Iatrogenic FDG foci in the lungs: a pitfall of PET image interpretation

Abstract.: 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) has become an important staging modality for many tumors, including bronchial carcinoma; however it is important to know that there are several pitfalls in PET image interpretation. In this report we demonstrate three cases in which focal intrapulmonary FDG uptake could possibly represent iatrogenic microembolism. These FDG accumulations would have been interpreted as malignant tumor mass in the lung if no anatomic correlation would have been performed. For this reason, we further present an integrated PET/CT scanner, which recently has been introduced. This correlation of molecular and morphological information enables the specification of the FDG-PET finding

Sub-matrix updates for the Continuous-Time Auxiliary Field algorithm

We present a sub-matrix update algorithm for the continuous-time auxiliary field method that allows the simulation of large lattice and impurity problems. The algorithm takes optimal advantage of modern CPU architectures by consistently using matrix instead of vector operations, resulting in a speedup of a factor of $\approx 8$ and thereby allowing access to larger systems and lower temperature. We illustrate the power of our algorithm at the example of a cluster dynamical mean field simulation of the N\'{e}el transition in the three-dimensional Hubbard model, where we show momentum dependent self-energies for clusters with up to 100 sites

SimFS: A Simulation Data Virtualizing File System Interface

Nowadays simulations can produce petabytes of data to be stored in parallel filesystems or large-scale databases. This data is accessed over the course of decades often by thousands of analysts and scientists. However, storing these volumes of data for long periods of time is not cost effective and, in some cases, practically impossible. We propose to transparently virtualize the simulation data, relaxing the storage requirements by not storing the full output and re-simulating the missing data on demand. We develop SimFS, a file system interface that exposes a virtualized view of the simulation output to the analysis applications and manages the re-simulations. SimFS monitors the access patterns of the analysis applications in order to (1) decide the data to keep stored for faster accesses and (2) to employ prefetching strategies to reduce the access time of missing data. Virtualizing simulation data allows us to trade storage for computation: this paradigm becomes similar to traditional on-disk analysis (all data is stored) or in situ (no data is stored) according with the storage resources that are assigned to SimFS. Overall, by exploiting the growing computing power and relaxing the storage capacity requirements, SimFS offers a viable path towards exa-scale simulations