Erick O Buko1, Afis Ajala1, Jiming Zhang2, Pei Herng Hor1, and Raja Muthupillai2
1Physics, Texas Center for Superconductivity at University of Houston, Houston, TX, United States, 2Diagnostic and Interventional Radiology, Baylor St. Luke's Medical Center, Houston, TX, United States
Synopsis
Determination of perfusion fraction (f) from IVIM-MRI
data is challenging. Recent studies have shown that estimation of f is influenced
by echo time (TE). Eliminating thisTE dependency on f requires
acquiring data sets at multiple TEs. Here, we
propose an analytical segmented approach to correct for the TE dependency on f arising from T2 differences between the tissue and fluid compartments
(AS-T2). Numerical
simulations predict, and phantom experiments confirm that, -compared to commonly
used segmented approaches to estimate f, AS-T2 approach permits the determination of perfusion
fraction without TE dependence,
and with fewer measurements.
Synopsis
Determination
of perfusion fraction (f) from
IVIM-MRI data is challenging. Recent studies have shown that estimation of f is influenced by echo time (TE). Eliminating thisTE dependency on f requires acquiring data sets at
multiple TEs. Here, we propose an
analytical segmented approach to correct for the TE dependency on f arising from T2
differences between the tissue and fluid compartments (AS-T2). Numerical simulations predict, and phantom
experiments confirm that, compared to commonly used segmented approaches to
estimate f, AS-T2 approach
permits the determination of perfusion fraction without TE dependence, and with fewer measurements.Introduction
For
decades, the two compartment model of tissue diffusion proposed by LeBihan et
al1. has held the promise of providing valuable insights into various
pathological processes 2-5.
However, determination of IVIM model parameters related to perfusion –
perfusion fraction (f), and
pseudo-diffusion coefficient (Df)
has remained a vexing problem 6.
Recent studies, have shown that T2 relaxation rate differences
between tissue and the fluid compartments can cause significant overestimation
of fluid volume fraction (f) as a
function of echo time (TE) when using
conventional fitting approaches 7,8.
We
previously proposed an analytical segmented (AS) approach to evaluate f and Df 9.
In this work, we extend the AS framework to include T2 correction (AS-T2). The purposes of this works are to: a) describe AS-T2 approach for extracting IVIM model perfusion parameters;
(b) verify the feasibility of AS-T2 approach via numerical
simulations, and phantom experiments, and (c) compare the performance of AS-T2
approach to widely used segmented (S)
and over segmented (OS) approaches for extracting IVIM model parameters. METHODS
Theory: IVIM based experiments are designed to attenuate MR signal due to diffusivity of tissue and fluid compartments (Dt, and Df), in addition to signal attenuation due to T2 relaxation. Conventional segmented (S) and
over segmented (S) approaches as well as analytical
segmented (AS) approaches extract IVIM model parameters by implicitly assuming that the T2
relaxation rates of the tissue and fluid compartments are similar, and
this affects the estimation of f. The algorithmic steps of each method are
pictorially represented in Figure 1.
By sampling MR signal over the b,
TE space [S(b, TE)], it is possible to estimate f that is independent of TE (Eq. 5, Figure 1). Briefly, in AS-T2 method:
(1) Dt is estimated by fitting the DWI
images acquired with b-value high
enough to overwhelm signal from the fluid compartment (e.g., b > 200 s/mm2) using
equation 1.
(2) T2t is estimated by fitting the DWI
images acquired at a high b value e.g. b = 200 s/mm2 as a function of TE into a monoexponential function.
(3) By
rearranging Eq. 5, we obtain an expression for f (b, TE) [Equation 7, Figure 1]. By fitting f (b, TE), perfusion related parameters (f and Df) can
be extracted.
Numerical Simulations: IVIM MR signal was simulated with the following tissue parameters: f = 0.25; Df/Dt= 0.09/ 0.002 mm2/s;
T2f/T2t =
97.3/63.5 ms. IVIM-MRI acquisition
parameters were: 10 b values
(0,10,25,50,75,100,200,300,500,800 s/mm2);
8 TE ranging from 0 to 150 ms. 5000 signal curves with
SNR levels of 20 and 50 were created by adding Gaussian noise.
IVIM acquisition: A non-flowing Gd-doped water (T2
of 63.5 ms) served as static tissue model.
A flow pump was used to create flow through a fluid (T2 of
97.3 ms) filled cylinder (diameter: 48 mm) with a fluid velocity of 0.7368 mm/s. Under steady state conditions, DWI
were acquired with the same 10 b
values used in numerical simulations and at 6 TEs (60,70,80,100,120,150 ms). IVIM MRI signal was constructed by combining
the measured MR signal from voxels in the static and fluid compartments in
proportion to the presumed perfusion fraction (f).
Data
Analysis: Perfusion
fraction (f) was estimated using each
of the four algorithms (Figure 1) from the MR signal s(b, TE) space spanning b, and TE.RESULTS
The
mean and standard deviation (STD) of estimated f as a function of TE
is shown in Figure 2. Conventional fitting approaches and the AS method, consistently overestimate f, by as much as 40% as TE increases from 60 ms to 150 ms.
In contrast, AS-T2 method yields an estimate of f value that is independent of TE that matches theory. Experimental
results confirm theoretical predictions and numerical simulations (Figure
3). DISCUSSION AND CONCLUSION
In conclusion,
theoretical simulations and phantom experiments suggest that, the proposed
AS-T2 approach outperforms conventional segmented and oversegmented analysis
methods widely used in clinical settings in the estimation of perfusion
fraction. These findings need to be
validated in a clinical model.Acknowledgements
No acknowledgement found.References
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