1,711 research outputs found
The grid-dose-spreading algorithm for dose distribution calculation in heavy charged particle radiotherapy
A new variant of the pencil-beam (PB) algorithm for dose distribution
calculation for radiotherapy with protons and heavier ions, the grid-dose
spreading (GDS) algorithm, is proposed. The GDS algorithm is intrinsically
faster than conventional PB algorithms due to approximations in convolution
integral, where physical calculations are decoupled from simple grid-to-grid
energy transfer. It was effortlessly implemented to a carbon-ion radiotherapy
treatment planning system to enable realistic beam blurring in the field, which
was absent with the broad-beam (BB) algorithm. For a typical prostate
treatment, the slowing factor of the GDS algorithm relative to the BB algorithm
was 1.4, which is a great improvement over the conventional PB algorithms with
a typical slowing factor of several tens. The GDS algorithm is mathematically
equivalent to the PB algorithm for horizontal and vertical coplanar beams
commonly used in carbon-ion radiotherapy while dose deformation within the size
of the pristine spread occurs for angled beams, which was within 3 mm for a
single proton pencil beam of incidence, and needs to be assessed
against the clinical requirements and tolerances in practical situations.Comment: 7 pages, 3 figure
Kernels for sequentially ordered data
We present a novel framework for learning with sequential data of any kind, such as multivariate time series, strings, or sequences of graphs. The main result is a ”sequentialization”
that transforms any kernel on a given domain into a kernel for sequences in that domain.
This procedure preserves properties such as positive definiteness, the associated kernel feature map is an ordered variant of sample (cross-)moments, and this sequentialized kernel
is consistent in the sense that it converges to a kernel for paths if sequences converge to
paths (by discretization). Further, classical kernels for sequences arise as special cases of
this method. We use dynamic programming and low-rank techniques for tensors to provide
efficient algorithms to compute this sequentialized kernel
BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images
In cryo-electron microscopy (EM), molecular structures are determined from
large numbers of projection images of individual particles. To harness the full
power of this single-molecule information, we use the Bayesian inference of EM
(BioEM) formalism. By ranking structural models using posterior probabilities
calculated for individual images, BioEM in principle addresses the challenge of
working with highly dynamic or heterogeneous systems not easily handled in
traditional EM reconstruction. However, the calculation of these posteriors for
large numbers of particles and models is computationally demanding. Here we
present highly parallelized, GPU-accelerated computer software that performs
this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI
parallelization combined with both CPU and GPU computing. The resulting BioEM
software scales nearly ideally both on pure CPU and on CPU+GPU architectures,
thus enabling Bayesian analysis of tens of thousands of images in a reasonable
time. The general mathematical framework and robust algorithms are not limited
to cryo-electron microscopy but can be generalized for electron tomography and
other imaging experiments
- …