Dynamic magnetic resonance imaging method based on golden-ratio cartesian sampling and compressed sensing - Fig 3

<p>Point spread function comparison between (a) the golden-angle radial trajectory and (b) the proposed trajectory.</p

Mean AP values of reconstructed images versus <i>N</i> with a temporal resolution of 10TR.

<p>Mean AP values of reconstructed images versus <i>N</i> with a temporal resolution of 10TR.</p

Mean AP values of the four methods with different temporal resolutions.

<p>Mean AP values of the four methods with different temporal resolutions.</p

“Shepp-Logan” phantom used in the simulation.

<p>“Shepp-Logan” phantom used in the simulation.</p

The proposed BISA system.

<p>A gray-scale image is pre-processed with local contrast normalization and then a number of image patches are randomly cropped for CNN training, validation and final testing.</p

Performance evaluation with PLCC on Gaussian blurring images.

<p>Performance evaluation with PLCC on Gaussian blurring images.</p

CNN prediction performance with <i>N</i>_<i>i</i> or <i>P</i>_<i>n</i> changes.

<p>CNN prediction performance with <i>N</i>_<i>i</i> or <i>P</i>_<i>n</i> changes.</p

The time spent on score prediction of image sharpness.

<p>Several algorithms show promise in real-time image sharpness estimation.</p

Performance evaluation of SROCC on Gaussian blurring images.

<p>Performance evaluation of SROCC on Gaussian blurring images.</p

Example of Gaussian blurring images in four databases.

<p>Example of Gaussian blurring images in four databases.</p