A Simplified Model for Intravoxel Incoherent Motion Perfusion Imaging of the Brain

Conklin, John; Heyn, Chinthaka; Roux, Marion; Cerny, Milena; Wintermark, Max; Federau, Christian

doi:10.3174/ajnr.a4929

Cited by 41 publications

(42 citation statements)

References 39 publications

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“…We used a 2‐step fitting procedure to estimate [ f 1 , f 2 , D *, D ] in the 3‐compartment model in Equation . First, the fraction of tissue water and D were approximated using diffusion signals acquired with b ≥ 300 s/mm 2 , using a log‐linear fitting approach according to Conklin et al:

log (\frac{S}{S_{0}}) = - b D + log (1 - f_{tissue})

. Then, f 1 and D * were fitted in a mono‐exponential model using a constrained nonlinear linear‐square fitting method in MATLAB.…”

Section: Methodsmentioning

confidence: 99%

Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain

Zhang

2019

Magnetic Resonance in Med

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Purpose To investigate the diffusion time (TD) dependence of intravoxel incoherent motion (IVIM) signals in the brain. Methods A 3‐compartment IVIM model was proposed to characterize 2 types of microcirculatory flows in addition to tissue water in the brain: flows that cross multiple vascular segments (pseudo‐diffusive) and flows that stay in 1 segment (ballistic) within TD. The model was first evaluated using simulated flow signals. Experimentally, flow‐compensated (FC) pulsed‐gradient spin‐echo (PGSE) and oscillating‐gradient spin‐echo (OGSE) sequences were tested using a flow phantom and then used to examine IVIM signals in the mouse brain with TD ranging from ~2.5 ms to 40 ms on an 11.7T scanner. Results By fitting the model to simulated flow signals, we demonstrated the TD dependency of the estimated fraction of pseudo‐diffusive flow and the pseudo‐diffusion coefficient (D*), which were dictated by the characteristic timescale of microcirculatory flow (τ). Flow phantom experiments validated that the OGSE and FC‐PGSE sequences were not susceptible to the change in flow velocity. In vivo mouse brain data showed that both the estimated fraction of pseudo‐diffusive flow and D* increased significantly as TD increased. Conclusion We demonstrated that IVIM signals measured in the brain are TD ‐dependent, potentially because more microcirculatory flows approach the pseudo‐diffusive limit as TD increases with respect to τ. Measuring the TD dependency of IVIM signals may provide additional information on microvascular flows in the brain.

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log (\frac{S}{S_{0}}) = - b D + log (1 - f_{tissue})

. Then, f 1 and D * were fitted in a mono‐exponential model using a constrained nonlinear linear‐square fitting method in MATLAB.…”

Section: Methodsmentioning

confidence: 99%

Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain

Zhang

2019

Magnetic Resonance in Med

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“…Little is known regarding the optimal number of b ‐values, and their optimal range of magnitude, which might also depend on the organ under consideration. Interestingly, several reports suggest that using only a few b ‐values, even as little as two non‐zero b ‐value images, might be sufficient to extract clinically significant perfusion information in many organs . Therefore only a minimal amount of additional scan time to a diffusion weighted scan seems to be necessary to obtain additional perfusion information.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

“…Interestingly, several reports suggest that using only a few b-values, even as little as two non-zero b-value images, might be sufficient to extract clinically significant perfusion information in many organs. [18][19][20][21][22][23][24][25][26][27] Therefore only a minimal amount of additional scan time to a diffusion weighted scan seems to be necessary to obtain additional perfusion information. On the other hand, the use of multiple b-values permits a better separation of signal arising from the vascular and non-vascular compartments, by better assessing the b-value above which the perfusion effects vanish.…”

Section: Littlementioning

confidence: 99%

Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence

Federau

2017

NMR in Biomedicine

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The idea that in vivo intravoxel incoherent motion magnetic resonance signal is influenced by blood motion in the microvasculature is exciting, because it suggests that local and quantitative perfusion information can be obtained in a simple and elegant way from a few diffusion-weighted images, without contrast injection. When the method was proposed in the late 1980s some doubts appeared as to its feasibility, and, probably because the signal to noise and image quality at the time was not sufficient, no obvious experimental evidence could be produced to alleviate them. Helped by the tremendous improvements seen in the last three decades in MR hardware, pulse design, and post-processing capabilities, an increasing number of encouraging reports on the value of intravoxel incoherent motion perfusion imaging have emerged. The aim of this article is to review the current published evidence on the feasibility of in vivo perfusion imaging with intravoxel incoherent motion MRI.

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“…For DCE-MRI analysis, an extended Tofts model was used, which included the determination of the contrast-enhancement curve of the contrast agent in each individual voxel (volume 1 mm × 1 mm × 1.5 mm). DW-MRI data were analyzed using a simplified IVIM model-based analysis as described in [7].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

Lai

Geier

Fleischer

et al. 2018

Preprint

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Mathematical modeling and simulation have emerged as a potentially powerful, time and cost effective approach to personalized cancer treatment. The usefulness of mechanistic models to disentangle complex multi-scale cancer processes such as treatment response has been widely acknowledged. However, a major barrier for multi-scale models to predict the outcomes of therapeutic regimens in a particular patient lies in their initialization and parameterization which need to reflect individual cancer characteristics accurately. In this study we use multi-type routinely acquired measurements on a single breast tumor, including histopathology, magnetic resonance imaging, and molecular profiling to personalize parts of a complex multi-scale model of breast cancer treated with chemotherapeutic and anti-angiogenic agents. We model the dynamics of drugs in tissue (pharmacokinetics) and the corresponding effects on their targets (pharmacodynamics). We developed a open-source computer program that simulates cross-sections of tumors under 12-week 1 therapy regimes and use it to individually reproduce and elucidate treatment outcomes of four patients. For two of the tumors that did not respond to therapy, we used model simulations to suggest alternative regimes, depending on their individual characteristics, with improved outcomes. We found that more frequent doses of chemothereapy reduce tumor burden in a low proliferative tumor while lower doses of anti-angiogenic agents improve drug penetration in a poorly perfused tumor. In addition to bridge multi-type clinical data to shed light on individual treatment outcomes, our approach identified a few tumor-related aspects that need to be clinically portraited better to allow for future model-driven personalized cancer therapy.

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A Simplified Model for Intravoxel Incoherent Motion Perfusion Imaging of the Brain

Cited by 41 publications

References 39 publications

Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain

Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain

Intravoxel incoherent motion MRI as a means to measure in vivo perfusion: A review of the evidence

Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

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