Digital breast tomosynthesis (DBT) has been investigated as a promising alternative to conventional X-ray
mammography for breast cancer screening. By reconstructing 3D volumetric images from multiple 2D projections
measured over a limited angular range, it can offer depth-directional information and improve both sensitivity
and specificity of cancer detection in dense breasts. The diagnostic performance of DBT can be affected by
a number of imaging parameters. The angular range of scan orbit is one of the most crucial factors, since
it determines the depth-directional resolution. Recently, we proposed the wide angle tomosynthesis based on
voltage modulations of X-ray source. By using X-rays with large penetration power on exterior positions, it
can acquire high-SNR projections over a wide angular range. In this paper, we present comparative studies on
exposure conditions in DBT, including narrow and wide angle scan using an invariant tube voltage of X-ray
source, and wide angle scan with the voltage modulation technique. In addition, we compared the conventional
reconstruction methods with recently proposed IDIR algorithms. In preliminary studies, the wide-angle scheme
with proposed IDIR algorithm showed superior performances in detecting abnormal lesions over conventional
approaches.
Single particle reconstruction is often employed for 3-D reconstruction of diverse macromolecules. However, the
algorithm requires a good initial guess from a priori information to guarantee the convergence to the correct
solution. This paper describes a novel model free 3-D reconstruction algorithm by employing the symmetry
and sparsity of unknown structure. Especially, we develop an accurate and fully automatic iterative algorithm
for 3D reconstruction of unknown helix structures. Because the macromolecule structure assumes only sparse
supports in real space and the helical symmetry provides several symmetric views from a single micrograph,
a reasonably quality 3-D reconstruction can be obtained from the limited views using the compressed sensing
theory. Furthermore, the correct helix parameters usually provide the maximal variance of the reconstructed
volume, facilitating the parameter estimation. Remarkably, the search space of helix parameter can be drastically
reduced by exploiting the diffraction pattern. With the estimated helix parameter and additional 3-D registration,
the multiple helix segments can be combined for the optimal quality reconstruction. Experimental results using
synthetic and real helix data confirm that our algorithm provides superior reconstruction of 3-D helical structure.
Sparse object supports are often encountered in many imaging problems. For such sparse objects, recent theory
of compressed sensing tells us that accurate reconstruction of objects are possible even from highly limited
number of measurements drastically smaller than the Nyquist sampling limit by solving L1 minimization problem.
This paper employs the compressed sensing theory for cryo-electron microscopy (cryo-EM) single particle
reconstruction of virus particles. Cryo-EM single particle reconstruction is a nice application of the compressed
sensing theory because of the following reasons: 1) in some cases, due to the difficulty in sample collection, each
experiment can obtain micrographs with limited number of virus samples, providing undersampled projection
data, and 2) the nucleic acid of a viron is enclosed within capsid composed of a few proteins; hence the support
of capsid in 3-D real space is quite sparse. In order to minimize the L1 cost function derived from compressed
sensing, we develop a novel L1 minimization method based on the sliding mode control theory. Experimental
results using synthetic and real virus data confirm that the our algorithm provides superior reconstructions of
3-D viral structures compared to the conventional reconstruction algorithms.
A novel support vector machine (SVM) classifier incorporating the complexity of fluorescent spectral data is
designed to reliably differentiate normal and malignant human breast cancer tissues. Analysis has been carried
out with parallel and perpendicularly polarized fluorescence data using 36 normal and 36 cancerous tissue samples.
In order to incorporate the complexity of fluorescence spectral profile into a SVM design, the curvature of phase
space trajectory is extracted as a useful complexity feature. We found that the fluorescence intensity peaks at
541nm-620nm as well as the complexity features at 621nm-700nm are important discriminating features. By
incorporating both features in SVM design, we can improve both sensitivity and specificity of the classifier.
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