Abstract: Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is an essential technique for in-situ, micro-scale elemental characterization. However, most existing studies are limited to two-dimensional (2D) analysis, making it difficult to characterize the three-dimensional (3D) spatial distribution of elements in biological tissues. The 3D reconstruction of elemental distributions in biological tissues faces several key challenges, including noise interference from continuous sectioning, difficulties in elemental boundary extraction, and quantitative deviations caused by the significant differences between organic matrices and geological reference materials. Furthermore, current methods struggle to achieve simultaneous multi-element characterization, which restricts the analysis of elemental transport mechanisms in crops such as rice. Rice seeds are typical organic-inorganic composite matrices, containing a variety of organic components such as starch, proteins, lipids, and phytic acid, as well as associated inorganic structures. In this study, targeting the complex organic-inorganic composite matrix of rice seeds, we selected 13 target elements covering high-abundance mineral elements (e.g., Ca, Si, P), trace elements (Zn, Cu, Li, Sr, Ba, Se), and potential heavy metals (Al, Pb, Sn), and transferred the LA-ICP-MS mapping technique, a well-established method in geological research, to the analysis of biological samples. A standardized workflow was established, comprising epoxy resin embedding, serial sectioning, LA-ICP-MS mapping, data processing, and three-dimensional reconstruction. Integrated with the self-developed LIMS 2.0 software and the alphaShape algorithm, this workflow effectively suppressed noise, extracted elemental boundaries, and enabled focused analysis of elemental distribution patterns. The results demonstrate that a three-dimensional distribution model for all 13 target elements in rice seeds was successfully constructed, and three typical distribution patterns were identified: Ca, Si, Cu, and related elements exhibit surface-enriched ring-like accumulation; Se is uniformly distributed throughout the entire seed; and P shows irregular blocky enrichment in the endosperm core. Multi-element false-color composite imaging achieved precise spatial alignment between elemental distributions and anatomical structures including the husk, bran, aleurone layer, endosperm, and germ, effectively resolving the issue of ambiguous tissue boundary identification that commonly arises in conventional 2D LA-ICP-MS imaging. This work confirms that the proposed method enables accurate characterization of elemental spatial distributions across different seed tissues, providing reliable 3D data support for noise suppression and precise elemental boundary extraction, and offering a novel analytical approach for micro-scale elemental distribution studies in crop seeds.