flow (difference in mean values of donors vs control subjects, 54%; p = 0.001), GFR (78%, p < 0.0001), and renal parenchymal volume (65%, p < 0.0001) in the single kidney of donors compared with the single kidney of healthy control subjects. In the donors, a proportional increase in blood flow and GFR resulted in a comparable filtration fraction, as was observed in the control subjects. Significant correlations were found between MRI-derived GFR and parenchymal volume (p < 0.0016) as well as with iohexol GFR (p < 0.0001). The mean difference between MRI-derived GFR and iohexol GFR was 14.0 mL/min, and the limits of agreement between MRI-derived GFR and iohexol GFR were -24.1 and 52.1 mL/min. CONCLUSION: DCE-MRI-derived values for single-kidney function and volume in kidney donors were significantly higher than those in control subjects and suggest a future potential benefit of DCE-MRI for diagnostic and prognostic structural and functional assessments in living kidney donors.

Use of 3D DCE-MRI for the estimation of renal perfusion and glomerular filtration rate: an intrasubject comparison of FLASH and KWIC with a comprehensive framework for evaluation.

OBJECTIVE. The purpose of this article is to compare two 3D dynamic contrast-enhanced (DCE) MRI measurement techniques for MR renography, a radial k-space weighted image contrast (KWIC) sequence and a cartesian FLASH sequence, in terms of intrasubject differences in estimates of renal functional parameters and image quality characteristics. SUBJECTS AND METHODS. Ten healthy volunteers underwent repeated breath-hold KWIC and FLASH sequence examinations with temporal resolutions of 2.5 and 2.8 seconds, respectively. A two-compartment model was used to estimate MRI-derived perfusion parameters and glomerular filtration rate (GFR). The latter was compared with the iohexol GFR and the estimated GFR. Image quality was assessed using a visual grading characteristic analysis of relevant image quality criteria and signal-to-noise ratio calculations. RESULTS. Perfusion estimates from FLASH were closer to literature reference values than were the KWIC sequences. In relation to the iohexol GFR (mean [± SD], 103 ± 11 mL/min/1.73 m(2)), KWIC produced significant underestimations and larger bias in GFR values (mean, 70 ± 30 mL/min/1.73 m(2); bias = -33.2 mL/min/1.73 m(2)) compared with the FLASH GFR (110 ± 29 mL/min/1.73 m(2); bias = 6.4 mL/min/1.73 m(2)). KWIC was statistically significantly (p < 0.005) more impaired by artifacts than was FLASH (AUC = 0.18). The average signal-enhancement ratio (delta ratio) in the cortex was significantly lower for KWIC (delta ratio = 0.99) than for FLASH (delta ratio = 1.40). Other visually graded image quality characteristics and signal-to-noise ratio measurements were not statistically significantly different. CONCLUSION. Using the same postprocessing scheme and pharmacokinetic model, FLASH produced more accurate perfusion and filtration parameters than did KWIC compared with clinical reference methods. Our data suggest an apparent relationship between image quality characteristics and the degree of stability in the numeric model-based renal function estimates.

Segmentation-driven image registration- application to 4D DCE-MRI recordings of the moving kidneys.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the kidneys requires proper motion correction and segmentation to enable an estimation of glomerular filtration rate through pharmacokinetic modeling. Traditionally, co-registration, segmentation, and pharmacokinetic modeling have been applied sequentially as separate processing steps. In this paper, a combined 4D model for simultaneous registration and segmentation of the whole kidney is presented. To demonstrate the model in numerical experiments, we used normalized gradients as data term in the registration and a Mahalanobis distance from the time courses of the segmented regions to a training set for supervised segmentation. By applying this framework to an input consisting of 4D image time series, we conduct simultaneous motion correction and two-region segmentation into kidney and background. The potential of the new approach is demonstrated on real DCE-MRI data from ten healthy volunteers.

Comparison of effective radiation doses in patients undergoing unenhanced MDCT and excretory urography for acute flank pain.

OBJECTIVE: The purpose of this study was to measure and compare the effective radiation dose in patients undergoing unenhanced MDCT and excretory urography for acute flank pain, and to explore technical and practical factors affecting the effective dose. SUBJECTS AND METHODS: One hundred nineteen patients with acute flank pain were included. All patients were examined using both MDCT and excretory urography. CT involved one acquisition from the upper kidney margin to the symphysis pubis. The only protocol variation was in the tube current (mAs), which was made according to patient body mass. The excretory urography protocol consisted of three images, with more when supplementary images were needed. Effective radiation doses were computer-simulated using dosimetry programs for CT and conventional radiography, based on Norwegian Radiological Protection Board dose data sets. Mean and SDs of measured patient doses were calculated and compared. Further analyses of dose variations in body mass categories (body mass index) were conducted, as were analyses concerning the number of images taken. RESULTS: The mean effective doses were 7.7 mSv with MDCT and 3.63 mSv with excretory urography. The effective dose varied both in and between techniques but could be predicted. Radiation risk decreased significantly with increased patient weight. CONCLUSION: The average effective dose with MDCT was more than double that with excretory urography. However, the appropriate dose could be strongly predicted by the patient's body mass index and by procedure. An optimum low-dose protocol should be considered before initiating unenhanced MDCT for ureteral colic in order to minimize the radiation-induced cancer risk and to secure adequate image quality.