q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep networks are trained to learn the mapping directly from diffusion signals to tissue microstructure. ...
the refresh rate of digital micro-mirror devices (DMDs) has significantly increased, reaching tens of thousands fps, while at an affordable price. This motivated us to combine computational imaging and deep learning to encode temporal information in space and break through...
Interplanetary space probes entering celestial atmospheres at hypersonic speeds convert large amounts of kinetic energy into thermal energy behind a strong shock wave. For the fastest re-entry conditions, ablative thermal protection systems (TPS) are adopted as heat shields1,2,3. Ablators use thermo...
Deep learning-based analysis can address limitations using massive training sets generated from both experimental and theoretical point spread function model. The number of emitters can be optimized, e.g., by the integration of microfluidic channels with an automated flow system to place bead emitters...
q-Space deep learning ( q -DL) enables accurate estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans with signals undersampled in the q -space. However, in many scenarios, such as clinical settings, the quality of tissue microstructure estimation is limited ...
This could help in creating a deep learning model allowing for 2D to 3D mapping based on mid-sagittal images only. Nevertheless, an obvious disadvantage of the 2D multislice strategy is the need to repeat the corpus multiple times. These repetitions are not always temporally aligned. The data ...