Optimal <i>h</i> values for different denoising algorithms, image types, radius of patches (<i>r_p</i>), and a search window with a radius (<i>r_s</i>) of 5.

<p>Optimal <i>h</i> values for different denoising algorithms, image types, radius of patches (<i>r_p</i>), and a search window with a radius (<i>r_s</i>) of 5.</p

Comparison of RNLM and RNLM-CPP algorithms on denoising simulated T1w images.

<p>Top row, from left to right: noisy image with 5% of Rician noise, denoised results with different algorithms. Second row, from left to right: zoomed part of the corresponding images in the top row, the dotted boxes indicate the local areas around manually-defined particles. Bottom row, from left to right: T1w noise-free image and corresponding image residuals.</p

PSNR comparison of RNLM and RNLM-CPP algorithms under varying noise levels (ranging from 1% to 9% with an increase of 2%) for different image types (T1w, T2w, and PD) and patch sizes (radius of 1, 2, and 3).

<p>PSNR comparison of RNLM and RNLM-CPP algorithms under varying noise levels (ranging from 1% to 9% with an increase of 2%) for different image types (T1w, T2w, and PD) and patch sizes (radius of 1, 2, and 3).</p

Two MSE plots or parameter selections of the neighbor window (A) and the controlling parameters (B) of KIBF algorithm.

<p>Two MSE plots or parameter selections of the neighbor window (A) and the controlling parameters (B) of KIBF algorithm.</p

MSE plots for parameter selections of the standard deviation for GF algorithm (A) and the controlling parameters of BF algorithm (B).

<p>MSE plots for parameter selections of the standard deviation for GF algorithm (A) and the controlling parameters of BF algorithm (B).</p

LSSIM results for quantitative comparison of RNLM and RNLM-CPP algorithms with parameters (<i>r_p</i> = 1, <i>r_s</i> = 5, = 4, and = 5) for T1w, T2w and PDw images.

<p>LSSIM results for quantitative comparison of RNLM and RNLM-CPP algorithms with parameters (<i>r_p</i>  = 1, <i>r_s</i>  = 5,  = 4, and  = 5) for T1w, T2w and PDw images.</p

PSNRs at each frame of the activity images reconstructed by different algorithms.

<p>PSNRs at each frame of the activity images reconstructed by different algorithms.</p

The ¹⁸F-FDG PET simulation settings.

<p>(A) A brain phantom composed of gray matter, white matter and a small tumor; (B) the blood input function and regional time activity curves.</p

Kinetic parameters used in the ¹⁸F-FDG PET simulation.

<p>Kinetic parameters used in the ¹⁸F-FDG PET simulation.</p

The TSNRs of the activity images reconstructed by different algorithms.

<p>The TSNRs of the activity images reconstructed by different algorithms.</p