This paper introduces 3D-DDnet, a cutting-edge 3D deep learning (DL) framework designed to improve the image quality of low-dose computed tomography (LDCT) scans. Although LDCT scans are advantageous for reducing radiation exposure, they inherently suffer from reduced image quality. Our novel 3D DL architecture addresses this issue by effectively enhancing LDCT images to achieve parity with the quality of standard-dose CT scans. By exploiting the inter-slice correlation present in volumetric CT data, 3D-DDnet surpasses existing denoising benchmarks. It incorporates distributed data parallel (DDP) and transfer learning techniques to significantly accelerate the training process. The DDP approach is particularly tailored for operation across multiple Nvidia A100 GPUs, facilitating the processing of large-scale volumetric data sets that were previously unmanageable due to size constraints. Comparative analyses demonstrate that 3D-DDnet reduces the mean square error (MSE) by 10% over its 2D counterpart, 2D-DDnet. Moreover, by applying transfer learning from pre-trained 2D models, 3D-DDnet effectively 'jump starts' the learning process, cutting training times by half without compromising on model accuracy. / Master of Science / This research focuses on improving the quality of low-dose CT scans using advanced technology. CT scans are medical imaging techniques used to see inside the body. Low-dose CT (LDCT) scans use less radiation than standard CT scans, making them safer, but the downside is that the images are not as clear. To solve this problem, we developed a new deep learning method to make these low-dose images clearer and as good as regular CT scans. Our approach, called 3D-DDnet, is unique because it looks at the scans in 3D, considering how slices of the scan are related, which helps remove the noise and improve the image quality. Additionally, we used a technique called distributed data parallel (DDP) with advanced GPUs (graphics processing units, which are powerful computer components) to speed up the training of our system. This means our method can learn to improve images faster and work with larger data sets than before. Our results are promising: 3D-DDnet improved the image quality of low-dose CT scans significantly better than previous methods. Also, by using what we call "transfer learning" (starting with a pre-made model and adapting it), we cut the training time in half without losing accuracy. This development is essential for making low-dose CT scans more effective and safer for patients.