Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object.

PURPOSE: Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T METHODS: Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols. RESULTS: Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. CONCLUSIONS: Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residua

Systematic Assessment Of Multi-Echo Dynamic Susceptibility Contrast Mri Using A Digital Reference Object

Purpose: Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and (Formula presented.) contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol. Methods: Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols. Results: Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. Conclusions: Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual (Formula presented.) leakage effects remains compulsory for high rCBV accuracy

A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials

The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and changes observed across multiple glioblastomas, the DRO\u27s input parameters were trained using DSC-MRI data from 23 glioblastomas (\u3e40 000 voxels). The DRO\u27s ability to produce reliable signals for combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC-MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials

An efficient computational approach to characterize DSC-MRI signals arising from three-dimensional heterogeneous tissue structures.

The systematic investigation of susceptibility-induced contrast in MRI is important to better interpret the influence of microvascular and microcellular morphology on DSC-MRI derived perfusion data. Recently, a novel computational approach called the Finite Perturber Method (FPM), which enables the study of susceptibility-induced contrast in MRI arising from arbitrary microvascular morphologies in 3D has been developed. However, the FPM has lower efficiency in simulating water diffusion especially for complex tissues. In this work, an improved computational approach that combines the FPM with a matrix-based finite difference method (FDM), which we call the Finite Perturber the Finite Difference Method (FPFDM), has been developed in order to efficiently investigate the influence of vascular and extravascular morphological features on susceptibility-induced transverse relaxation. The current work provides a framework for better interpreting how DSC-MRI data depend on various phenomena, including contrast agent leakage in cancerous tissues and water diffusion rates. In addition, we illustrate using simulated and micro-CT extracted tissue structures the improved FPFDM along with its potential applications and limitations

Computed k_p values for vascular structure extracted from micro-CT.

<p>(a) SE and (b) GE k_p values as a function of vascular volume fraction computed using the FPFDM for the kidney microvascular models (with vascular volume fractions >0.1%) shown in Fig. 7. SE k_p values ranged from 3.6–27.8 (mM–sec) ⁻¹, and GE k_p values ranged from 53.8–174.3 (mM–sec) ⁻¹. Above 5% volume fraction, the GE k_p values were relatively constant with a mean value of 103.3(mM–sec) ⁻¹.</p

Validation of the FPFDM.

<p>(a) FPFDM replicates the characteristic vessel size dependence of ΔR₂^*and ΔR₂ as has been previously shown with MC methods. (b) A comparison of computed ΔR₂^* values as a function of sphere volume fraction and packing arrangement using MC (filled symbols) and FPFDM (open symbols) techniques, with excellent agreement between the two methods. (c) The computed ΔR₂^* percentage difference between MC and FPFDM decreases as the number of FPFDM structures used for averaging increases.</p

Kidney vascular structure extracted from micro-CT.

<p>Kidney vasculature extracted from micro-CT along with representative MR voxel-sized (1 mm³) microvascular models taken from different sections of the kidney vasculature with their respective vascular volume fractions. The existence of the bubble-like structures demonstrates the filling of glomeruli with Microfil but a higher resolution would be required to differentiate the individual capillaries.</p

The influence of vascular morphology on ΔR₂^* and ΔR₂.

<p>(a–c) Sample microvascular networks simulated using a fractal tree model with increasing branching angle heterogeneity. (d) Three orthogonal slices through the magnetic field perturbation at the body center for the vascular network in (c). (e–f) Effect of branching angle heterogeneity on the concentration dependence of ΔR₂^* and ΔR₂ computed with FPFDM (B₀ = 4.7T, Δχ = 1×10⁻⁷, 2% target vascular volume fraction). Both ΔR₂ and ΔR₂^* increase with branching angle heterogeneity.</p

Dependence of DSC-MRI signals on cellular features in the presence of CA leakage.

<p>The GE post-contrast to pre-contrast DSC-MRI signal ratio (<i>S/S₀</i>), both in the presence (<i>K</i>^Trans = 0.2 min⁻¹) and absence (<i>K</i>^Trans = 0 min⁻¹) of CA leakage at pre-contrast <i>T</i>₁ values of <i>T</i>₁₀ = 500 ms, <i>T</i>₁₀ = 1000 ms and <i>T</i>₁₀ = 1500 ms, for tissue structures constructed using ellipsoids with mean radii of 5 µm (a–c) and 15 µm (d–f), respectively. The (<i>S/S₀</i>) values were computed using input parameters of B₀ = 3T, D = 1.3×10⁻⁵ cm²/s, Δt = 0.2 ms, TE = 50 ms TR = 1500 ms, α = 90°, T₂₀^* = 50 ms, r₁ = 3.9 mM⁻¹s⁻¹, r₂ = 5.3 mM⁻¹s⁻¹ and <i>P_m</i> = 0.</p

Dependence of ΔR₂^* and ΔR₂ on cellular shape and packing arrangement.

<p>(a) Example of a cellular model using ellipsoid packing (left) and a 2D slice through the associated magnetic field perturbation for B₀ = 1.5T and Δχ = 5×10⁻⁸ (right). (b,c) The computed ΔR₂^* and ΔR₂ dependence on cell volume fraction and packing arrangement. For all packing arrangements, the relaxivity increases and then decreases with cell volume fraction. Ellipsoid packing yields greater relaxivity than spheres. ΔR₂ exhibits qualitatively similar behavior to ΔR₂^* yet with a reduced magnitude.</p