Two-View Topogram-Based Anatomy-Guided CT Reconstruction for Prospective Risk Minimization

To facilitate a prospective estimation of CT effective dose and risk minimization process, a prospective spatial dose estimation and the known anatomical structures are expected. To this end, a CT reconstruction method is required to reconstruct CT volumes from as few projections as possible, i.e. by using the topograms, with anatomical structures as correct as possible. In this work, an optimized CT reconstruction model based on a generative adversarial network (GAN) is proposed. The GAN is trained to reconstruct 3D volumes from an anterior-posterior and a lateral CT projection. To enhance anatomical structures, a pre-trained organ segmentation network and the 3D perceptual loss are applied during the training phase, so that the model can then generate both organ-enhanced CT volume and the organ segmentation mask. The proposed method can reconstruct CT volumes with PSNR of 26.49, RMSE of 196.17, and SSIM of 0.64, compared to 26.21, 201.55 and 0.63 using the baseline method. In terms of the anatomical structure, the proposed method effectively enhances the organ shape and boundary and allows for a straight-forward identification of the relevant anatomical structures. We note that conventional reconstruction metrics fail to indicate the enhancement of anatomical structures. In addition to such metrics, the evaluation is expanded with assessing the organ segmentation performance. The average organ dice of the proposed method is 0.71 compared with 0.63 in baseline model, indicating the enhancement of anatomical structures

New hadrons as ultra-high energy cosmic rays

Ultra-high energy cosmic ray (UHECR) protons produced by uniformly distributed astrophysical sources contradict the energy spectrum measured by both the AGASA and HiRes experiments, assuming the small scale clustering of UHECR observed by AGASA is caused by point-like sources. In that case, the small number of sources leads to a sharp exponential cutoff at the energy E<10^{20} eV in the UHECR spectrum. New hadrons with mass 1.5-3 GeV can solve this cutoff problem. For the first time we discuss the production of such hadrons in proton collisions with infrared/optical photons in astrophysical sources. This production mechanism, in contrast to proton-proton collisions, requires the acceleration of protons only to energies E<10^{21} eV. The diffuse gamma-ray and neutrino fluxes in this model obey all existing experimental limits. We predict large UHE neutrino fluxes well above the sensitivity of the next generation of high-energy neutrino experiments. As an example we study hadrons containing a light bottom squark. These models can be tested by accelerator experiments, UHECR observatories and neutrino telescopes.Comment: 17 pages, revtex style; v2: shortened, as to appear in PR

Antideuterons from dark matter annihilations and hadronization model dependence

We calculate the antideuteron yield in dark matter annihilations on an event-by-event basis using the herwig++ Monte Carlo generator. We present the resulting antideuteron fluxes for quark and gauge boson final states. As deuteron production in the coalescence model depends on momentum differences between nucleons that are small compared to ΛQCD, it is potentially very sensitive to the hadronization model employed. We therefore compare our antideuteron yields to earlier results based on pythia, thereby estimating their uncertainties. We also briefly discuss the importance of n>2 final states for annihilations of heavy dark matter particles. © 2012 American Physical Societ

CR propagation at TeV–PeV, the knee, secondaries, . . .

<p>Talk by Michael Kachelrieß at CRATER2018, Gran Sasso Science Institute, L'Aquila</p

Hyperfast Perspective Cone–Beam Backprojection

Abstract — Cone–beam image reconstruction, such as the reconstruction of CT projection values, is computational very demanding. The most time–consuming step is the backprojection that is often limited by the memory bandwidth. Recently, a novel general purpose architecture optimized for distributed computing became available: the Cell Broadband Engine (CBE). Its eight synergistic processing elements (SPEs) currently allow for a theoretical performance of 192 GFlops (3 GHz, 8 units, 4 floats per vector, 2 instructions, multiply and add, per clock). Our aim is to maximize the image reconstruction speed for flat–panel–based cone–beam CT such as micro–CT or C–arm– CT. Therefore we implemented a highly optimized perspective cone–beam backprojection algorithm on the Cell processor. Data mining techniques and double buffering of source data were extensively used to optimally utilize both the memory bandwidth and the available local store of each SPE. The voxel–driven backprojection code uses 32 bit floating point arithmetic and bilinear interpolation between neighboring detector channels. The latter is performed in two stages by first upsampling the detector (this includes bilinear interpolation) to double the number of detector pixels followed by a nearest neighbor interpolation during backprojection. Performance was measured by backprojecting simulated data with 512 cone–beam projections per full rotation and 1024 by 1024 detector elements. The data were backprojected into a volume of 512 3 voxels fully contained in the field of measurement using an optimized PC–based (CPU–based) approach and the new Cell–based (CBE–based) algorithm. Both the PC and the CBE were clocked at 3 GHz. PC–based backprojection takes 3.2 min whereas the CBE version finishes within 13.6 s. Using both CBEs of our dual Cell–based blade (Mercury Computer Systems) one can do the cone–beam backprojection in 6.8 s. I

Implementation of a cone-beam backprojection algorithm on the Cell Broadband Engine processor

Tomographic image reconstruction is computationally very demanding. In all cases the backprojection represents the performance bottleneck due to the high operational count and due to the high demand put on the memory subsystem. In the past, solving this problem has lead to the implementation of specific architectures, connecting Application Specific Integrated Circuits (ASICs) or Field Programmable Gate Arrays (FPGAs) to memory through dedicated high speed busses. More recently, there have also been attempt to use Graphic Processing Units (GPUs) to perform the backprojection step. Originally aimed at the gaming market, IBM, Toshiba and Sony have introduced the Cell Broadband Engine (CBE) processor, often considered as a multicomputer on a chip. Clocked at 3 GHz, the Cell allows for a theoretical performance of 192 GFlops and a peak data transfer rate over the internal bus of 200 GB/s. This performance indeed makes the Cell a very attractive architecture for implementing tomographic image reconstruction algorithms. In this study, we investigate the relative performance of a perspective backprojection algorithm when implemented on a standard PC and on the Cell processor. We compare these results to the performance achievable with FPGAs based boards and high end GPUs. The cone-beam backprojection performance was assessed by backprojecting a full circle scan of 512 projections of 1024x1024 pixels into a volume of size 512x512x512 voxels. It took 3.2 minutes on the PC (single CPU) and is as fast as 13.6 seconds on the Cell