Signal Processing - May 2016 - 74
regions for the learning process and applying postprocessing
techniques to determine the tissue class for which sufficient
information is available from the MRI. In a more recent contribution, Arabi and Zaidi [50] improved the robustness of
the aforementioned technique [46] to nonsystematic registration bias and anatomical abnormalities by discarding locally
gross misalignment errors from the training and pseudo-CT
generation process through local sorting of the atlas images
using the local normalized cross-correlation criterion as a
metric to assess the similarity to the target image prior to providing it to the training step. Despite promising preliminary
results reported in a number of studies using more advanced
approaches [48], [50], more research is still required to make
the procedure completely automated and suitable for clinical
usage in whole-body PET/MRI.
Emission-based techniques form the last category of algorithms and have gained substantial momentum during the last
decade. They are now recognized as valuable approaches for
estimation of the attenuation map in PET/MRI through the
simultaneous estimation of activity and attenuation within a
maximum-likelihood (MLAA) framework [51]. However,
these techniques suffer from cross-talk, depend on tracer distribution, and are susceptible to counting statistics. The use of
TOF information proved to partially mitigate the cross-talk
issue and stabilize the joint estimation problem [52]. It is worth
emphasizing that TOF PET is less sensitive to attenuation
artifacts than conventional non-TOF PET. Recent advances
in emission-based techniques demonstrated the promise of
an MRI-guided MLAA algorithm for attenuation correction
in whole-body PET/MRI [53]. In this work, the estimation of
attenuation maps takes advantage of a constrained Gaussian
mixture model and Markov random field smoothness prior
imposed by MRI spatial and CT statistical constraints. These
techniques proved to outperform previous approaches reported
in the literature [54]. Overall, each category of techniques has
its own pros and cons, and it is expected that hybrid techniques
combining at least two (and ideally the three categories of
attenuation correction methods) will result in more accurate
and robust techniques.
The many other challenging issues that still have to be
addressed in this regard, including attenuation of MRI hardware (tables, rigid and non-rigid RF coils, pillows, headphones,
medical probes, and other objects that are MRI-invisible but
contribute to photon attenuation), patient positioning aids in
the FOV, and conductive MR-compatible or nonconductive
but MRI-invisible implants, should also be taken into account.
Another challenging issue is transaxial plane truncation owing
to the limited MRI FOV, which results in incomplete attenuation maps, producing artifacts on corresponding attenuationcorrected PET images.
MRI-guided image reconstruction in PET/MRI
One of the important limitations of statistical iterative reconstruction techniques, such as the maximum-likelihood-
expectation-maximization (ML-EM) algorithm, is that a
large number of iterations deteriorate image quality and
74
amplify noise in PET images [55]. An elegant way to control the noise characteristics consists of incorporating a prior
distribution to depict the statistical properties of the image
to be determined and thus generate a posteriori probability
distributions from the image conditioned upon the data [10].
The well-established Bayesian reconstruction framework
forms a prevailing expansion of the popular ML-EM algorithm. The maximum a posterior (MAP) estimate is derived
from maximization of the a posteriori probability over the set
of probable images [56]. There are many benefits associated
with this approach in the sense that the diverse mechanisms
of the prior, including the pseudo-Poisson nature of statistics, nonnegativity of the solution, local voxel correlations, or
identified presence of anatomical boundaries (from correlated
structural imaging), may be incorporated into the estimation
process, evaluated independently, and employed during the
algorithm's implementation [10]. Prior anatomical information obtained from correlated anatomical imaging can also be
included in PET reconstruction within a Bayesian framework
to avoid resolution loss resulting from regularization, albeit to
recover resolution by taking advantage of the better resolution
of anatomical images [57]. This has been achieved with various degrees of success over the years using MRI [58].
A coupling term is usually incorporated in this category
of reconstruction techniques, which favors the preservation of
edges in PET images related to the location of relevant anatomical boundaries extracted from corresponding anatomical
images. A Gibbs prior distribution is typically used to encourage the piecewise smoothness of PET images, which can be
included in the Bayesian model. Promising preliminary results
were reported by various investigators using segmentation-free
anatomical priors based on similarity measures analogous to
mutual information, but further research and development
efforts are still required. Therefore, the advent of simultaneous
hybrid PET/MRI systems creating perfectly aligned molecular
and anatomical images is stimulating the further development
and assessment in the clinical setting of Bayesian MAP reconstruction algorithms.
As an example, a MAP algorithm for PET image reconstruction incorporating MRI information with joint entropy
between PET and MRI features serving as the regularization
constraint was proposed [59]. A nonparametric method was
then used to estimate the joint probability density of PET
and MR images. It was demonstrated that incorporation of
the anatomical information using this approach, following
parameter optimization, produces significant improvement
in the noise versus bias tradeoff in ROI-based quantitative
analysis compared with conventional MAP reconstruction.
MRI-guided partial-volume correction in PET/MRI
The accuracy of PET for measuring regional radiotracer concentrations is limited by the finite spatial resolution capability
of current-generation clinical PET scanners and the resulting partial-volume effect (PVE). Accurate PET quantification requires that the source size be greater than two to three
times the scanner's spatial resolution, expressed in terms of
IEEE Signal Processing Magazine
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May 2016
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Table of Contents for the Digital Edition of Signal Processing - May 2016
Signal Processing - May 2016 - Cover1
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