Distributed capillary adiabatic tissue homogeneity model in parametric multi‐channel blind AIF estimation using DCE‐MRI

Kratochvíla, Jiří; Jiřík, Radovan; Bartoš, Michal; Standara, Michal; Starčuk, Zenon; Taxt, Torfinn

doi:10.1002/mrm.25619

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…Many AIF derivation methods [5,6,11–16] require an AIF amplitude scaling in the process. Since the ground truth of the scaling factor is often unknown (for example, using muscle as a reference tissue for AIF scaling runs into the problem of varying pharmacokinetic properties of the muscle itself under different physiological conditions), this makes investigation of AIF scaling effects important.…”

Section: Discussionmentioning

confidence: 99%

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

Cai

Moloney

et al. 2016

Journal of Magnetic Resonance

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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 (Ktrans) 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.

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Section: Discussionmentioning

confidence: 99%

“…Alternative methods mitigating the challenges of AIF quantification from blood signal have been proposed. Examples of these include reference tissue methods [11–13] and blinded AIF estimation [14–16]. Unfortunately, these approaches also require AIF amplitude scaling after derivation of the AIF time-course curves.…”

Section: Introductionmentioning

confidence: 99%

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

Cai

Moloney

et al. 2016

Journal of Magnetic Resonance

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“…Shi et al achieved more efficient AIF detection by using an accelerated version of affinity propagation (AP) clustering that differed from the original and manual AP in the presence of free‐breathing . An alternative approach, blind multichannel deconvolution with a distributed capillary adiabatic tissue homogeneity model (DCATH), was presented by Kratochvila et al to resolve the problems of contrast dispersion, common artifacts, and partial volume effects present in earlier AIF evaluation approaches …”

Section: Renal Fmrimentioning

confidence: 99%

Renal Functional MRI and Its Application

Zhou

Wang

Zeng

et al. 2018

Magnetic Resonance Imaging

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“…A testing clinical dataset of a renal‐cell‐carcinoma‐metastasis patient (details in Ref. ) was acquired on a Magnetom Avanto 1.5 T MRI scanner (Siemens AG, Munich, Germany) using the T1‐weighted 2D saturation‐recovery prepared Turbo FLASH (nonselective SR pulse): TR/TE/TI 400/1.09/200 ms, FA 16°, acquisition matrix 128 × 128, temporal resolution 1.2 s, three coronal slices, total scan time 10 min. The contrast agent bolus of 7.5 ml (Gadovist – Bayer Schering Pharma, Berlin, Germany) was injected into antecubital vein.…”

Section: Methodsmentioning

confidence: 99%

“…The pre‐contrast scans acquisition preceded with the same parameters except TI (500, 1000, 3000 ms, five frames per each) to perform conversion to contrast‐agent concentration . The AIF was estimated using multi‐channel blind deconvolution …”

Section: Methodsmentioning

confidence: 99%

Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

Bartoš

Rajmic

Šorel

et al. 2019

Magnetic Resonance in Med

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Purpose The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast‐enhanced MRI (DCE‐MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability‐surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. Theory and Methods Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. Results The proposed algorithm helps to reduce noise in the estimated perfusion‐parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. Conclusion The reliability of the DCE‐MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.

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Distributed capillary adiabatic tissue homogeneity model in parametric multi‐channel blind AIF estimation using DCE‐MRI

Abstract: Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

Cited by 14 publications

References 39 publications

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

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

Renal Functional MRI and Its Application

Spatially regularized estimation of the tissue homogeneity model parameters in DCE‐MRI using proximal minimization

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