A biexponential DWI study in rat brain intracellular oedema

Steier, Roy; Aradi, Mihály; Pál, József; Perlaki, Gábor; Orsi, G.; Bogner, Péter; Galyas, Ferenc; Bukovics, Péter; Jánszky, J.; Dóczi, Tamás; Schwarcz, Attila

doi:10.1016/j.ejrad.2011.03.058

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…where M is the signal in the presence of diffusion sensitization, M 0 is that in the absence of diffusion sensitization, fast ADC and slow ADC are ADC values, and P fast and P slow are the percentages of contributions to the signal from the fast- and slow-diffusing water compartments ( P fast = 1 − P slow ). [ 16 ]…”

Section: Ethodsmentioning

confidence: 99%

Quantitative Measurement of Cerebral Perfusion with Intravoxel Incoherent Motion in Acute Ischemia Stroke

Hong

Zhu

2015

Chinese Medical Journal

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Background:Intravoxel incoherent motion (IVIM) has the potential to provide both diffusion and perfusion information without an exogenous contrast agent, its application for the brain is promising, however, feasibility studies on this are relatively scarce. The aim of this study is to assess the feasibility of IVIM perfusion in patients with acute ischemic stroke (AIS).Methods:Patients with suspected AIS were examined by magnetic resonance imaging within 24 h of symptom onset. Fifteen patients (mean age was 68.7 ± 8.0 years) who underwent arterial spin labeling (ASL) and diffusion-weighted imaging (DWI) were identified as having AIS with ischemic penumbra were enrolled, where ischemic penumbra referred to the mismatch areas of ASL and DWI. Eleven different b-values were applied in the biexponential model. Regions of interest were selected in ischemic penumbras and contralateral normal brain regions. Fast apparent diffusion coefficients (ADCs) and ASL cerebral blood flow (CBF) were measured. The paired t-test was applied to compare ASL CBF, fast ADC, and slow ADC measurements between ischemic penumbras and contralateral normal brain regions. Linear regression and Pearson's correlation were used to evaluate the correlations among quantitative results.Results:The fast ADCs and ASL CBFs of ischemic penumbras were significantly lower than those of the contralateral normal brain regions (1.93 ± 0.78 μm2/ms vs. 3.97 ± 2.49 μm2/ms, P = 0.007; 13.5 ± 4.5 ml·100 g-1·min-1 vs. 29.1 ± 12.7 ml·100 g-1·min-1, P < 0.001, respectively). No significant difference was observed in slow ADCs between ischemic penumbras and contralateral normal brain regions (0.203 ± 0.090 μm2/ms vs. 0.198 ± 0.100 αμm2/ms, P = 0.451). Compared with contralateral normal brain regions, both CBFs and fast ADCs decreased in ischemic penumbras while slow ADCs remained the same. A significant correlation was detected between fast ADCs and ASL CBFs (r = 0.416, P < 0.05). No statistically significant correlation was observed between ASL CBFs and slow ADCs, or between fast ADCs and slow ADCs (r = 0.111, P = 0.558; r = 0.200, P = 0.289, respectively).Conclusions:The decrease in cerebral blood perfusion primarily results in the decrease in fast ADC in ischemic penumbras; therefore, fast ADC can reflect the perfusion situation in cerebral tissues.

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Section: Ethodsmentioning

confidence: 99%

Quantitative Measurement of Cerebral Perfusion with Intravoxel Incoherent Motion in Acute Ischemia Stroke

Hong

Zhu

2015

Chinese Medical Journal

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“…This phenomenon can be explained by a two‐component model. The diffusion of extracellular water molecules is fast with a high ADC, while that of intra‐cellular water molecules is slow, with a low ADC . Another explanation for the fast diffusion component is pseudodiffusion due to tissue perfusion .…”

Section: Discussionmentioning

confidence: 99%

“…Another explanation for the fast diffusion component is pseudodiffusion due to tissue perfusion . Peritumoral edema is vasogenic edema, and the increased water molecules are in extracellular space . Although the signal intensity of a fast diffusion component decreases much faster than that of a slow diffusion component, the signal intensity may still remain high at a normal b‐value DWI in the peritumoral edema region due to the T 2 shine‐through effect.…”

Section: Discussionmentioning

confidence: 99%

Glioma infiltration sign on high b‐value diffusion‐weighted imaging in gliomas and its prognostic value

Zeng

Ling

Shi

et al. 2018

Magnetic Resonance Imaging

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“…Another explanation for this model is that two components represent intra- and extra-cellular compartments, respectively (Niendorf et al, 1996 ; Steier et al, 2012 ). However, researchers have found that the attenuation of DWI signal does not obey the mono-exponential model even without extra-cellular compartment or even in a single cell (Grant et al, 2001 ; Schwarcz et al, 2004 ; Steier et al, 2012 ). These findings of previous studies also indicate that the bi-exponential model may not be a satisfying model for explaining the attenuation of DWI signal.…”

Section: Discussionmentioning

confidence: 99%

“…However, the bi-exponential model is being challenged for the heterogeneity of its results across studies and its lack of reproducibility (Müller et al, 1998 ; Grant et al, 2001 ; Schwarcz et al, 2004 ; Koh et al, 2011 ; Steier et al, 2012 ; Hu et al, 2014 ; Lin et al, 2015 ). This model is considered oversimplified (Bisdas et al, 2013 ), and some researchers even speculate that there are continuous distributions of diffusion coefficients inside each voxel (Bennett et al, 2003 , 2006 ).…”

Section: Introductionmentioning

confidence: 99%

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

et al. 2018

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Purpose: To present a new modified tri-exponential model for diffusion-weighted imaging (DWI) to detect the strictly diffusion-limited compartment, and to compare it with the conventional bi- and tri-exponential models.Methods: Multi-b-value diffusion-weighted imaging (DWI) with 17 b-values up to 8,000 s/mm2 were performed on six volunteers. The corrected Akaike information criterions (AICc) and squared predicted errors (SPE) were calculated to compare these three models.Results: The mean f0 values were ranging 11.9–18.7% in white matter ROIs and 1.2–2.7% in gray matter ROIs. In all white matter ROIs: the AICcs of the modified tri-exponential model were the lowest (p < 0.05 for five ROIs), indicating the new model has the best fit among these models; the SPEs of the bi-exponential model were the highest (p < 0.05), suggesting the bi-exponential model is unable to predict the signal intensity at ultra-high b-value. The mean ADCvery−slow values were extremely low in white matter (1–7 × 10−6 mm2/s), but not in gray matter (251–445 × 10−6 mm2/s), indicating that the conventional tri-exponential model fails to represent a special compartment.Conclusions: The strictly diffusion-limited compartment may be an important component in white matter. The new model fits better than the other two models, and may provide additional information.

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A biexponential DWI study in rat brain intracellular oedema

Cited by 6 publications

References 26 publications

Quantitative Measurement of Cerebral Perfusion with Intravoxel Incoherent Motion in Acute Ischemia Stroke

Quantitative Measurement of Cerebral Perfusion with Intravoxel Incoherent Motion in Acute Ischemia Stroke

Glioma infiltration sign on high b‐value diffusion‐weighted imaging in gliomas and its prognostic value

A Modified Tri-Exponential Model for Multi-b-value Diffusion-Weighted Imaging: A Method to Detect the Strictly Diffusion-Limited Compartment in Brain

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