Interventional cardiac magnetic resonance (MR) procedures are the subject of an increasing number
of research studies. Typically, during the procedure only two-dimensional images of oblique slices
can be presented to the interventionalist in real time. There is a clear benefit to being able to register
the real-time 2D slices to a previously acquired 3D computed tomography (CT) or MR image of the heart.
Results from a study of the accuracy of registration of 2D cardiac images of an anesthetized
pig to a 3D volume obtained in diastole are presented.
Fast cine MR images representing twenty phases of the cardiac
cycle were obtained of a 2D slice in a known oblique orientation.
The 2D images were initially mis-oriented at distances ranging from 2 to 20 mm,
and rotations of +/-10 degrees about all three axes. Images from all 20
cardiac phases were registered to examine the effect of timing between the 2D image
and the 3D pre-procedural image.
Linear registration using mutual information computed with 64 histogram bins yielded
the highest accuracy. For the diastolic phases, mean translation and rotation errors ranged between
0.91 and 1.32 mm and between 1.73 and 2.10 degrees. Scans acquired at other phases also
had high accuracy. These results are promising for the use of real time MR
in image-guided cardiac interventions, and demonstrate the feasibility of registering 2D oblique MR slices to
previously acquired single-phase volumes without preprocessing.
This paper presents parameter estimation of general ultrasound backscatter models, such as the generalized Nakagami and generalized <i>K</i> distributions, via entropy maximization. Parameters of these distributions are related to scatterer density and regularity,
and therefore accurate parameter estimation techniques are needed. Parameter estimation based on entropy maximization shows promising
results in terms of accuracy for simulated <i>K</i> data and
high goodness-of-fit values for the two general backscatter models, especially for the generalized Nakagami distribution.
Information theoretic similarity metrics, including mutual information, have been widely and successfully employed in multimodal biomedical image registration. These metrics are generally based on the Shannon-Boltzmann-Gibbs definition of entropy. However, other entropy definitions exist, including generalized entropies, which are parameterized by a real number. New similarity metrics can be derived by exploiting the additivity and pseudoadditivity properties of these entropies. In many cases, use of these measures results in an increased percentage of correct registrations. Results suggest that generalized information theoretic similarity metrics, used in conjunction with other measures, including Shannon entropy metrics, can improve registration performance.
In medical ultrasonography, speckle model parameters are dependent on scatterer density and regularity, and can be exploited for use in tissue characterization. The purpose of the current study is to quantify the goodness-of-fit of two models (the Nakagami and K distributions), applied to envelope data representing a range of clinically relevant scattering conditions. Ground truth data for computing goodness-of-fit were generated with envelope simulators. In the first simulation, 100 datasets of various sample sizes were generated with 40 scatterer densities, ranging from 0.025 to 20. Kolmogorov-Smirnov significance values quantified the goodness-of-fit of the two models. In the second simulation, densities ranged from 2 to 60, and additional scattering parameters were allowed to vary. Goodness-of-fit was assessed with four statistical tests. Although the K distribution has a firm physical foundation as a scattering model, inaccuracy and high standard deviation of parameter estimates reduced its effectiveness, especially for smaller sample sizes. In most cases, the Nakagami model, whose parameters are relatively easy to compute, fit the data best, even for large scatterer densities.