This paper highlights the potential of medical imaging AI and its limitations due to data shortages and privacy concerns. To address these challenges, we propose a novel generative diffusion model, MedLoRD, capable of generating high-resolution medical images even in computationally limited environments. Using a GPU with 24 GB of VRAM, MedLoRD generates high-dimensional medical images with a resolution of up to 512x512x256. Extensive evaluations using coronary CT angiography and lung CT datasets demonstrate that MedLoRD outperforms existing state-of-the-art models. The evaluation encompasses various aspects, including radiological evaluation, relative volume analysis, compliance with conditional masks, and follow-up tasks.