Enhanced blood perfusion in a tissue mass is an indication of neo-vascularity and a sign of a potential malignancy. Ultrasonic pulsed-Doppler imaging is a preferred modality for noninvasive monitoring of blood flow. However, the weak blood echoes and disorganized slow flow make it difficult to detect perfusion using standard methods without the expense and risk of contrast enhancement. Our research measures the efficiency of conventional power-Doppler (PD) methods at discriminating flow states by comparing measurement performance to that of an ideal discriminator. ROC analysis applied to the experimental results shows that power Doppler methods are just 30-50 % efficient at perfusion flows less than 1ml/min, suggesting an opportunity to improve perfusion assessment through signal processing. A new perfusion estimator is proposed by extending the statistical discriminator approach. We show that 2-D perfusion color imaging may be enhanced using this approach.
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.
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