Estimating the arterial input function from dynamic contrast‐enhanced MRI data with compensation for flow enhancement (II): Applications in spine diagnostics and assessment of crohn's disease

Schie, Jeroen J.N. van; Lavini, Cristina; Vliet, Lucas J. van; Kramer, Gem; Bos, Indra Pieters ‐ van den; Marcus, J. Tim; Stoker, Jaap; Vos, Frans M.

doi:10.1002/jmri.25905

Cited by 5 publications

(5 citation statements)

References 29 publications

(55 reference statements)

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“…Studies that compare scan-specific and population-based AIFs in dynamic contrast-enhanced (DCE)-MRI differ in their recommendations. Some prefer scan-specific AIFs (32)(33)(34), some prefer population-based AIFs (19,35), and some recommend population-based AIFs in the absence of suitable scan-specific AIFs (32,36). The signal-to-noise ratio in DCE-MRI is lower than in DSC-MRI, due to lower temporal resolution, and could explain the reported preferences for population-based AIFs.…”

Section: Discussionmentioning

confidence: 99%

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

et al. 2022

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Background Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) could be helpful to separate true disease progression from pseudo-progression in brain metastases when assessing the need for retreatment. However, the selection of arterial input functions (AIFs) is not standardized for analysis, limiting its use for this application. Purpose To compare population-based AIFs, AIFs specific to each patient, and AIFs specific to every visit in the longitudinal follow-up of brain metastases. Material and Methods Longitudinal data were collected from eight patients before treatment (6 of 8 patients) and after treatment (6–17 visits). Imaging was performed using a 1.5-T MRI system. Lesions were segmented by subtracting precontrast images from postcontrast images. Cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were computed, and Pearson's product moment correlation coefficients were calculated to evaluate similarity of DSC parameters dependent on various AIF choices across time. AIF shape characteristics were compared. Parameter differences between white matter (WM) and gray matter (GM) were obtained to determine which AIF choice maximizes tissue differentiation. Results Although DSC parameters follow similar patterns in time, the various AIF selections cause large parameter variations with relative standard deviations of up to ±60%. AIFs sampled in one patient across sessions more similar in shape than AIFs sampled across patients. Estimates of rCBV based on scan-specific AIFs differentiated better between perfusion in WM and GM than patient-specific or population-based AIFs ( P ≤ 0.02). Conclusion Results indicate that scan-specific AIFs are the best choice for DSC-MRI parameter estimations in the longitudinal follow-up of brain metastases.

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Section: Discussionmentioning

confidence: 99%

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

et al. 2022

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“…RRIFT does not need the AIF peak and thus does not need a high temporal resolution and is unaffected by factors such as saturation effects, but there are some remaining obstacles such as inflow and partial volume effects which will affect the AIF tail measurement. The impact of these factors has been studied at fast temporal resolutions which are typical in DCE‐MRI . The inflow effect can lead to a bias of roughly 40% but this can be corrected with additional measurements, or the effect can be minimized by changing the slice orientation or by optimal selection of the AIF voxel .…”

Section: Discussionmentioning

confidence: 99%

“…The inflow effect can lead to a bias of roughly 40% but this can be corrected with additional measurements, or the effect can be minimized by changing the slice orientation or by optimal selection of the AIF voxel . The impact of partial volume effects is difficult to quantify because they have a non‐linear effect on the AIF shape and the severity depends on the acquired spatial resolution. Since RRIFT is viable at slower temporal resolutions, the extra scan time could be used to improve volume coverage, spatial resolution, and noise.…”

Section: Discussionmentioning

confidence: 99%

“…The impact of these factors has been studied at fast temporal resolutions which are typical in DCE-MRI. 12,[45][46][47] The inflow effect can lead to a bias of roughly 40% but this can be corrected with additional measurements, 45 or the effect can be minimized by changing the slice orientation 46 or by optimal selection of the AIF voxel. 9 The impact of partial volume effects is difficult to quantify because they have a non-linear effect on the AIF shape and the severity depends on the acquired spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

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Pharmacokinetic modeling of dynamic contrast‐enhanced MRI using a reference region and input function tail

Ahmed

Levesque

2019

Magnetic Resonance in Med

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Purpose Quantitative analysis of dynamic contrast‐enhanced MRI (DCE‐MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. Methods RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5‐30 s) and noise levels (σ = 0.01‐0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient‐specific AIF tails, and another assuming a literature‐based AIF tail. Results RRIFT estimated the transfer constant Ktrans and interstitial volume ve with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of Ktrans had a median and interquartile range of −9 ± 45% with the ETM and −2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for Ktrans, 0.96 for ve, and 0.73 for the plasma volume vp using a measured AIF tail. With the literature‐based AIF tail, the CCC was 0.89 for Ktrans, 0.93 for ve and 0.78 for vp. Conclusions Quantitative DCE‐MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.

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“…The simulated contrast agent was gadobutrol, of which a longitudinal relaxivity r 1 = 4.1 L/mmol/s and a transverse relaxivity

r_{2}^{*}

= 6.5 L/mmol/s was assessed at 3T . All these MRI settings were identical to the settings of a patient study, which is described in Part II of this article . Subsequently, Gaussian white noise with an signal‐to‐noise ratio (SNR) of 20 was added.…”

Section: Methodsmentioning

confidence: 99%

Estimating the arterial input function from dynamic contrast‐enhanced MRI data with compensation for flow enhancement (I): Theory, method, and phantom experiments

Schie

Lavini

Vliet

et al. 2017

Magnetic Resonance Imaging

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Estimating the arterial input function from dynamic contrast‐enhanced MRI data with compensation for flow enhancement (II): Applications in spine diagnostics and assessment of crohn's disease

Abstract: 3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204.

Cited by 5 publications

References 29 publications

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

Pharmacokinetic modeling of dynamic contrast‐enhanced MRI using a reference region and input function tail

Estimating the arterial input function from dynamic contrast‐enhanced MRI data with compensation for flow enhancement (I): Theory, method, and phantom experiments

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