This repository accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques."

Computed Tomography (CT) is a widely employed non-destructive testing tool. In industrial applications, minimizing scanning time is crucial for efficient in-line inspection. One approach to achieve faster scanning is through low-dose CT. However, the reduction in radiation dose results in increased noise levels in the reconstructed CT images. Deep learning-based post-processing methods have shown promise in mitigating this noise, but their effectiveness relies on access to datasets with a substantial amount of training data.

Existing low-dose CT datasets either are not specifically tailored for industrial applications or are based on simulated image formation. In this study, we present a new benchmark low-dose CT dataset, LoDoInd, which consists of experimental low-dose CT images explicitly designed for industrial purposes. LoDoInd incorporates complex and diverse secondary filling objects within the same testing object, simulating real-world scenarios encountered in industrial settings. The dataset can be accessed at this Zenodo repository.

Building upon the foundation set by LoDoInd, we further investigate the efficacy of various post-processing methods in denoising tasks. Through a detailed comparative analysis of 2D, 2.5D, and 3D training, we demonstrate that 2.5D training strikes an optimal balance between performance and computational efficiency.

LoDoInd comprises five distinct low-dose CT reconstructions of a test object, each at different noise levels, along with a reference dataset. The image below showcases a sample slice across various noise levels and the reference:

• Conda should be installed on your system.

1. Clone this repository:

   git clone [email protected]:jiayangshi/LoDoInd.git
   cd LoDoInd

2. Create and activate a Conda environment:

   conda env create -f environment.yml
   conda activate denoise-ct

1. Data Preparation: Edit split_train_test.py to specify the noise level and the train/test data ratio.

2 Training and Testing: Modify the training file path in train.py and select the training mode ('2D', '2.5D', '3D').

• For 2D training, set mode to '2D':

    python train.py

• For 2.5D training, set mode to '2.5D' (stack size is adjustable, default is 5):

    python train.py

• For 3D training, set mode to '3D':

    python convert3h5.py
    python train.py