中心博士生赵麾宇同学的工作——Calcium deblooming in coronary computed tomography angiography via semantic-oriented generative adversarial network（由语义引导生成对抗网络实现冠状动脉CT的钙化伪影去除）的相关成果最近被Computerized Medical Imaging and Graphics期刊接收，该工作得到了国家自然科学基金（T2225023, 8225024）的部分资助。
钙化斑块在冠状动脉层析成像血管造影（CCTA）中会产生钙化伪影，这是放射科医生得到假阳性判断的始作俑者。过去的大多数研究都集中在降低CT图像的噪声上，而在面对钙化伪影时性能受到了很大的限制。
本文设计了一个以语义为导向的生成对抗网络，包含了一个语义特征提取模块、一个全局-局部融合模块、一个基于语义相似性的生成器和一个矩阵判别器。该网络利用钙化斑块作为CCTA中的语义区域。使用语义特征提取模块提取语义特征，并通过全局-局部融合模块，具有语义相似模块的生成器和矩阵判别器实现对语义特征的充分利用。设计方法的有效性在虚拟和临床数据集上得到了验证。临床数据集由上海市第六人民医院放射科提供的372个CCTA和相应的冠状动脉造影（CAG）结果组成，并在两名心脏放射学家（有10年和21年的经验）的帮助下进行临床评估。提出的方法有效地减少了三个主要冠状动脉的伪影，并显着提高了冠状动脉狭窄诊断的特异性和阳性预测价值。
摘要: Calcium blooming artifact produced by calcified plaque in coronary computed tomography angiography (CCTA) is a significant contributor to false-positive results for radiologists. Most previous research focused on general noise reduction of CT images, while performance was limited when facing the blooming artifact. To address this problem, we designed an automated and robust semantics-oriented adversarial network that fully exploits the calcified plaques as semantic regions in the CCTA. The semantic features were extracted using a feature extraction module and implemented through a global-local fusion module, a generator with a semantic similarity module, and a matrix discriminator. The effectiveness of our network was validated both on a virtual and a clinical dataset. The clinical dataset consists of 372 CCTA and corresponding coronary angiogram (CAG) results, with the assistance of two cardiac radiologists (with 10 and 21 years of experience) for clinical evaluation. The proposed method effectively reduces artifacts for three major coronary arteries and significantly improves the specificity and positive predictive value for the diagnosis of coronary stenosis.