Clinical CBCT image evaluation chart.

Cone-beam computed tomography (CBCT) can provide 3D images of a targeted area with the advantage of lower dosage than multidetector computed tomography (MDCT; also simply referred to as CT). However, in CBCT, due to the cone-shaped geometry of the X-ray source and the absence of post-patient collimation, the presence of more scattering rays deteriorates the image quality compared with MDCT. CBCT is commonly used in dental clinics, and image artifacts negatively affect the radiology workflow and diagnosis. Studies have attempted to eliminate image artifacts and improve image quality; however, a vast majority of that work sacrificed structural details of the image. The current study presents a novel approach to reduce image artifacts while preserving details and sharpness in the original CBCT image for precise diagnostic purposes. We used MDCT images as reference high-quality images. Pairs of CBCT and MDCT scans were collected retrospectively at a university hospital, followed by co-registration between the CBCT and MDCT images. A contextual loss-optimized multi-planar 2.5D U-Net was proposed. Images corrected using this model were evaluated quantitatively and qualitatively by dental clinicians. The quantitative metrics showed superior quality in output images compared to the original CBCT. In the qualitative evaluation, the generated images presented significantly higher scores for artifacts, noise, resolution, and overall image quality. This proposed novel approach for noise and artifact reduction with sharpness preservation in CBCT suggests the potential of this method for diagnostic imaging.

锥形束计算机断层扫描（Cone-beam computed tomography, CBCT）可获取靶区域的三维图像，相较多排探测器计算机断层扫描（multidetector computed tomography, MDCT，亦简称为CT）具有辐射剂量更低的显著优势。然而，CBCT成像中由于X射线源呈锥形几何布局且缺乏患者后准直装置，相较于MDCT，散射射线占比更高，进而导致图像质量劣化。CBCT目前广泛应用于牙科临床场景，而图像伪影会对放射学工作流程与诊断工作造成负面影响。既往研究虽尝试通过消除图像伪影以提升图像质量，但绝大多数此类方法都会牺牲图像的结构细节。本研究提出了一种全新方法，可在保留原始CBCT图像细节与锐度的前提下减少图像伪影，以满足精准诊断需求。本研究以MDCT图像作为高质量参考图像，回顾性收集了某大学医院的CBCT与MDCT扫描对，并完成了两种图像的配准。随后提出了一种上下文损失优化的多平面2.5D U-Net模型，使用该模型校正后的图像由牙科临床医师开展定量与定性评估。定量评估结果显示，模型输出图像的质量优于原始CBCT图像；定性评估中，生成图像在伪影、噪声、分辨率及整体图像质量维度的评分均显著更高。本研究提出的这种可在CBCT图像中实现噪声与伪影抑制并保留锐度的全新方法，为诊断成像领域提供了潜在应用价值。