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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quantitating Interfraction Target Dynamics During Concurrent Chemoradiation for Glioblastoma: A Prospective Serial Imaging Study

Magnetic resonance image (MRI) guided radiation therapy has the potential to improve outcomes for glioblastoma by adapting to tumor changes during radiation therapy. This study quantifies interfraction dynamics (tumor size, position, and geometry) based on sequential magnetic resonance imaging scans obtained during standard 6-week chemoradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

100 Gb/s High Throughput Serial Protocol (HTSP) for data acquisition systems with interleaved streaming

Demands on Field-Programmable Gate Array (FPGA) data transport have been increasing over the years as frame sizes and refresh rates increase. As the bandwidths requirements increase the ability to implement data transport protocol layers using "soft" programmable logic becomes harder and start to require harden IP blocks implementation. To reduce the number of physical links and interconnects, it is common for data acquisition systems to require interleaving of streams on the same link (e.g. streaming data and streaming register access). Further, this paper presents a way to leverage existing FPGA harden IP blocks to achieve a robust, low latency 100 Gb/s point-to-point link with minimal programmable logic overhead geared towards the needs of data acquisition systems with interleaved streaming requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A Framework for Compressing Unstructured Scientific Data via Serialization

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.

Reshniak, Viktor [ORNL] (ORCID:0000000315454462)↗