Noise2Inverse for 3D Low-Dose Cone-Beam Computed Tomography

This is the authors' abstract. We don't add key points for this paper.

Cone-beam computed tomography (CBCT) is a noninvasive x-ray imaging technique with multiple clinical applications (orthopedics, dentistry, radiation therapy, etc.). These systems can provide sub-millimeter resolution in images of high diagnostic quality with short scanning times. However, CBCT scans require subjecting the patient with a sufficient radiation dose to obtain a high-quality image. Although relatively low, some clinical applications such as image guidance in radiotherapy (IGRT) require obtaining CBCT daily. This repeated exposure to x-ray radiation outside the treatment volume can have detrimental effects and may increase the lifetime risk of a secondary malignancy in younger patients. Hence, there is a clinical need to reduce CBCT imaging dose. However, images produced with a reduced dose often have greater noise and artifacts which deteriorate CBCT image quality, reducing their clinical efficacy. Deep learning-based methods have become a popular choice showing impressive performance for noise reduction in low-dose CBCT. The success of these methods critically depends on the availability of paired images for training which is a major obstacle for most applications. Recently, the Noise2Inverse method was designed specifically for denoising computed tomography (CT) data without requiring paired noisy and clean images. While the Noise2Inverse method was shown to reduce noise in challenging real-world experimental datasets, it processes 3D-CT volumes as individual 2D images. In this work, we extend the Noise2Inverse method to work directly with 3D-CBCT volumes requiring the use of high-end HPC resources. We compare the results between a 2D and 3D UNet++ model utilizing the ICASSP-2024 3D Low Dose CBCT Grand Challenge dataset with CBCT data corresponding to two dose levels, clinical and low-dose. Experimental results show that both 2D and 3D models can significantly reduce the noise in CBCT increasing the SSIM up to 148.05% compared to the low-dose FDK reconstruction. Furthermore, the 3D model improves the SSIM by 0.26% and 1.46% for clinical- and low-dose, respectively, over the 2D model.