Kasparian, Armen Caspar2024-04-172024-04-172024-04-11vt_gsexam:39648https://hdl.handle.net/10919/118623This 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.ETDenIn Copyrightdeep learningdistributed data paralleltransfer learningcomputed tomographyimage enhancementThesis