From imaging to simulation: a framework applied to simulate the blood flow in the carotids

In this work we present a methodology to extract information from medical imaging and use it for hemodynamical simulation in arteries. Based on in-vivo magnetic resonance images (MRI), the velocity of the blood flow has been measured at different positions and times. Also, the anatomy of the vessel has been converted into a volume mesh suitable for numerical modeling. This data has been used to solve computationally the dynamics of the fluid inside the artery in healthy and pathologic cases. As an application, we have developed a computational model within the carotids. The next step in the pipeline will be to extend the simulation to fluidstructure interaction (FSI) to find the parameters in an atherosclerotic plaque that could lead to rupture.Peer Reviewe

From imaging to simulation: a framework applied to simulate the blood flow in the carotids

In this work we present a methodology to extract information from medical imaging and use it for hemodynamical simulation in arteries. Based on in-vivo magnetic resonance images (MRI), the velocity of the blood flow has been measured at different positions and times. Also, the anatomy of the vessel has been converted into a volume mesh suitable for numerical modeling. This data has been used to solve computationally the dynamics of the fluid inside the artery in healthy and pathologic cases. As an application, we have developed a computational model within the carotids. The next step in the pipeline will be to extend the simulation to fluidstructure interaction (FSI) to find the parameters in an atherosclerotic plaque that could lead to rupture.Peer Reviewe

DCE-MRI of the liver: reconstruction of the arterial input function using a low dose pre-bolus contrast injection.

To assess the quality of the arterial input function (AIF) reconstructed using a dedicated pre-bolus low-dose contrast material injection imaged with a high temporal resolution and the resulting estimated liver perfusion parameters.In this IRB-approved prospective study, 24 DCE-MRI examinations were performed in 21 patients with liver disease (M/F 17/4, mean age 56 y). The examination consisted of 1.3 mL and 0.05 mmol/kg of gadobenate dimeglumine for pre-bolus and main bolus acquisitions, respectively. The concentration-curve of the abdominal aorta in the pre-bolus acquisition was used to reconstruct the AIF. AIF quality and shape parameters obtained with pre-bolus and main bolus acquisitions and the resulting estimated hepatic perfusion parameters obtained with a dual-input single compartment model were compared between the 2 methods. Test-retest reproducibility of perfusion parameters were assessed in three patients.The quality of the pre-bolus AIF curve was significantly better than that of main bolus AIF. Shape parameters peak concentration, area under the time activity curve of gadolinium contrast at 60 s and upslope of pre-bolus AIF were all significantly higher, while full width at half maximum was significantly lower than shape parameters of main bolus AIF. Improved liver perfusion parameter reproducibility was observed using pre-bolus acquisition [coefficient of variation (CV) of 4.2%-38.7% for pre-bolus vs. 12.1-71.4% for main bolus] with the exception of distribution volume (CV of 23.6% for pre-bolus vs. 15.8% for main bolus). The CVs between pre-bolus and main bolus for the perfusion parameters were lower than 14%.The AIF reconstructed with pre-bolus low dose contrast injection displays better quality and shape parameters and enables improved liver perfusion parameter reproducibility, although the resulting liver perfusion parameters demonstrated no clinically significant differences between pre-bolus and main bolus acquisitions

Hepatocellular carcinoma: IVIM diffusion quantification for prediction of tumor necrosis compared to enhancement ratios

Purpose: To correlate intra voxel incoherent motion (IVIM) diffusion parameters of liver parenchyma and hepatocellular carcinoma (HCC) with degree of liver/tumor enhancement and necrosis; and to assess the diagnostic performance of diffusion parameters vs. enhancement ratios (ER) for prediction of complete tumor necrosis. Patients and methods: In this IRB approved HIPAA compliant study, we included 46 patients with HCC who underwent IVIM diffusion-weighted (DW) MRI in addition to routine sequences at 3.0 T. True diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (PF) and apparent diffusion coefficient (ADC) were quantified in tumors and liver parenchyma. Tumor ER were calculated using contrast-enhanced imaging, and degree of tumor necrosis was assessed using post-contrast image subtraction. IVIM parameters and ER were compared between HCC and background liver and between necrotic and viable tumor components. ROC analysis for prediction of complete tumor necrosis was performed. Results: 79 HCCs were assessed (mean size 2.5 cm). D, PF and ADC were significantly higher in HCC vs. liver (p  0.95, p < 0.002). Conclusion: D has a reasonable diagnostic performance for predicting complete tumor necrosis, however lower than that of contrast-enhanced imaging. Keywords: Hepatocellular carcinoma, Diffusion, Perfusion, Necrosi

Diagram depicting calculated AIF (arterial input function) parameters (A).

<p>An example is shown in a 67-year-old patient with HCV (same patient as in Fig. 1). Pre-bolus AIF (B) and main bolus AIF (C). Pre-bolus AIF demonstrates higher peak concentration, upslope and AUC60, shorter TTP and smaller FWHM (values are given on the figures).</p

Test-retest reproducibility of pre-bolus and main bolus AIF (arterial input function) shape and corresponding hepatic perfusion parameters measured in 3 patients expressed as mean and range of coefficients of variation (in %).

<p>Test-retest reproducibility of pre-bolus and main bolus AIF (arterial input function) shape and corresponding hepatic perfusion parameters measured in 3 patients expressed as mean and range of coefficients of variation (in %).</p

Quantitative AIF (arterial input function) curve parameters obtained for main bolus and pre-bolus acquisitions (mean ± SD).

<p>Quantitative AIF (arterial input function) curve parameters obtained for main bolus and pre-bolus acquisitions (mean ± SD).</p

Quantitative liver perfusion parameters obtained for main bolus and pre-bolus acquisitions (mean ± SD), the coefficients of variation (CV, %) and the Bland-Altman (BA) limits of agreements (%).

<p>Quantitative liver perfusion parameters obtained for main bolus and pre-bolus acquisitions (mean ± SD), the coefficients of variation (CV, %) and the Bland-Altman (BA) limits of agreements (%).</p

Sequence parameters of the Look-Locker for T1 mapping, 2D-TurboFLASH for pre-bolus acquisition and 3D-FLASH sequences for main bolus acquisition for liver DCE-MRI.

<p>Sequence parameters of the Look-Locker for T1 mapping, 2D-TurboFLASH for pre-bolus acquisition and 3D-FLASH sequences for main bolus acquisition for liver DCE-MRI.</p