A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution

Koh, Tong San; Cheong, Dennis Lai-Hong; Tan, Cheen-Hau; Lim, C. C. Tchoyoson

doi:10.1016/j.neuroimage.2005.09.032

Cited by 24 publications

(14 citation statements)

References 33 publications

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“…In Krogh's model, a cylinder is used to approximate a bundle of muscle fibers surrounding a central blood vessel that supplies oxygen; in kidney tissue, our model is comprised of convoluted blood vessels with heterogeneously distributed concentrations of intravascular oxygen along the axial direction and supplies oxygen only to nearby surrounding tissue. This distributed-parameter model, partly inspired by the Krogh model, has been widely validated in tumor imaging literature (19,22,38) and is used to analyze the transit of intravenously injected tracer through tumor tissue. Because of the convoluted arrangement of the blood vessels and the steady-state conditions during the BOLD MRI scan, it is appropriate to assume well mixing of oxygen, i.e., a single tissue PO 2 , within the tissue.…”

Section: Discussionmentioning

confidence: 99%

Measurement of renal tissue oxygenation with blood oxygen level-dependent MRI and oxygen transit modeling

Zhang

Morrell

Rusinek

et al. 2014

American Journal of Physiology-Renal Physiology

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Blood oxygen level-dependent (BOLD) MRI data of kidney, while indicative of tissue oxygenation level (Po2), is in fact influenced by multiple confounding factors, such as R2, perfusion, oxygen permeability, and hematocrit. We aim to explore the feasibility of extracting tissue Po2 from renal BOLD data. A method of two steps was proposed: first, a Monte Carlo simulation to estimate blood oxygen saturation (SHb) from BOLD signals, and second, an oxygen transit model to convert SHb to tissue Po2. The proposed method was calibrated and validated with 20 pigs (12 before and after furosemide injection) in which BOLD-derived tissue Po2 was compared with microprobe-measured values. The method was then applied to nine healthy human subjects (age: 25.7 ± 3.0 yr) in whom BOLD was performed before and after furosemide. For the 12 pigs before furosemide injection, the proposed model estimated renal tissue Po2 with errors of 2.3 ± 5.2 mmHg (5.8 ± 13.4%) in cortex and -0.1 ± 4.5 mmHg (1.7 ± 18.1%) in medulla, compared with microprobe measurements. After injection of furosemide, the estimation errors were 6.9 ± 3.9 mmHg (14.2 ± 8.4%) for cortex and 2.6 ± 4.0 mmHg (7.7 ± 11.5%) for medulla. In the human subjects, BOLD-derived medullary Po2 increased from 16.0 ± 4.9 mmHg (SHb: 31 ± 11%) at baseline to 26.2 ± 3.1 mmHg (SHb: 53 ± 6%) at 5 min after furosemide injection, while cortical Po2 did not change significantly at ∼58 mmHg (SHb: 92 ± 1%). Our proposed method, validated with a porcine model, appears promising for estimating tissue Po2 from renal BOLD MRI data in human subjects.

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Section: Discussionmentioning

confidence: 99%

Measurement of renal tissue oxygenation with blood oxygen level-dependent MRI and oxygen transit modeling

Zhang

Morrell

Rusinek

et al. 2014

American Journal of Physiology-Renal Physiology

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“…Such distributed capillary models have been devised for the AATH (81) and DP2 (82) models. Extension of the distributed capillary formalism to multiple-compartmental versions of DP models is also possible (82).…”

Section: Distributed Parameter (Dp) Models and Variationsmentioning

confidence: 99%

Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI

Koh

Bisdas

Koh

et al. 2011

Magnetic Resonance Imaging

115

130

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Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T 1 -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrastenhanced MRI data.

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“…rCBF evaluation by ET (or by deconvolution) is not expected to be affected by leakage in the following applications: In computed tomography (CT) perfusion analysis with a first pass bolus (Aviv et al, 2009; Bisdas et al, 2007; Cenic et al, 2000; Koh et al, 2006; Nabavi et al, 1999), in T1 based MRI Dynamic Contrast-enhanced (DSC-MRI) first pass study (Larsson et al, 2009), in PET perfusion (Larson et al, 1987) of the distributed parameter models where radiolabeled water is not 100% freely diffusive and retains a vascular component. The reason is that given the assumption of R ( t ) = 1 before the tissue mean transit time, the external image detector, as explained by Larson et al (Larson et al, 1987) cannot distinguish between tracer extracted into tissue and tracer in blood, but instead registers a response proportional to the total amount of tracer in the injected bolus.…”

Section: Discussionmentioning

confidence: 99%

Early time points perfusion imaging: Theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function

Kwong¹,

Chesler²

2011

NeuroImage

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If local arterial input function (AIF) could be identified, we present a theoretical approach to generate a correction factor based on local AIF for the estimation of relative cerebral blood flow (rCBF) under the framework of early time points perfusion imaging (ET). If C(t), the contrast agent bolus concentration signal time course, is used for rCBF estimation in ET, the correction factor for C(t) is the integral of its local AIF. The recipe to apply the correction factor is to divide C(t) by the integral of its local AIF to obtain the correct rCBF. By similar analysis, the correction factor for the maximum derivative (MD1) of C(t) is the maximum signal of AIF and the correction factor for the maximum second derivative (MD2) of C(t) is the maximum derivative of AIF. In the specific case of using normalized gamma-variate function as a model for AIF, the correction factor for C(t) (but not for MD1) at the time to reach the maximum derivative is relatively insensitive to the shape of the local AIF.

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A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution

Cited by 24 publications

References 33 publications

Measurement of renal tissue oxygenation with blood oxygen level-dependent MRI and oxygen transit modeling

Measurement of renal tissue oxygenation with blood oxygen level-dependent MRI and oxygen transit modeling

Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI

Early time points perfusion imaging: Theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function

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