2026-10-12T19:39:26Z https://www.scidb.cn/oai

10.57760/sciencedb.012n5 2026-09-20T11:05:20Z

2026-09-20 Associated Data for “Self-Paced Learning-Driven Training-Free Deep Generative Magnetic Resonance Image Denoising” doi:10.57760/sciencedb.012n5 en This dataset contains the associated data for the article “Self-Paced Learning-Driven Training-Free Deep Generative Magnetic Resonance Image Denoising.” It supports the quantitative comparisons, parameter sensitivity analyses, weighting-strategy evaluation, and computational-efficiency analysis reported in the article. The proposed SP-DIP method divides noisy magnetic resonance (MR) images into overlapping patches and progressively incorporates patches into network optimization according to their fitting losses. The denoised patches are subsequently returned to their original spatial positions and averaged pixelwise to reconstruct the complete image.The experiments mainly used brain and knee MR images. Data #1 comprises T2-weighted brain MR data with an original matrix size of 256 × 232 × 208, acquired using a 3.0 T MR scanner and a 12-channel coil. Data #2 comprises T1-weighted knee MR data with an original matrix size of 320 × 320 × 227, acquired using a 3.0 T Siemens MR scanner and a 15-channel coil. Its in-plane spatial resolution is 0.44 × 0.44 mm², with a slice thickness of 3 mm. Ten two-dimensional slices were selected from each dataset. This study does not involve time-series data, and no personally identifiable information is included.The deposited files contain quantitative comparisons among SP-DIP, Patch-DIP, K-SVD, CBM3D, S2S, and CNN-DMRI, together with parameter sensitivity, weighting-strategy, performance-curve, and computational-efficiency results. Self-Paced Learning; Deep Generative; Magnetic Resonance Image Denoising Tan Yumin Liang Dong RESTRICTED dataset Science Data Bank