Processing Time Reduction: an Application in Living Human High-Resolution Diffusion Magnetic Resonance Imaging Data.
| Autores | Lori NF, Ibañez A, Lavrador R, Fonseca L, Santos C, Travasso R, Pereira A, Rossetti R, Sousa N, Alves V. |
|---|---|
| Año | 2016 |
| Journal | J Med Syst. |
| Volumen | Epub 2016 Sep 29. |
| Abstract | High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal’s quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data. |
| Resumen | Los métodos de conectividad estructural del cerebro requieren mucho tiempo de procesamiento. En este trabajo se demuestra que usando una función de distribución de la orientación axonal en combinación con métodos de Monte Carlo para la selección de vóxeles se puede reducir el 50% del tiempo de computo sin perder calidad de la señal. Los resultados son relevante para el análisis de grandes conjuntos de datos de conectividad cerebral. |