Machine learning for diagnosis of coronary artery disease in computed tomography angiography:A survey

Coronary artery disease(CAD)has become a major illness endangering human health.It mainly manifests as atherosclerotic plaques,especially vulnerable plaques without obvious symptoms in the early stage.Once a rupture occurs,it will lead to severe coronary stenosis,which in turn may trigger a major adverse cardiovascular event.Computed tomography angiography(CTA)has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution,noninvasiveness,and three-dimensional imaging.However,manual examination of CTA images by radiologists has been proven to be tedious and time-consuming,which might also lead to intra-and interobserver errors.Nowadays,many machine learning algorithms have enabled the(semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images.This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images,including coronary artery extraction,coronary plaque detection,vulnerable plaque identification,and coronary stenosis assessment.Most included articles were published within this decade and are found in the Web of Science.We wish to give readers a glimpse of the current status,challenges,and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.

Rising role of artificial intelligence in image reconstruction for biomedical imaging

In this editorial,we review recent progress on the applications of artificial intelligence(AI)in image reconstruction for biomedical imaging.Because it abandons prior information of traditional artificial design and adopts a completely data-driven mode to obtain deeper prior information via learning,AI technology plays an increasingly important role in biomedical image reconstruction.The combination of AI technology and the biomedical image reconstruction method has become a hotspot in the field.Favoring AI,the performance of biomedical image reconstruction has been improved in terms of accuracy,resolution,imaging speed,etc.We specifically focus on how to use AI technology to improve the performance of biomedical image reconstruction,and propose possible future directions in this field.

Development of tomographic reconstruction for three-dimensional optical imaging:From the inversion of light propagation to artificial intelligence

Optical molecular tomography(OMT)is an imaging modality which uses an optical signal,especially near-infrared light,to reconstruct the three-dimensional information of the light source in biological tissue.With the advantages of being low-cost,noninvasive and having high sensitivity,OMT has been applied in preclinical and clinical research.However,due to its serious ill-posedness and illcondition,the solution of OMT requires heavy data analysis and the reconstruction quality is limited.Recently,the artificial intelligence(commonly known as AI)-based methods have been proposed to provide a different tool to solve the OMT problem.In this paper,we review the progress on OMT algorithms,from conventional methods to AI-based methods,and we also give a prospective towards future developments in this domain.