Imaging Sci Dent.  2020 Dec;50(4):331-337. 10.5624/isd.2020.50.4.331.

Very deep super-resolution for efficient cone-beam computed tomographic image restoration

• Hwang JJ
• Jung YH
• Cho BH
• Heo MS

• KMID: 2527034
• DOI: http://doi.org/10.5624/isd.2020.50.4.331

Abstract

Purpose
As cone-beam computed tomography (CBCT) has become the most widely used 3-dimensional (3D) imaging modality in the dental field, storage space and costs for large-capacity data have become an important issue. Therefore, if 3D data can be stored at a clinically acceptable compression rate, the burden in terms of storage space and cost can be reduced and data can be managed more efficiently. In this study, a deep learning network for super-resolution was tested to restore compressed virtual CBCT images.
Materials and Methods
Virtual CBCT image data were created with a publicly available online dataset (CQ500) of multidetector computed tomography images using CBCT reconstruction software (TIGRE). A very deep superresolution (VDSR) network was trained to restore high-resolution virtual CBCT images from the low-resolution virtual CBCT images.
Results
The images reconstructed by VDSR showed better image quality than bicubic interpolation in restored images at various scale ratios. The highest scale ratio with clinically acceptable reconstruction accuracy using VDSR was 2.1.
Conclusion
VDSR showed promising restoration accuracy in this study. In the future, it will be necessary to experiment with new deep learning algorithms and large-scale data for clinical application of this technology.

Keyword

Cone-Beam Computed Tomography; Data Compression; Radiographic Image Enhancement