4D MRI of polycystic kidneys from rapamycin‐treated Glis3‐deficient mice

Xie, Luke; Qi, Yi; Subashi, Ergys; Liao, Grace; Miller-DeGraff, Laura; Jetten, Anton M.; Johnson, G. Allan

doi:10.1002/nbm.3281

Cited by 9 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been increased interest recently in the development of imaging tools to probe microstructure with MRI 11 , 12 . Recently, a cationized form of ferritin (the predominant mammalian iron-storage protein) has been developed as a contrast agent for MRI to measure kidney glomerular morphology 10 , 13 – 17 .…”

Section: Introductionmentioning

confidence: 99%

Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction

Baldelomar

Charlton

Beeman

et al. 2016

Kidney International

View full text Add to dashboard Cite

Reduced nephron mass is strongly linked to susceptibility to chronic renal and cardiovascular diseases. There are currently no tools to identify nephropenia in clinical or preclinical diagnostics. Such new methods could uncover novel mechanisms and therapies for chronic kidney disease (CKD) and reveal how variation among traits can affect renal function and morphology. Here we used cationized ferritin (CF) enhanced-MRI (CFE-MRI) to investigate the relationship between glomerular number (Nglom) and volume (Vglom) in kidneys of healthy wild type mice and mice with oligosyndactylism (Os/+), a model of congenital nephron reduction. Mice were injected with cationic ferritin and perfused and the resected kidneys imaged with 7T MRI to detect CF-labeled glomeruli. CFE-MRI was used to measure the intrarenal distribution of individual glomerular volumes and revealed two major populations of glomeruli distinguished by size. Spatial mapping revealed that the largest glomeruli were located in the juxtamedullary region in both wild type and Os/+ mice and the smallest population located in the cortex. Os/+ mice had about a 50% reduction and 35% increase of Nglom and Vglom, respectively, in both glomerular populations compared to wild type, consistent with glomerular hypertrophy in the Os/+ mice. Thus, we provide a foundation for whole-kidney, MRI-based phenotyping of mouse renal glomerular morphology and provide new potential for quantitative human renal diagnostics.

show abstract

Section: Introductionmentioning

confidence: 99%

Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction

Baldelomar

Charlton

Beeman

et al. 2016

Kidney International

View full text Add to dashboard Cite

show abstract

“…For example, ultrasound can detect renovascular expression of adhesion molecules (3), whereas computed tomography is a powerful tool to quantitate renal hemodynamics (4). Initially used to detect scar formation in the kidney, magnetic resonance imaging (MRI) is now employed to interrogate a range of functional and structural parameters in the kidney during preclinical studies (7,19,21).…”

Section: Introductionmentioning

confidence: 99%

Dynamic contrast-enhanced MRI promotes early detection of toxin-induced acute kidney injury

Privratsky

Wang

et al. 2019

American Journal of Physiology-Renal Physiology

Self Cite

View full text Add to dashboard Cite

Acute kidney injury (AKI) is a common cause of morbidity and mortality in hospitalized patients. Nevertheless, there is limited ability to diagnose AKI in its earliest stages through the collection of structural and functional information. Magnetic resonance imaging (MRI) is increasingly being used to provide structural and functional data that characterize the injured kidney. Dynamic contrast-enhanced (DCE) MRI is an imaging modality with robust spatial and temporal resolution; however, its ability to detect changes in kidney function following AKI has not been determined. We hypothesized that DCE MRI would detect a prolongation in contrast transit time following toxin-induced AKI earlier than commonly used serum and tissue biomarkers. To test our hypothesis, we injected mice with either vehicle or cisplatin (30 mg/kg) and performed DCE MRI at multiple time points. We found that commonly used kidney injury biomarkers, including creatinine, blood urea nitrogen, and neutrophil gelatinase-associated lipocalin, did not rise until day 2 following cisplatin. Tissue levels of the proinflammatory cytokines and chemokines, tumor necrosis factor-α, interleukin (IL)-1β, IL-1α, IL-6, C-C motif chemokine ligand 2, and C-X-C motif chemokine ligand 2 similarly did not upregulate until day 2 following cisplatin. However, the time to peak intensity of contrast in the renal collecting system was already prolonged at day 1 following cisplatin compared with vehicle-treated mice. This intensity change mirrored changes in kidney injury as measured by histological analysis and in transporter expression in the proximal tubule. Taken together, DCE MRI is a promising preclinical imaging modality that is useful for assessing functional capacity of the kidney in the earliest stages following AKI.

show abstract

“…This architecture has been shown to generalize even from a limited amount of annotated data [25], and as such is well suited for medical imaging, where datasets as large as the ones commonly used for CNNs are rare. Valverde et al [26] recently demonstrated the effectiveness of U-Net-like architectures in preclinical research, designing the first DNN for the segmentation of ischemic lesions in rodents and achieving segmentation accuracy comparable or better to inter-rater agreement in manual segmentation.…”

mentioning

confidence: 99%

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Feo

Shatillo²,

Sierra

et al. 2020

Preprint

View full text Add to dashboard Cite

11Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-theart multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net. 12 1 Introduction 13 Preclinical imaging studies serve a fundamental role in biological and medical research, relating research results at the 14 molecular level to clinical application in diagnosis and therapy. Magnetic Resonance Imaging (MRI) represents about 23% of 15 all small-animal imaging studies providing the opportunity to monitor the development of pathological condition and response 16to treatment in a non-invasive way 1 . Its unique qualities also include the availability of different imaging contrasts, making MRI 17 especially useful in the context of preclinical neuroscience, from drug development 2 to structural, parametric and functional 18 studies 3 . 19 Skull-stripping and region segmentation represent an integral part of processing pipelines in both human and murine MR 20 imaging. Skull-stripping refers to the identification of the brain within the MRI volume and region segmentation refers to 21 the labeling of specific anatomical regions of interest (ROIs) within the brain. Especially in preclinical imaging, these tasks 22 are often performed manually. While manual segmentation represents the gold standard and is employed as the ground truth 23 when evaluating automated segmentation algorithms, it is a slow process that depends on the expertise of the individual expert. 24 Furthermore, manual segmentation suffers from both intra-and inter-rater variability. Dice scores are typically used to quantify 25 segmentation agreement 4 . Inter-and intra-rater Dice scores reported in literature range between 0.80 to 0.96 across different 26 segmentation tasks, and can be as low as 0.75 in the presence of pathological states 5-7 . 27In preclinical MRI, state-of-the-art automated region segmentation pipelines are based on atlas registration: individual MRI 28 volumes are aligned with a labeled template (atlas) to propagate...

show abstract

4D MRI of polycystic kidneys from rapamycin‐treated Glis3‐deficient mice

Cited by 9 publications

References 41 publications

Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction

Phenotyping by magnetic resonance imaging nondestructively measures glomerular number and volume distribution in mice with and without nephron reduction

Dynamic contrast-enhanced MRI promotes early detection of toxin-induced acute kidney injury

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Contact Info

Product

Resources

About