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Chronic kidney disease (CKD) is characterized by inflammation and fibrosis in the kidney. Renal biopsies and estimated glomerular filtration rate (eGFR) remain the standard of care, but these endpoints have limitations in detecting the stage, progression, and spatial distribution of fibrotic pathology in the kidney. MRI diffusion tensor imaging (DTI) has emerged as a promising noninvasive technology to evaluate renal fibrosis in vivo both in clinical and preclinical studies. However, these imaging studies have not systematically identified fibrosis particularly deeper in the kidney where biopsy sampling is limited, or completed an extensive analysis of whole organ histology, blood biomarkers, and gene expression to evaluate the relative strengths and weaknesses of MRI for evaluating renal fibrosis. In this study, we performed DTI in the sodium oxalate mouse model of CKD. The DTI parameters fractional anisotropy, apparent diffusion coefficient, and axial diffusivity were compared between the control and oxalate groups with region of interest (ROI) analysis to determine changes in the cortex and medulla. In addition, voxel-based analysis (VBA) was implemented to systematically identify local regions of injury over the whole kidney. DTI parameters were found to be significantly different in the medulla by both ROI analysis and VBA, which also spatially matched with collagen III immunohistochemistry (IHC). The DTI parameters in this medullary region exhibited moderate to strong correlations with histology, blood biomarkers, hydroxyproline, and gene expression. Our results thus highlight the sensitivity of DTI to the heterogeneity of renal fibrosis and importance of whole kidney noninvasive imaging. NEW & NOTEWORTHY: Chronic kidney disease (CKD) can be characterized by inflammation and fibrosis of the kidney. Although standard of care methods have been limited in scope, safety, and spatial distribution, MRI diffusion tensor imaging (DTI) has emerged as a promising noninvasive technology to evaluate renal fibrosis in vivo. In this study, we performed DTI in an oxalate mouse model of CKD to systematically identify local kidney injury. DTI parameters strongly correlated with histology, blood biomarkers, hydroxyproline, and gene expression. [ABSTRACT FROM AUTHOR]

This manuscript describes the ISMRM OSIPI (Open Science Initiative for Perfusion Imaging) lexicon for dynamic contrast‐enhanced and dynamic susceptibility‐contrast MRI. The lexicon was developed by Taskforce 4.2 of OSIPI to provide standardized definitions of commonly used quantities, models, and analysis processes with the aim of reducing reporting variability. The taskforce was established in February 2020 and consists of medical physicists, engineers, clinicians, data and computer scientists, and DICOM (Digital Imaging and Communications in Medicine) standard experts. Members of the taskforce collaborated via a slack channel and quarterly virtual meetings. Members participated by defining lexicon items and reporting formats that were reviewed by at least two other members of the taskforce. Version 1.0.0 of the lexicon was subject to open review from the wider perfusion imaging community between January and March 2022, and endorsed by the Perfusion Study Group of the ISMRM in the summer of 2022. The initial scope of the lexicon was set by the taskforce and defined such that it contained a basic set of quantities, processes, and models to enable users to report an end‐to‐end analysis pipeline including kinetic model fitting. We also provide guidance on how to easily incorporate lexicon items and definitions into free‐text descriptions (e.g., in manuscripts and other documentation) and introduce an XML‐based pipeline encoding format to encode analyses using lexicon definitions in standardized and extensible machine‐readable code. The lexicon is designed to be open‐source and extendable, enabling ongoing expansion of its content. We hope that widespread adoption of lexicon terminology and reporting formats described herein will increase reproducibility within the field. [ABSTRACT FROM AUTHOR]

The article discusses the road to the ISMRM OSIPI, a community-led initiative aimed at improving the reproducibility and harmonization of perfusion MRI. The initiative was launched in response to a survey sent out to members of the ISMRM Perfusion Study Group, which received strong interest and support from participants. The article outlines the mission and governance structure of OSIPI, as well as its specific aims and task forces. The results of the first roadmap are also shared, highlighting the resources produced by the initiative. With over 75 contributing volunteers, OSIPI is positioned to serve as a centralized platform for perfusion resources for researchers and clinicians. [Extracted from the article]

