Aragonés Lozano, MarioRomero Martínez, José OscarLeón Fernández, Antonio2026-06-082026-06-082026-04-16https://riunet.upv.es/handle/10251/235914[EN] This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspired by the logic of matrix singular value decomposition (SVD), the proposed approach represents third-order volumetric data in the spatial domain and supports progressive reconstruction through ordered quasi-singular coefficients. The experimental evaluation was carried out on two biological volumetric datasets: one full-volume scan of a fish and another of a brain. The results show that Structured 3D-SVD achieves reconstruction quality close to that of Tucker decomposition while requiring shorter computation times and outperforms canonical polyadic decomposition (CPD) in both accuracy and runtime. In addition, a progressive reconstruction analysis shows that relatively low truncation levels are sufficient to preserve the main volumetric structures, while higher truncation levels lead to more detailed reconstructions.Reconocimiento (by)Structured 3D-SVDTensor decompositionVolumetric imagingProgressive reconstructionBiological imagingTensor compressionArtículo10.3390/app16083887Abierto2076-3417