Tissue-specific transformation model for CT-images

Model transformation languages for domain-specific workbenches

Domain-specific languages (DSLs) are software languages which are tailored to a specific application domain. DSLs enable domain experts to create domain-specific models, that is, high-level descriptions of domain knowledge. As any other software languages, DSLs rely on language tools which provide assistance for processing and managing domain-specific models. A domain-specific workbench is an i...

Model-View-Controller Architecture Specific Model Transformation

In this paper, we propose a model-driven development technique specific to the Model-View-Controller architecture domain. Even though a lot of application frameworks and source code generators are available for implementing this architecture, they do depend on implementation specific concepts, which take much effort to learn and use them. To address this issue, we define a UML profile to captur...

Tissue-Specific Sparse Deconvolution for Low-Dose CT Perfusion

Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra usually occur in cerebral per...

Domain - Specific Models , Model Analysis , Model Transformation

Tissue-specific sparse deconvolution for brain CT perfusion

Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting...