Magnetic resonance data modeling: The Bayesian analysis toolbox

Quirk, James D.; Bretthorst, G. Larry; Garbow, Joel R.; Ackerman, Joseph J. H.

doi:10.1002/cmr.a.21467

Cited by 14 publications

(12 citation statements)

References 63 publications

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“…Spectra were analyzed with the Bayesian Toolbox (https://bayesiananalysis.wustl.edu/) "Bayes Analyze" package to model the resonances from glucose, lactate, and TSP. 31 This time-domain (FID) signal modeling minimized interference from water and protein resonances in quantifying the signals of interest (glucose, lactate, and TSP), enabling precise determination of resonance amplitudes, thus glucose and lactate concentrations. Lactate and glucose concentrations were established using the TSP resonance amplitude as a concentration reference.…”

Section: H-nmr Analysismentioning

confidence: 99%

Quantifying dynamic range in red blood cell energetics: Evidence of progressive energy failure during storage

Rogers

Brummet

et al. 2021

Transfusion

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Background During storage, red blood cells (RBCs) undergo significant biochemical and morphologic changes, referred to collectively as the “storage lesion”. It was hypothesized that these defects may arise from disrupted oxygen‐based regulation of RBC energy metabolism, with resultant depowering of intrinsic antioxidant systems. Study Design and Methods As a function of storage duration, the dynamic range in RBC metabolic response to three models of biochemical oxidant stress (methylene blue, hypoxanthine/xanthine oxidase, and diamide) was assessed, comparing glycolytic flux by NMR and UHPLC–MS methodologies. Blood was processed/stored under standard conditions (AS‐1 additive solution) with leukoreduction. Over a 6‐week period, RBC metabolic and antioxidant status were assessed at baseline and following exposure to the three biochemical oxidant models. Comparison was made of glycolytic flux (1H‐NMR tracking of [2‐13C]‐glucose and metabolomic phenotyping with [1,2,3‐13C3] glucose), reducing equivalent (NADPH/NADP+) recycling, and thiol‐based (GSH/GSSG) antioxidant status. Results As a function of storage duration, we observed the following: (1) a reduction in baseline hexose monophosphate pathway (HMP) flux, the sole pathway responsible for the regeneration of the essential reducing equivalent NADPH; with (2) diminished stress‐based dynamic range in both overall glycolytic as well as proportional HMP flux. In addition, progressive with storage duration, RBCs showed (3) constraint in reducing equivalent (NADPH) recycling capacity, (4) loss of thiol based (GSH) recycling capacity, and (5) dysregulation of metabolon assembly at the cytoplasmic domain of Band 3 membrane protein (cdB3). Conclusion Blood storage disturbs normal RBC metabolic control, depowering antioxidant capacity and enhancing vulnerability to oxidative injury.

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Section: H-nmr Analysismentioning

confidence: 99%

Quantifying dynamic range in red blood cell energetics: Evidence of progressive energy failure during storage

Rogers

Brummet

et al. 2021

Transfusion

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“…All images were processed with an “un-ringing” algorithm ( 25 ) and a Gaussian filter (σ = 0.75). The R1, R2, and ADC parametric maps were computed on a voxel-wise basis with the Bayesian Toolbox ( 26 ), a data modeling software package based upon the precepts of Bayesian probability theory. The voxel-wise maps of MTR and DCE AUC were calculated in MATLAB (The Mathworks, Natick, MA).…”

Section: Methodsmentioning

confidence: 99%

Distinguishing Tumor Admixed in a Radiation Necrosis (RN) Background: 1H and 2H MR With a Novel Mouse Brain-Tumor/RN Model

Song

Engelbach

et al. 2022

Front. Oncol.

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PurposeDistinguishing radiation necrosis (RN) from recurrent tumor remains a vexing clinical problem with important health-care consequences for neuro-oncology patients. Here, mouse models of pure tumor, pure RN, and admixed RN/tumor are employed to evaluate hydrogen (1H) and deuterium (2H) magnetic resonance methods for distinguishing RN vs. tumor. Furthermore, proof-of-principle, range-finding deuterium (2H) metabolic magnetic resonance is employed to assess glycolytic signatures distinguishing RN vs. tumor.Materials and MethodsA pipeline of common quantitative 1H MRI contrasts, including an improved magnetization transfer ratio (MTR) sequence, and 2H magnetic resonance spectroscopy (MRS) following administration of 2H-labeled glucose, was applied to C57BL/6 mouse models of the following: (i) late time-to-onset RN, occurring 4–5 weeks post focal 50-Gy (50% isodose) Gamma Knife irradiation to the left cerebral hemisphere, (ii) glioblastoma, growing ~18–24 days post implantation of 50,000 mouse GL261 tumor cells into the left cerebral hemisphere, and (iii) mixed model, with GL261 tumor growing within a region of radiation necrosis (1H MRI only). Control C57BL/6 mice were also examined by 2H metabolic magnetic resonance.ResultsDifferences in quantitative 1H MRI parametric values of R1, R2, ADC, and MTR comparing pure tumor vs. pure RN were all highly statistically significant. Differences in these parameter values and DCEAUC for tumor vs. RN in the mixed model (tumor growing in an RN background) are also all significant, demonstrating that these contrasts—in particular, MTR—can effectively distinguish tumor vs. RN. Additionally, quantitative 2H MRS showed a highly statistically significant dominance of aerobic glycolysis (glucose ➔ lactate; fermentation, Warburg effect) in the tumor vs. oxidative respiration (glucose ➔ TCA cycle) in the RN and control brain.ConclusionsThese findings, employing a pipeline of quantitative 1H MRI contrasts and 2H MRS following administration of 2H-labeled glucose, suggest a pathway for substantially improving the discrimination of tumor vs. RN in the clinic.

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“…T1, T2, and ADC parametric maps and uncertainties were derived with the Bayesian Toolbox (12), a data modeling software package based upon the precepts of Bayesian probability theory (13, 14) available for free download for noncommercial uses (http://bayesiananalysis.wustl.edu/). These analyses were performed in 2 different ways: (1) averaging the data and modeling the combined ROI data, indicated below as <ROI> and (2) modeling each voxel in the ROI independently and averaging the parameter estimates, indicated below as <Voxel>.…”

Section: Methodsmentioning

confidence: 99%

Test–Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

Quirk

Engelbach

et al. 2019

Tomography

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Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest–averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm2/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test–retest coefficient-of-variation (CVWS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CVWS. Determination of T2 is also robust with a ∼3% CVWS. Measurements of T1 and ADC are less robust with CVWS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and Ktrans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CVWS.

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Magnetic resonance data modeling: The Bayesian analysis toolbox

Cited by 14 publications

References 63 publications

Quantifying dynamic range in red blood cell energetics: Evidence of progressive energy failure during storage

Quantifying dynamic range in red blood cell energetics: Evidence of progressive energy failure during storage

Distinguishing Tumor Admixed in a Radiation Necrosis (RN) Background: 1H and 2H MR With a Novel Mouse Brain-Tumor/RN Model

Test–Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

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