KEYWORDS: Image registration, Medical imaging, Magnetic resonance imaging, Algorithm development, Optimization (mathematics), Signal to noise ratio, Software development, 3D modeling, Image analysis, Visualization
Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.
Optimization is an important component in linear and nonlinear medical image registration. While common non-derivative approaches such as Powell's method are accurate and efficient, they cannot easily be adapted for parallel hardware. In this paper, new optimization strategies are proposed for parallel, shared-memory (SM) architectures. The Dividing Rectangles (DIRECT) global method is combined with the local Generalized Pattern Search (GPS) and
Multidirectional Search (MDS) and to improve efficiency on multiprocessor systems. These methods require no derivatives, and can be used with all similarity metrics. In a multiresolution framework, DIRECT is performed with relaxed convergence criteria, followed by local refinement with MDS or GPS. In 3D-3D MRI rigid registration of simulated MS lesion volumes to normal brains with
varying noise levels, DIRECT/MDS had the highest success rate, followed by DIRECT/GPS. DIRECT/GPS was the most efficient (5-10 seconds with 8 CPUs, and 10-20 seconds with 4 CPUs). DIRECT followed by MDS or GPS greatly increased efficiency while maintaining accuracy. Powell's method generally required more than 30 seconds (1 CPU) with a low success rate (0.3 or lower). This work indicates that parallel optimization on shared memory systems can markedly improve registration speed and accuracy, particularly for large initial misorientations.
Pre-computed finite element methods are valuable because of their extreme speed and high accuracy for soft tissue modeling, but they are not suitable for surgical incision simulation. In this paper we present an adaptive algorithm for finite element computation based on a preprocessing approach. It inverts the global stiffness matrix in a pre-computing stage and then simulates each cutting step by updating two lists of basic components iteratively with some localization techniques. This method allows a fast and physically accurate simulation of incision procedures.
New applications currently demand utilizing computed tomography (CT) scout images for diagnostic purposes. However, many CT scout images cannot be used diagnostically due to their poor resolution, particularly in the direction of table movement, and loss of detail when displayed with one view. We present two methods to address these two problems. First, spatial resolution generally can be improved with image restoration techniques. Based on the principles of Wiener filtering and inverse filtering, a modified Wiener filtering approach is presented in the frequency domain. The concept of an equivalent target point spread function is also introduced, which makes the restoration process steerable. Consequently, balancing resolution improvement with noise suppression is facilitated. Relevant experiments compare the image quality with traditional inverse filtering and Wiener filtering. The modified Wiener filtering method has been shown to restore the scout image with higher resolution and lower noise. In addition, CT scout images have a wide dynamic range, from 0 to 105 intensity values. They are difficult to display in full detail with only 8 bits (256 intensities). An image fusion approach is developed to preserve and enhance details of CT scout images. The enhanced image is obtained by high-boosting one fused image from another, both of which are computed by fusing a set of preenhanced subimages which derived from the original, using different fusion rules. Image fusion is performed pixel by pixel by the discrete wavelet transform. Final experiments compare the image quality of the resolution-improved and detail-enhanced image and the noise level with those of the original image. Results show that more details, easily observed by a radiologist, are present in the restored and enhanced image than in the original.
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.
KEYWORDS: Backscatter, Data modeling, Statistical analysis, Scattering, Ultrasonography, Tissues, Signal to noise ratio, Speckle, Image information entropy, Analytical research
This paper presents parameter estimation of general ultrasound backscatter models, such as the generalized Nakagami and generalized K 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 K 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.
Currently, new applications demand utilizing CT scout images for diagnostic purposes. However, many CT scout images cannot be used diagnostically due to their poor resolution, particularly in the direction of table movement. Spatial resolution generally can be improved with image restoration techniques. Based on the principles of Wiener filtering and inverse filtering, this paper presents a modified Wiener filtering approach in the frequency domain. The concept of equivalent target point spread function is introduced, which makes the restoration process steerable. Consequently, balancing resolution improvement with noise suppression is facilitated. Experiments compare the image quality with traditional inverse filtering and Wiener filtering. The modified Wiener filtering method has been shown to restore the scout image with higher resolution and lower noise.
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.
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