Background: Diffusion‐weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease. Purpose: To compare 14 site‐specific parametric fitting implementations applied to the same dataset of whole‐mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms. Study Type: Prospective. Population: Thirty‐three patients prospectively imaged prior to prostatectomy. Field Strength/Sequence: 3 T, field‐of‐view optimized and constrained undistorted single‐shot DWI sequence. Assessment: Datasets, including a noise‐free digital reference object (DRO), were distributed to the 14 teams, where locally implemented DWI parameter maps were calculated, including mono‐exponential apparent diffusion coefficient (MEADC), kurtosis (K), diffusion kurtosis (DK), bi‐exponential diffusion (BID), pseudo‐diffusion (BID*), and perfusion fraction (F). The resulting parametric maps were centrally analyzed, where differentiation of benign from cancerous tissue was compared between DWI parameters and the fitting algorithms with a receiver operating characteristic area under the curve (ROC AUC). Statistical Test: Levene's test, P < 0.05 corrected for multiple comparisons was considered statistically significant. Results: The DRO results indicated minimal discordance between sites. Comparison across sites indicated that K, DK, and MEADC had significantly higher prostate cancer detection capability (AUC range = 0.72–0.76, 0.76–0.81, and 0.76–0.80 respectively) as compared to bi‐exponential parameters (BID, BID*, F) which had lower AUC and greater between site variation (AUC range = 0.53–0.80, 0.51–0.81, and 0.52–0.80 respectively). Post‐processing parameters also affected the resulting AUC, moving from, for example, 0.75 to 0.87 for MEADC varying cluster size. Data Conclusion: We found that conventional diffusion models had consistent performance at differentiating prostate cancer from benign tissue. Our results also indicated that post‐processing decisions on DWI data can affect sensitivity and specificity when applied to radiological–pathological studies in prostate cancer. Level of Evidence: 1 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Background: Dynamic susceptibility contrast (DSC)-MRI analysis pipelines differ across studies and sites, potentially confounding the clinical value and use of the derived biomarkers.Purpose/hypothesis: To investigate how postprocessing steps for computation of cerebral blood volume (CBV) and residue function dependent parameters (cerebral blood flow [CBF], mean transit time [MTT], capillary transit heterogeneity [CTH]) impact glioma grading.Study Type: Retrospective study from The Cancer Imaging Archive (TCIA).Population: Forty-nine subjects with low- and high-grade gliomas.Field Strength/sequence: 1.5 and 3.0T clinical systems using a single-echo echo planar imaging (EPI) acquisition.Assessment: Manual regions of interest (ROIs) were provided by TCIA and automatically segmented ROIs were generated by k-means clustering. CBV was calculated based on conventional equations. Residue function dependent biomarkers (CBF, MTT, CTH) were found by two deconvolution methods: circular discretization followed by a signal-to-noise ratio (SNR)-adapted eigenvalue thresholding (Method 1) and Volterra discretization with L-curve-based Tikhonov regularization (Method 2).Statistical Tests: Analysis of variance, receiver operating characteristics (ROC), and logistic regression tests.Results: MTT alone was unable to statistically differentiate glioma grade (P > 0.139). When normalized, tumor CBF, CTH, and CBV did not differ across field strengths (P > 0.141). Biomarkers normalized to automatically segmented regions performed equally (rCTH AUROC is 0.73 compared with 0.74) or better (rCBF AUROC increases from 0.74-0.84; rCBV AUROC increases 0.78-0.86) than manually drawn ROIs. By updating the current deconvolution steps (Method 2), rCTH can act as a classifier for glioma grade (P < 0.007), but not if processed by current conventional DSC methods (Method 1) (P > 0.577). Lastly, higher-order biomarkers (eg, rCBF and rCTH) along with rCBV increases AUROC to 0.92 for differentiating tumor grade as compared with 0.78 and 0.86 (manual and automatic reference regions, respectively) for rCBV alone.Data Conclusion: With optimized analysis pipelines, higher-order perfusion biomarkers (rCBF and rCTH) improve glioma grading as compared with CBV alone. Additionally, postprocessing steps impact thresholds needed for glioma grading.Level Of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:547-553. [ABSTRACT FROM AUTHOR]

