■传统的漫射光断层扫描(DOT)重建受到诸如DOT源更接近浅层病变等因素引起的图像伪影的阻碍,不良的光电-组织耦合,组织异质性,和大的高对比度病变缺乏信息在更深的区域(称为阴影效应)。解决这些挑战对于提高DOT图像的质量和获得可靠的病变诊断至关重要。
■我们通过引入基于注意力的U-Net(APU-Net)模型来解决当前DOT成像重建的局限性,以增强DOT重建的图像质量,最终提高病变诊断的准确性。
■我们设计了一个APU-Net模型,其中包含上下文变压器注意力模块,以增强DOT重建。模型是在模拟和幻影数据上训练的,专注于诸如伪影引起的变形和病变阴影效应等挑战。然后通过临床数据评估模型。
■从模拟和幻像数据过渡到临床患者数据,我们的APU-Net模型有效地减少了伪影,平均伪影对比度降低了26.83%,并改善了图像质量。此外,统计分析显示,深度剖面的对比度显着改善,第二和第三目标层的平均对比度增加了20.28%和45.31%,分别。这些结果强调了我们的方法在乳腺癌诊断中的功效。
■APU-Net模型通过减少DOT图像伪影并改善目标深度轮廓来提高DOT重建的图像质量。
UNASSIGNED: Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis.
UNASSIGNED: We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy.
UNASSIGNED: We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data.
UNASSIGNED: Transitioning from simulation and phantom data to clinical patients\' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis.
UNASSIGNED: The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.