Dataset Information

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue.

ABSTRACT: To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low-perfused tissues.Model data simulations relevant to brain and low-perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed at a series of signal-to-noise ratio (SNR) levels to assess the influence of noise on the fitting.The estimated IVIM parameters from model simulations were found significantly different (P?

SUBMITTER: Meeus EM

PROVIDER: S-EPMC5412931 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue.

Meeus Emma M EM Novak Jan J Withey Stephanie B SB Zarinabad Niloufar N Dehghani Hamid H Peet Andrew C AC

Journal of magnetic resonance imaging : JMRI 20160822 5

<h4>Purpose</h4>To investigate the robustness of constrained and simultaneous intravoxel incoherent motion (IVIM) fitting methods and the estimated IVIM parameters (D, D* and f) for applications in brain and low-perfused tissues.<h4>Materials and methods</h4>Model data simulations relevant to brain and low-perfused tumor tissues were computed to assess the accuracy, relative bias, and reproducibility (CV%) of the fitting methods in estimating the IVIM parameters. The simulations were performed a ...[more]

PMID: 27545824

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Project description:PurposeEarlier work showed that IVIM-NETorig , an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim , and characterizes its superior performance in pancreatic cancer patients.MethodIn simulations (signal-to-noise ratio [SNR] = 20), the accuracy, independence, and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, number of hidden layers, dropout, batch normalization, learning rate), by calculating the normalized root-mean-square error (NRMSE), Spearman's ρ, and the coefficient of variation (CVNET ), respectively. The best performing network, IVIM-NEToptim was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NEToptim 's performance was evaluated in an independent dataset of 23 patients with pancreatic ductal adenocarcinoma. Fourteen of the patients received no treatment between two repeated scan sessions and nine received chemoradiotherapy between the repeated sessions. Intersession within-subject standard deviations (wSD) and treatment-induced changes were assessed.ResultsIn simulations (SNR = 20), IVIM-NEToptim outperformed IVIM-NETorig in accuracy (NRMSE(D) = 0.177 vs 0.196; NMRSE(f) = 0.220 vs 0.267; NMRSE(D*) = 0.386 vs 0.393), independence (ρ(D*, f) = 0.22 vs 0.74), and consistency (CVNET (D) = 0.013 vs 0.104; CVNET (f) = 0.020 vs 0.054; CVNET (D*) = 0.036 vs 0.110). IVIM-NEToptim showed superior performance to the LS and Bayesian approaches at SNRs < 50. In vivo, IVIM-NEToptim showed significantly less noisy parameter maps with lower wSD for D and f than the alternatives. In the treated cohort, IVIM-NEToptim detected the most individual patients with significant parameter changes compared to day-to-day variations.ConclusionIVIM-NEToptim is recommended for accurate, informative, and consistent IVIM fitting to DWI data.

Dataset Information

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue.

Publications

Evaluation of intravoxel incoherent motion fitting methods in low-perfused tissue.

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