Iterative processing utilizing AI technology to enhance visibility for low dose CT images
The technology controls image quality based on a statistical model, an object model and a physical model using iterative processing.
This technology provides a high visibility image even at a low radiation dose , by maintaining the original texture of the image at a high denoise condition.
It brings both “high visibility at low dose” and “original texture at high noise reduction”
This technology provides a high visibility image even at a low radiation dose , by maintaining the original texture of the image at a high denoise condition.
It brings both “high visibility at low dose” and “original texture at high noise reduction”
Image EnhancementCTITRadiologyCardiologyRespiratoryOrthopedics
Virtual thin slice generation technology
The technology virtually generates thin slices from thick slices. It can be applied to the whole body, useful for utilizing past data. It brings high visibility in the VR display and reconstructed sagittal/coronal images.
Image EnhancementCTITRadiologyCardiologyRespiratoryGastroenterologyOrthopedics
Lung analysis
AI technology to automatically extract lung fields, 5 lobes, and bronchus, surrounding vessels from CT images.
From each extraction result, the percentage of low attenuation area is calculated.
These are expected to contribute the diagnosis of COPD.
From each extraction result, the percentage of low attenuation area is calculated.
These are expected to contribute the diagnosis of COPD.
Anatomy SegmentationCTITRadiologyRespiratory
Lung labelling
AI technology to subdivide lung into 10 segments in right lung, 8 segments in left lung from CT images. It can be used for confirming the position of chest nodules detected by a doctor.
Anatomy SegmentationCTITRadiologyRespiratory
COVID CAD
AI technology to identify suspicious region with COVID-19 related findings from CT images. This technology will help doctors diagnose efficiently.
Detection/DiagnosisCTITRadiologyRespiratory
Lung nodule CAD
AI technology to detect and quantify suspicious lesion from CT Images.
This will assit prevention of overlooking of nodule and generation of language of findings for radiology report.
This will assit prevention of overlooking of nodule and generation of language of findings for radiology report.
Detection/DiagnosisCTITRadiologyRespiratory
Interstitial lung disease classification
This technology identifies various findings of interstitial pneumonia that appear on CT images, such as consolidation, reticular pattern, ground glass opacity and honeycomb, and calculates their distribution and volume.
This will assist in the diagnosis of the severity and therapeutic efficacy of interstitial pneumonia, which are conventionally performed qualitatively, by providing a quantitative value for assessment.
This will assist in the diagnosis of the severity and therapeutic efficacy of interstitial pneumonia, which are conventionally performed qualitatively, by providing a quantitative value for assessment.
Detection/DiagnosisCTITRadiologyRespiratory
Chest X-ray CAD
The technology detects three types of imaging findings: nodule, consolidation, and pneumothorax from chest X-ray images. It is expected to contribute to preventing oversights in various chest X-ray examinations, such as health checkups and routine medical examinations.
Detection/DiagnosisITRadiologyRespiratory
Quantification of high absorption ROI in lung field
The technology estimates high-value threshold of the region of interest, and quantify high intensity area in lung field. For example, it is expected to offer information for quantitative analysis of partially solid nodules.
Detection/DiagnosisCTITRadiologyRespiratory