  Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique
offering high-resolution 3D images and valuable insights into human tissue
conditions. Even at present, the refinement of denoising methods for MRI
remains a crucial concern for improving the quality of the images. This study
aims to enhance the prefiltered rotationally invariant non-local principal
component analysis (PRI-NL-PCA) algorithm. This paper relaxed the original
restriction, using the particle swarm optimization and traversal method to
determine the optimal parameters of the algorithm. This paper also combined the
component filters of the original algorithm and picked the most suitable
combination as the new collaborative algorithm. It was found that the original
algorithm has already achieved the best possible outcome, apart from a few
threshold parameters that need to be adjusted. The effective way to further
enhance the performance is to attach only one NL-PCA filter before and after
the pre-filtered rotationally invariant non-local mean (PRI-NLM) filter.
Although the performance of the new collaborative algorithm is still a little
short of advanced deep learning methods, it shows that the algorithm based on
PCA denoising is indeed feasible. It requires only a few parameters to be
adjusted, and it is conceivable that they can be determined directly from the
image, granting it a strong general capacity for various body parts, and it
merits further exploration. An auxiliary tool was also extracted from the new
algorithm, encouraging further combination of it with other state-of-the-art
methods to further improve their denoising performance.