Purpose: Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and T2∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi‐echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi‐echo DSC‐MRI perfusion protocol. Methods: Using a population‐based DSC‐MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi‐echo DSC‐MRI protocols with standard single‐echo protocols. Results: Multi‐echo DSC‐MRI is highly consistent across all protocols, and multi‐echo rCBV (with or without use of a preload dose) had higher accuracy than single‐echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi‐echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi‐echo rCBF was less biased than single‐echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. Conclusions: Multi‐echo acquisitions were more robust than single‐echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi‐echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T2∗ leakage effects remains compulsory for high rCBV accuracy. [ABSTRACT FROM AUTHOR]

Background: A previous study demonstrated the feasibility of using 3D radial ultrashort echo time (UTE) oxygen-enhanced MRI (UTE OE-MRI) for functional imaging of healthy human lungs. The repeatability of quantitative measures from UTE OE-MRI needs to be established prior to its application in clinical research.Purpose: To evaluate repeatability of obstructive patterns in asthma and cystic fibrosis (CF) with UTE OE-MRI with isotropic spatial resolution and full chest coverage.Study Type: Volunteer and patient repeatability.Population: Eighteen human subjects (five asthma, six CF, and seven normal subjects).Field Strength/sequence: Respiratory-gated free-breathing 3D radial UTE (80 μs) sequence at 1.5T.Assessment: Two 3D radial UTE volumes were acquired sequentially under normoxic and hyperoxic conditions. A subset of subjects underwent repeat acquisitions on either the same day or ≤15 days apart. Asthma and CF subjects also underwent spirometry. A workflow including deformable registration and retrospective lung density correction was used to compute 3D isotropic percent signal enhancement (PSE) maps. Median PSE (MPSE) and ventilation defect percent (VDP) of the lung were measured from the PSE map.Statistical Tests: The relations between MPSE, VDP, and spirometric measures were assessed using Spearman correlations. The test-retest repeatability was evaluated using Bland-Altman analysis and intraclass correlation coefficients (ICC).Results: Ventilation measures in normal subjects (MPSE = 8.0%, VDP = 3.3%) were significantly different from those in asthma (MPSE = 6.0%, P = 0.042; VDP = 21.7%, P = 0.018) and CF group (MPSE = 4.5%, P = 0.0006; VDP = 27.2%, P = 0.002). MPSE correlated significantly with forced expiratory lung volume in 1 second percent predicted (ρ = 0.72, P = 0.017). The ICC of the test-retest VDP and MPSE were both ≥0.90. In all subject groups, an anterior/posterior gradient was observed with higher MPSE and lower VDP in the posterior compared to anterior regions (P ≤ 0.0021 for all comparisons).Data Conclusion: 3D radial UTE OE-MRI supports quantitative differentiation of diseased vs. healthy lungs using either whole lung VDP or MPSE with excellent test-retest repeatability.Level Of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1287-1297. [ABSTRACT FROM AUTHOR]

Purpose To assess the feasibility of spatial-temporal constrained reconstruction for accelerated regional lung perfusion using highly undersampled dynamic contrast-enhanced (DCE) three-dimensional (3D) radial MRI with ultrashort echo time (UTE). Methods A combined strategy was used to accelerate DCE MRI for 3D pulmonary perfusion with whole lung coverage. A highly undersampled 3D radial UTE MRI acquisition was combined with an iterative constrained reconstruction exploiting principal component analysis and wavelet soft-thresholding for dimensionality reduction in space and time. The performance of the method was evaluated using a 3D fractal-based DCE digital lung phantom. Simulated perfusion maps and contrast enhancement curves were compared with ground truth using the structural similarity index (SSIM) to determine robust threshold and regularization levels. Feasibility studies were then performed in a canine and a human subject with 3D radial UTE (TE = 0.08 ms) acquisition to assess feasibility of mapping regional 3D perfusion. Results The method was able to accurately recover perfusion maps in the phantom with a nominal isotropic spatial resolution of 1.5 mm (SSIM of 0.949). The canine and human subject studies demonstrated feasibility for providing artifact-free perfusion maps in a simple 3D breath-held acquisition. Conclusion The proposed method is promising for fast and flexible 3D pulmonary perfusion imaging. Magn Reson Med 73:555-564, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]