The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge.

PURPOSE: K trans $$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for K trans $$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize K trans $$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate K trans $$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for K trans $$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' K trans $$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIP I gold $$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in K trans $$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within K trans $$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations.

Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (kio [s-1 ]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na+ , K+ -ATPase (NKA), c MRNKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal 1 H2 O MR kio determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic kio biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine kio in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/µL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water (ρ = V = 0; kio undefined; diffusion coefficient, D0 ) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (Dn ) is diffusion time-dependent, a manifestation of the "diffusion dispersion". When the "well-mixed" (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (kio ) cytoplasmic membranes, and less so by Dn magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.

Metabolic activity diffusion imaging (MADI): II. Noninvasive, high-resolution human brain mapping of sodium pump flux and cell metrics.

We introduce a new 1 H2 O magnetic resonance approach: metabolic activity diffusion imaging (MADI). Numerical diffusion-weighted imaging decay simulations characterized by the mean cellular water efflux (unidirectional) rate constant (kio ), mean cell volume (V), and cell number density (ρ) are produced from Monte Carlo random walks in virtual stochastically sized/shaped cell ensembles. Because of active steady-state trans-membrane water cycling (AWC), kio reflects the cytolemmal Na+ , K+ ATPase (NKA) homeostatic cellular metabolic rate (c MRNKA ). A digital 3D "library" contains thousands of simulated single diffusion-encoded (SDE) decays. Library entries match well with disparate, animal, and human experimental SDE decays. The V and ρ values are consistent with estimates from pertinent in vitro cytometric and ex vivo histopathological literature: in vivo V and ρ values were previously unavailable. The library allows noniterative pixel-by-pixel experimental SDE decay library matchings that can be used to advantage. They yield proof-of-concept MADI parametric mappings of the awake, resting human brain. These reflect the tissue morphology seen in conventional MRI. While V is larger in gray matter (GM) than in white matter (WM), the reverse is true for ρ. Many brain structures have kio values too large for current, invasive methods. For example, the median WM kio is 22s-1 ; likely reflecting mostly exchange within myelin. The kio •V product map displays brain tissue c MRNKA variation. The GM activity correlates, quantitatively and qualitatively, with the analogous resting-state brain 18 FDG-PET tissue glucose consumption rate (t MRglucose ) map; but noninvasively, with higher spatial resolution, and no pharmacokinetic requirement. The cortex, thalamus, putamen, and caudate exhibit elevated metabolic activity. MADI accuracy and precision are assessed. The results are contextualized with literature overall homeostatic brain glucose consumption and ATP production/consumption measures. The MADI/PET results suggest different GM and WM metabolic pathways. Preliminary human prostate results are also presented.

Vascular disease risk factors in multiple sclerosis: Effect on metabolism and brain volumes.

BACKGROUND: Vascular disease risk factors (VDRF) such as hypertension, hyperlipidemia, obesity, diabetes and heart disease likely play a role in disease progression in people with multiple sclerosis (PwMS) (Marrie, Rudick et al. 2010). Studies exploring the mechanistic connection between vascular disease and MS disease progression are scant. We hypothesized that phosphate energy metabolism impairment in PwMS with VDRFs (VDRF+) will be greater compared to PwMS without VDRFs (VDRF-) and is related to increased brain atrophy in VDRF+. To test this hypothesis, we planned to study the differences in the high energy phosphate (HEP) metabolites in cerebral gray matter as assessed by 31P magnetic resonance spectroscopic imaging (MRSI) and MRI brain volumetric in the VDRF+ and VDRF- PwMS at four different timepoints over a 3 yearlong period using a 7T MR system. We present here the results from the cross-sectional evaluation of HEP metabolites and brain volumes. We also evaluated the differences in clinical impairment, blood metabolic biomarkers and quality of life in VDRF+ and VDRF- PwMS in this cohort. METHODS: Group differences in high energy phosphate metabolites were assessed from a volume of interest in the occipital region using linear mixed models. Brain parenchymal and white matter lesion volumes were determined from MR anatomic images. We present here the cross-sectional analysis of the baseline data collected as part of a longitudinal 3 yearlong study where we obtained baseline and subsequent 6-monthly clinical and laboratory data and annual 7T MRI volumetric and 31P MR spectroscopic imaging (MRSI) data on 52 PwMS with and without VDRF. Key clinical and laboratory outcomes included: body mass index (BMI), waist and thigh circumferences and disability [Expanded Disability Status Scale (EDSS)], safety (complete blood count with differential, complete metabolic), lipid panel including total cholesterol and HbA1C. We analyzed clinical and laboratory data for the group differences using student's t or χ2 test. We investigated relationship between phosphate metabolites and VDRF using mixed effect linear regression. RESULTS: Complete MRI data were available for 29 VDRF+, age 56.3 (6.8) years [mean (SD)] (83% female), and 23 VDRF-, age 52.5 (7.5) years (57% female) individuals with MS. The mean value of normalized adenosine triphosphate (ATP) (calculated as the ratio of ATP to total phosphate signal in a voxel) was decreased by 4.5% (p < .05) in VDRF+ compared to VDRF- MS group. White matter lesion (WML) volume fraction in VDRF+ individuals {0.007 (0.007)} was more than doubled compared to VDRF- participants {0.003 (0.006), p= .02}. CONCLUSIONS: We found significantly lower brain ATP and higher inorganic phosphate (Pi) in those PwMS with VDRFs compared to those without. ATP depletion may reflect mitochondrial dysfunction. Ongoing longitudinal data analysis from this study, not presented here, will evaluate the relationship of phosphate metabolites, brain atrophy and disease progression in PwMS with and without vascular disease.

Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study.

We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%-57% and that of combined DCE- or combined DW-MRI markers to 57%-71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%-93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.

Modeling disease trajectory in Duchenne muscular dystrophy.

OBJECTIVE: To quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles. METHODS: MRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal changes in biomarkers were modeled with a cumulative distribution function using a nonlinear mixed-effects approach. RESULTS: MRS FF and MRI qT2 increased with DMD disease duration, with the progression time constants differing markedly between individuals and across muscles. The average age at half-maximal muscle involvement (µ) occurred 4.8 years earlier in vastus lateralis than soleus, and these measures were strongly associated with loss-of-ambulation age. Corticosteroid treatment was found to delay µ by 2.5 years on average across muscles, although there were marked differences between muscles with more slowly progressing muscles showing larger delay. CONCLUSIONS: MRS FF and MRI qT2 provide sensitive noninvasive measures of DMD progression. Modeling changes in these biomarkers across multiple muscles can be used to detect and monitor the therapeutic effects of corticosteroids on disease progression and to provide prognostic information on functional outcomes. This modeling approach provides a method to transform these MRI biomarkers into well-understood metrics, allowing concise summaries of DMD disease progression at individual and population levels. CLINICALTRIALSGOV IDENTIFIER: NCT01484678.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling.

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K(trans)) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

High-resolution steady-state cerebral blood volume maps in patients with central nervous system neoplasms using ferumoxytol, a superparamagnetic iron oxide nanoparticle.

Cerebral blood volume (CBV) measurement complements conventional magnetic resonance imaging (MRI) to indicate pathologies in the central nervous system (CNS). Dynamic susceptibility contrast (DSC) perfusion imaging is limited by low resolution and distortion. Steady-state (SS) imaging may provide higher resolution CBV maps but was not previously possible in patients. We tested the feasibility of clinical SS-CBV measurement using ferumoxytol, a nanoparticle blood pool contrast agent. SS-CBV measurement was analyzed at various ferumoxytol doses and compared with DSC-CBV using gadoteridol. Ninety nine two-day MRI studies were acquired in 65 patients with CNS pathologies. The SS-CBV maps showed improved contrast to noise ratios, decreased motion artifacts at increasing ferumoxytol doses. Relative CBV (rCBV) values obtained in the thalamus and tumor regions indicated good consistency between the DSC and SS techniques when the higher dose (510 mg) ferumoxytol was used. The SS-CBV maps are feasible using ferumoxytol in a clinical dose of 510 mg, providing higher resolution images with comparable rCBV values to the DSC technique. Physiologic imaging using nanoparticles will be beneficial in visualizing CNS pathologies with high vascularity that may or may not correspond with blood-brain barrier abnormalities.

Sort-first parallel volume rendering.

Sort-first distributions have been studied and used far less than sort-last distributions for parallel volume rendering, especially when the data are too large to be replicated fully. We demonstrate that sort-first distributions are not only a viable method of performing data-scalable parallel volume rendering, but more importantly they allow for a range of rendering algorithms and techniques that are not efficient with sort-last distributions. Several of these algorithms are discussed and two of them are implemented in a parallel environment: a new improved variant of early ray termination to speed up rendering when volumetric occlusion occurs and a volumetric shadowing technique that produces more realistic and informative images based on half angle slicing. Improved methods of distributing the computation of the load balancing and loading portions of a subdivided data set are also presented. Our detailed test results for a typical GPU cluster with distributed memory show that our sort-first rendering algorithm outperforms sort-last rendering in many scenarios.