Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom

Malyarenko, Dariya I.; Swanson, Scott D.; Konar, Amaresha Shridhar; LoCastro, Eve; Paudyal, Ramesh; Liu, Michael Z.; Jambawalikar, Sachin; Schwartz, Lawrence H.; Shukla–Dave, Amita; Chenevert, Thomas L.

doi:10.18383/j.tom.2018.00030

Cited by 14 publications

(19 citation statements)

References 26 publications

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“…Plots of log(ADC) versus inverse temperature in Figure 1c illustrate Arrhenius model fit (Equations ( 2) and ( 3)) used to derive calibration coefficients, C1 and C2, by linear regression (Equation ( 3)). Good alignment of current K40 calibration data to 3-year-old multi-site data 0%, 20%, and 40% [PVP], 25 and other previous K40 PVP studies 15,24 at intermediate temperatures is shown in Figure S4a. This figure also illustrates that for intermediate PVP concentrations not directly calibrated in this work, the linear interpolation can be performed between two calibrated values.…”

Section: Resultssupporting

confidence: 55%

Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions

et al. 2022

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The goal of this work is to provide temperature and concentration calibration of water diffusivity in polyvinylpyrrolidone (PVP) solutions used in phantoms to assess system bias and linearity in apparent diffusion coefficient (ADC) measurements. Method: ADC measurements were performed for 40 kDa (K40) PVP of six concentrations (0%, 10%, 20%, 30%, 40%, and 50% by weight) at three temperatures (19.5 • C, 22.5 • C, and 26.4 • C), with internal phantom temperature monitored by optical thermometer (±0.2 • C). To achieve ADC measurement and fit accuracy of better than 0.5%, three orthogonal diffusion gradients were calibrated using known water diffusivity at 0 • C and system gradient nonlinearity maps. Noise-floor fit bias was also controlled by limiting the maximum b-value used for ADC calculation of each sample. The ADC temperature dependence was modeled by Arrhenius functions of each PVP concentration. The concentration dependence was modeled by quadratic function for ADC normalized by the theoretical water diffusion values. Calibration coefficients were obtained from linear regression model fits. Results: Measured phantom ADC values increased with temperature and decreasing PVP concentration, [PVP]. The derived Arrhenius model parameters for [PVP] between 0% and 50%, are reported and can be used for K40 ADC temperature calibration with absolute ADC error within ±0.016 µm 2 /ms. Arrhenius model fit parameters normalized to water value scaled with [PVP] between 10% and 40%, and proportional change in activation energy increased faster than collision frequency. ADC normalization by water diffusivity, D W , from the Speedy-Angell relation accounted for the bulk of temperature dependence (±0.035 µm 2 /ms) and yielded quadratic calibration for ADC PVP /D W = (12.5 ± 0.7) ⋅10 −5 ⋅[PVP] 2 − (23.2 ± 0.3)⋅10 −3 ⋅[PVP]+1, nearly independent of PVP molecular weight and temperature. Conclusion:The study provides ground-truth ADC values for K40 PVP solutions commonly used in diffusion phantoms for scanning at ambient room temperature. The described procedures and the reported calibration can be used for quality control and standardization of measured ADC values of PVP at different concentrations and temperatures.

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Section: Resultssupporting

confidence: 55%

Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions

et al. 2022

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“…Although phantoms based on various easily obtainable substances (including cream and asparagus stems 130 ) provide restricted diffusion, they offer a limited range of diffusion properties. This limitation has been addressed through a variety of synthetic phantoms (e.g., particle suspensions, 130 fibers, 131 and liquid crystal systems 127 ), which provide various diffusion restrictions, but also suffer from a limited range of diffusion properties.…”

Section: Current Status Of Validation and Dissemination Of Diffusion Mrimentioning

confidence: 99%

Quantitative diffusion MRI of the abdomen and pelvis

2021

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Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion‐weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal‐to‐noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.

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“…In conclusion, development and validation of quantitative imaging tools for supporting cancer patient trials and ultimately routine clinical adoption have been the focus of the National Institutes of Health Quantitative Imaging Network over the past decade (34,(36)(37)(38). In this present study, we found that ADC histogram percentiles down to 15% have high repeatability and reproducibility, comparable to mean ADC, while low-ADC volumetric measures were substantially less repeatable.…”

Section: Resultsmentioning

confidence: 49%

Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial

Newitt¹,

Amouzandeh

Partridge

et al. 2020

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Mean tumor apparent diffusion coefficient (ADC) of breast cancer showed excellent repeatability but only moderate predictive power for breast cancer therapy response in the ACRIN 6698 multicenter imaging trial. Previous single-center studies have shown improved predictive performance for alternative ADC histogram metrics related to low ADC dense tumor volume. Using test/retest (TT/RT) 4 b-value diffusion-weighted imaging acquisitions from pretreatment or early-treatment time-points on 71 ACRIN 6698 patients, we evaluated repeatability for ADC histogram metrics to establish confidence intervals and inform predictive models for future therapy response analysis. Histograms were generated using regions of interest (ROIs) defined separately for TT and RT diffusion-weighted imaging. TT/RT repeatability and intra- and inter-reader reproducibility (on a 20-patient subset) were evaluated using wCV and Bland–Altman limits of agreement for histogram percentiles, low-ADC dense tumor volumes, and fractional volumes (normalized to total histogram volume). Pearson correlation was used to reveal connections between metrics and ROI variability across the sample cohort. Low percentiles (15th and 25th) were highly repeatable and reproducible, wCV < 8.1%, comparable to mean ADC values previously reported. Volumetric metrics had higher wCV values in all cases, with fractional volumes somewhat better but at least 3 times higher than percentile wCVs. These metrics appear most sensitive to ADC changes around a threshold of 1.2 μm2/ms. Volumetric results were moderately to strongly correlated with ROI size. In conclusion, Lower histogram percentiles have comparable repeatability to mean ADC, while ADC-thresholded volumetric measures currently have poor repeatability but may benefit from improvements in ROI techniques.

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Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom

Cited by 14 publications

References 26 publications

Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions

Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions

Quantitative diffusion MRI of the abdomen and pelvis

Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial

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