Synopsis
The intravoxel incoherent motion model is
of great interest as it gives a more complete characterization of DWI signals.
However, estimates of the pseudo-diffusion coefficient D* are noisy,
which can be mitigated using Markov random field (MRF) models. The MRF
smoothing weights are usually subjectively chosen; by removing this requirement,
we show that while the smoothing weights for the pseudo-diffusion volume
fraction and diffusion coefficient can be estimated from the data, smoothing weights for D*
cannot. This suggests that with currently available data, D* estimates
require stabilization by imposing subjective constraints of some kind, such as the MRF used
here.Introduction
The intravoxel
incoherent motion model (IVIM) is of interest as it gives a
more complete characterization of DWI signal attenuation in a
number of normal organs and pathologies [1]. In practice, estimates of the
pseudo-diffusion coefficient $$$\small{}D^*$$$ are noisy, so
a number of advanced techniques have been proposed: using a fixed value
for $$$\small{}D^*$$$ [2], a hierarchical Bayesian model incorporating a histogram
model [3], and Markov random field (MRF) models [4]. MRF methods augment the data cost function with a spatial
smoothness prior that encourages neighboring voxels to have similar values. MRFs use weighting factors that are
typically fixed to subjectively pre-determined values [4,5]. In this abstract, a Bayesian model and
associated Markov
chain Monte-Carlo (MCMC) algorithm are presented that enable data-driven estimation of
the MRF smoothness weights.
Purpose
To explore the
feasibility of the proposed MRF model and MCMC algorithm, and to show that the
convergence properties of the algorithm shed light on which parameters of the
IVIM model can be reliably estimated from data similar to that obtained in a
clinical setting.
Methods
MRF model: The data likelihood assumes Gaussian errors between
the log-data and the log-transformed IVIM
model:$$$\small{}\;\log{}S_i=\log{}S_0+\log\left(\,f\mathrm{e}^{-b_iD^*}+(1-f)\mathrm{e}^{-b_iD}\right)+\eta_i$$$,
where $$$\small{}f$$$ is the pseudo-diffusion volume
fraction,$$$\,\small{}D^*$$$ and$$$\,\small{}D$$$ are fast and slow diffusion coefficients
and $$$\small{}\mathrm{var}(\eta_i)=\sigma_S^2$$$ over the whole ROI/image. The smoothness prior is defined by a
product of nearest-neighbour clique terms, each of which is modeled with a Laplace
distribution$$$\,\small{}p\!\left(x_i,\,x_j\right)=\frac{1}{2}w_x\exp\left(-w_x\left|x_i-x_j\right|\right)$$$,
and the scale term $$$\small{}w_x$$$ is the smoothing weight for $$$\small{}x\in\{f,\,D,\,D^*\}$$$. This distribution is the probabilistic
equivalent to the robust L1-norm [4] which has desirable edge-preserving
properties. The posterior distribution
is the product of the data likelihood and the smoothness priors
for$$$\,\small{}f,\,D\,$$$and$$$\,\small{}D^*$$$.
The key innovation introduced here is to estimate the smoothing weights
in addition to the voxel IVIM parameters using an MCMC algorithm.
Data and evaluation: Abdominal DWI data were acquired coronally in seven patients with liver tumours on a 1.5T MAGNETOM Avanto (Siemens Healthcare, Erlangen, Germany) using
a prototype single-shot EPI sequence and 20$$$\!\small{}\times$$$5mm$$$\;$$$slices, TR/TE=5000/60ms, matrix=1282,
FOV=400mm, 5$$$\;$$$NSA, 3$$$\;$$$orthogonal directions,
phase-partial-Fourier$$$\;$$$7/8, GRAPPA$$$\;$$$2,
b-values=0,20,40,60,120,240,480,900s/mm2.
Individually acquired images were registered [6], and averaged per
b-value. A single slice was
selected covering the liver, and including the kidneys where visible, and a
rectangular portion of the images was cropped to remove the background, see
figure 1. Initial values
were obtained using least-squares, and the MRF/MCMC algorithm used to generate 10,000 posterior samples.
Results
The principal result is that in all seven patients,
estimates of $$$\small{}w_{D^*}$$$ did not converge, but instead diverged to large
values, which leads to unrealistically smooth parameter maps. Therefore, for the remainder of the evaluation,$$$\,\small{}w_{D^*}$$$
was fixed to 5. This was chosen to be at the lower end of the estimates
of $$$\small{}w_D$$$ and$$$\,\small{}w_f$$$ obtained in figure 2, in order to avoid
over-smoothing the $$$\small{}D^*$$$ maps. For the Laplace clique term with weight$$$\,\small{}w$$$, the average percentage change between neighbouring voxels is$$$\,\small{}100\%\times(w-1)^{-1}$$$, which is around 25% for$$$\,\small{}w_{D^*}=5$$$. The example in
figure 1 shows the least-squares fit (used as initialisation), parameter maps
from a single MCMC sample (showing the level of image smoothness determined by
the estimated MRF weights), the mean of the post-burn-in MCMC samples (showing
the final MRF estimates), and the coefficient of variation of the MCMC samples
(computed from the standard deviation of the MCMC samples). With$$$\small{}\,w_{D^*}\,$$$fixed,$$$\small{}\,w_f$$$
failed to converge in one patient, and$$$\small{}\,w_D\,$$$failed to converge in a
different patient. Posterior
distributions for the smoothing weights for all cases which converged are shown
in figure 2, and the mean$$$\small{}\;(\pm$$$sd$$$)\,$$$were$$$\small{}\,w_D=14.9\pm2.6\,$$$and$$$\small{}\,w_f=8.4\pm2.0$$$.
Discussion and Conclusions
For the Bayesian model given above, estimation of the smoothing weights is essentially driven by a balance between spatial heterogeneity and parameter uncertainty. For these data, the convergence of$$$\small{}\,w_D\,$$$and$$$\small{}\,w_f$$$ in almost all patients indicates that the uncertainty (given the image noise and b-value support) in$$$\small{}\,D\,$$$and$$$\small{}\,f$$$ is sufficiently low that the degree of spatial heterogeity can be determined from the data. However, the divergence of$$$\small{}\,w_{D^*}$$$ leads to very smooth $$$\small{}D^*$$$ maps, which is equivalent to a single value of$$$\small{}\,D^*$$$ for the whole image. This is consistent with the known difficulty in estimating$$$\small{}\,D^*$$$, and is driven by poor data support for the pseudo-diffusion coefficient of the IVIM model. Despite this finding, it is clear that a single value for$$$\small{}\,D^*$$$ is not appropriate when modelling complex anatomy, which motivates the use of a fixed value for$$$\small{}\,w_{D^*}$$$.
To account for image
acquisition differences (in particular the voxel dimensions), it would be preferable
to estimate the smoothing weights independently for every patient, but where
this is impractical, weights can be estimated from a subset of patients and the
average used for subsequent patients.
Acknowledgements
CRUK and EPSRC support to the Cancer Imaging Centre at ICR and RMH in association with MRC and Department of Health C1060/A10334, C1060/A16464 and NHS funding to the NIHR Biomedical Research Centre and the Clinical Research Facility in Imaging.References
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