A 3D Deep Learning Architecture for Denoising Low-Dose CT Scans

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Kasparian, Armen Caspar

Chairs dc:contributor.committeechair

• Feng, Wu-Chun
• Doan, Thinh Thanh

Committee member dc:contributor.committeemember

• Raghvendra, Sharath

• deep learning
• distributed data parallel
• transfer learning
• computed tomography
• image enhancement

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:39648

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/118623