Volumetric optical microscopy using non-diffracting beams, offers rapid imaging of 3D volumes by projecting them axially to 2D images, but it often falls short in providing depth information. To address this limitation, we introduce MicroDiffusion, a pioneering tool designed for high-quality, depth-resolved 3D volume reconstruction from a limited set of 2D microscopy projections. Existing 3D reconstruction methods, such as Implicit Neural Representation (INR) models, often result in incomplete and noisy outputs. In contrast, Denoising Diffusion Probabilistic Models (DDPM) excel at capturing fine-grained details. Our method merges the strengths of INR’s ability to maintain structural 3D coherence with DDPM’s proficiency in enhancing details.Initially, we pretrain an INR model that transforms the 2D axially-projected images into a preliminary 3D volume. Then, the pretrained INR serves as a global prior, directing DDPM's generative process through linear interpolation between INR outputs and noise inputs. This strategy effectively enriches the diffusion process with structured 3D information while simultaneously enhancing detail and minimizing noise in localized 2D images. Furthermore, by conditioning the diffusion model on the closest 2D image, MicroDiffusion substantially enhances the fidelity of the resulting 3D reconstructions. MicroDiffusion enables depth-resolved volumetric microscopy by delivering high-quality 3D reconstructions that are sharper than those produced by INR models and more coherent than standard DDPM outputs.Extensive results from three microscopy datasets demonstrate MicroDiffusion's superiority in producing 3D reconstructions with enhanced image quality, structural coherence, and fidelity, compared to traditional INR and diffusion models.