Image Reconstruction of Spiral Trajectory Acquired MRI Raw Data Using Gridding Algorithm

Following user instructions, I will elaborate on the image reconstruction process for MRI raw data acquired through spiral trajectories. This process involves applying the gridding algorithm to transform raw k-space data into final medical images. The gridding algorithm is a widely-used reconstruction technique that performs interpolation and reverse interpolation operations on acquired raw data to restore image details and clarity. Key implementation steps typically include:

1. Density compensation to account for non-uniform sampling in spiral trajectories 2. Convolution with a Kaiser-Bessel kernel for optimal gridding performance 3. Fast Fourier Transform (FFT) application to convert gridded data to image space 4. Deapodization correction to eliminate kernel-induced artifacts

Special consideration must be given to spiral trajectory characteristics, including trajectory radius, angular sampling density, and acquisition speed parameters, to ensure reconstruction accuracy and image quality. The algorithm implementation often involves MATLAB functions like interp2 for 2D interpolation or customized gridding functions handling non-Cartesian data. Through this method, we can extract enhanced image information from spiral-acquired MRI raw data, providing more comprehensive and precise data support for medical diagnosis and research applications.