KEYWORDS: Heart, Data modeling, Positron emission tomography, Reconstruction algorithms, Magnetic resonance imaging, Motion models, Data acquisition, Cardiovascular magnetic resonance imaging, 3D modeling, Mathematical modeling
In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images.
The Living Heart Model (LHM) was developed as part of the Living Heart Project by Dassault Systemes to provide a numerical finite element (FE) model of the human heart that accurately reproduces the normal cardiac physiology. We previously incorporated the LHM into the 4D extended cardiac-torso (XCAT) phantom for imaging research, rigidly transforming the model to fit it within different anatomies. This captured the variation in the overall size, position, and orientation of the heart but did not capture more subtle geometrical changes. Anatomic measurements of normal heart structures can show standard deviation variations of upwards of 25-30%. In this work, we investigate the use of Hyperelastic Warping to non-rigidly fit the LHM to new anatomies based on 4D CT data from anatomically diverse, normal patients. For each patient target, the mid-diastolic frame from the CT (heart is most relaxed) was segmented to define the cardiac chambers. The geometry of the LHM was then altered to match the targets using Hyperelastic Warping to register the LHM mesh, in its relaxed state, to each segmented dataset. The altered meshes were imported back into the FE software to simulate cardiac motion from the new geometries to incorporate into the XCAT phantom. By preserving the underlying LHM architecture, our work shows that Hyperelastic Warping allows for efficient revision of the LHM geometry. This method can produce a diverse collection of heart models, with added interior variability, to incorporate into the XCAT phantom to investigate 4D imaging methods used to diagnose and treat cardiac disease.
Breast imaging is an important area of research with many new techniques being investigated to further reduce the morbidity and mortality of breast cancer through early detection. Computerized phantoms can provide an essential tool to quantitatively compare new imaging systems and techniques. Current phantoms, however, lack sufficient realism in depicting the complex 3D anatomy of the breast. In this work, we created one-hundred realistic and detailed 3D computational breast phantoms based on high-resolution CT datasets from normal patients. We also developed a finiteelement application to simulate different compression states of the breast, making the phantoms applicable to multimodality imaging research. The breast phantoms and tools developed in this work were packaged into user-friendly software applications to distribute for breast imaging research.
KEYWORDS: Breast, Tissues, Data modeling, Image segmentation, Mathematical modeling, 3D modeling, Image processing, Mammography, 3D image processing, Image processing algorithms and systems
We previously proposed a three-dimensional computerized breast phantom that combines empirical data with
the flexibility of mathematical models1. The goal of this project is to enhance the breast phantom to include a
more detailed anatomy than currently visible and create additional phantoms from different breast CT data.
To improve the level of detail in our existing segmentations, the breast CT data is reconstructed at a higher
resolution and additional image processing techniques are used to correct for noise and scatter in the image
data. A refined segmentation algorithm is used that incorporates more detail than previously defined. To
further enhance high-resolution detail, mathematical models, implementing branching algorithms to extend
the glandular tissue throughout the breast and to define Cooper's ligaments, are under investigation. We
perform the simulation of mammography and tomosynthesis using an analytical projection algorithm that can
be applied directly to the mathematical model of the breast without voxelization2. This method speeds up
image acquisition, reduces voxelization artifacts, and produces higher resolution images than the previously
used method. The realistic 3D computerized breast phantom will ultimately be incorporated into the 4DXCAT
phantom3-5 to be used for breast imaging research.
KEYWORDS: 3D modeling, Computed tomography, Heart, Arteries, Image segmentation, Data modeling, Ischemia, Medical imaging, Instrument modeling, 3D image processing
A realistic 3D coronary arterial tree (CAT) has been developed for the heart model of the computer generated 3D
XCAT phantom. The CAT allows generation of a realistic model of the location, size and shape of the associated
regional ischemia or infarction for a given coronary arterial stenosis or occlusion. This in turn can be used in medical
imaging applications. An iterative rule-based generation method that systematically utilized anatomic, morphometric
and physiologic knowledge was used to construct a detailed realistic 3D model of the CAT in the XCAT phantom. The
anatomic details of the myocardial surfaces and large coronary arterial vessel segments were first extracted from cardiac
CT images of a normal patient with right coronary dominance. Morphometric information derived from porcine data
from the literature, after being adjusted by scaling laws, provided statistically nominal diameters, lengths, and
connectivity probabilities of the generated coronary arterial segments in modeling the CAT of an average human. The
largest six orders of the CAT were generated based on the physiologic constraints defined in the coronary generation
algorithms. When combined with the heart model of the XCAT phantom, the realistic CAT provides a unique
simulation tool for the generation of realistic regional myocardial ischemia and infraction. Together with the existing
heart model, the new CAT provides an important improvement over the current 3D XCAT phantom in providing a more
realistic model of the normal heart and the potential to simulate myocardial diseases in evaluation of medical imaging
instrumentation, image reconstruction, and data processing methods.
The goal of this work is to create a detailed three-dimensional (3D) digital breast phantom based on
empirical data and to incorporate it into the four-dimensional (4D) NCAT phantom, a computerized
model of the human anatomy widely used in imaging research. Twenty sets of high-resolution breast
CT data were used to create anatomically diverse models. The datasets were segmented using
techniques developed in our laboratory and the breast structures will be defined using a combination of
non-uniform rational b-splines (NURBS) and subdivision surfaces (SD). Imaging data from various
modalities (x-ray and nuclear medicine) were simulated to demonstrate the utility of the new breast
phantoms. As a proof of concept, a simple compression technique was used to deform the breast models
while maintaining a constant volume to simulate modalities (mammography and tomosynthesis) that
involve compression. Initial studies using one CT dataset indicate that the simulated breast phantom is
capable of providing a realistic and flexible representation of breast tissue and can be used with
different acquisition methods to test varying imaging parameters such as dose, resolution, and patient
motion. The final model will have a more accurate depiction of the internal breast structures and will
be scaleable in terms of size and density. Also, more realistic finite-element techniques will be used to
simulate compression. With the ability to simulate realistic, predictive patient imaging data, we believe
the phantom will provide a vital tool to investigate current and emerging breast imaging methods and
techniques.
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