Very deep super-resolution for efficient cone-beam computed tomographic image restoration
황재준, 정연화, 조봉혜, 허민석,
소속 상세정보
황재준 ( Hwang Jae-Joon ) - Pusan National University School of Dentistry Department of Oral and Maxillofacial Radiology
정연화 ( Jung Yun-Hoa ) - Pusan National University School of Dentistry Department of Oral and Maxillofacial Radiology
조봉혜 ( Cho Bong-Hae ) - Pusan National University School of Dentistry Department of Oral and Maxillofacial Radiology
허민석 ( Heo Min-Suk ) - Seoul National University School of Dentistry Department of Oral and Maxillofacial Radiology
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 super-resolution (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.
키워드
Cone-Beam Computed Tomography; Data Compression; Radiographic Image Enhancement
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