15 research outputs found
Real-time segmentation for tomographic imaging
In tomography, reconstruction and analysis is often performed once the acquisition has been completed due to the computational cost of the 3D imaging algorithms. In contrast, real-time reconstruction and analysis can avoid costly repetition of experiments and enable optimization of experimental parameters. Recently, it was shown that by reconstructing a subset of arbitrarily oriented slices, real-time quasi-3D reconstruction can be attained. Here, we extend this approach by including realtime segmentation, thereby enabling real-time analysis during the experiment. We propose to use a convolutional neural network (CNN) to perform real-time image segmentation and introduce an adapted training strategy in order to apply CNNs to arbitrarily oriented slices. We evaluate our method on both simulated and real-world data. The experiments show that our approach enables realtime tomographic segmentation for real-world applications and outperforms standard unsupervised segmentation methods
Scaled Projected-Directions Methods with Application to Transmission Tomography
Statistical image reconstruction in X-Ray computed tomography yields
large-scale regularized linear least-squares problems with nonnegativity
bounds, where the memory footprint of the operator is a concern. Discretizing
images in cylindrical coordinates results in significant memory savings, and
allows parallel operator-vector products without on-the-fly computation of the
operator, without necessarily decreasing image quality. However, it
deteriorates the conditioning of the operator. We improve the Hessian
conditioning by way of a block-circulant scaling operator and we propose a
strategy to handle nondiagonal scaling in the context of projected-directions
methods for bound-constrained problems. We describe our implementation of the
scaling strategy using two algorithms: TRON, a trust-region method with exact
second derivatives, and L-BFGS-B, a linesearch method with a limited-memory
quasi-Newton Hessian approximation. We compare our approach with one where a
change of variable is made in the problem. On two reconstruction problems, our
approach converges faster than the change of variable approach, and achieves
much tighter accuracy in terms of optimality residual than a first-order
method.Comment: 19 pages, 7 figure