Enhancement & Texture Analysis

Enhancement

Enhancement is an innovative technology that uses AI to adjust factors such as dose, kernel, and the presence of contrast agents in medical imaging, ensuring patient safety and improving the accuracy of examinations.
By utilizing advanced algorithms, it enhances the quality of already acquired medical images, supporting medical professionals in conducting more accurate and effective analysis and diagnosis

- Image Neutralization

100 KVP 50 mAs100 KVP 200 mAs

Dose Neutralization

Delivers high-quality images with minimal radiation using AI

Dose Neutralization uses AI to deliver high-quality images with minimal radiation. This reduces noise and enhances clarity, making it ideal for frequent scans, especially in children. It improves diagnostic accuracy and patient care by addressing radiation safety.

Slice Thickness: 3mmSlice Thickness: 1mm

Slice Thickness Reduction

Generates high-resolution CT images without increasing radiation dose

Slice Thickness Reduction technology uses advanced algorithms to create thinner, higher-resolution CT images. This improves the visualization of small structures and subtle abnormalities, enhancing diagnostic accuracy without increasing radiation dose.

CT Kernel Neutralization

It is a deep learning method that enables accurate diagnosis and comparison between various machines and ensures high-quality CT images

CT Kernel Neutralization is a deep-learning based method to standardize and convert kernels from different vendors into a unified format, ensuring consistent, high-quality CT images. This facilitates accurate diagnoses and seamless comparisons across different machines, enhancing the interoperability and reliability of CT image analyses.

- Contrast Synthesis

This technique is safe and very useful
in pediatric patients

Create high-quality images
without the use
of contrast agents

Virtual Contrast (0% CA)Pre contrast

Contrast Synthesis

Create high-quality images without the use of contrast agents

Contrast Synthesis uses deep-learning algorithms to simulate contrast effects without actual agents, producing high-quality, enhanced images. This enables accurate diagnoses and detailed vascular visualization, enhancing patient safety and broadening CT imaging applicability.

- Virtual Quantitative Imaging

CT-based Fat Fraction mapping

Being able to evaluate several locations without a separate MRI search number saves time and money.

CT-based Fat Fraction mapping generates synthetic MRI Proton Density Fat Fraction (PDFF) images from CT scans. This technique assesses liver fat content without needing separate MRI scans, saving time and costs. It combines CT's structural detail with MRI's fat quantification, enhancing diagnostic efficiency and patient convenience, especially for conditions like NAFLD.

- PET-CT Registration

Data Measurement Accuracy

Through ‘Registration’ of PET and CT images, you can find the exact location of the target you want to identify from CT images received.

Texture Analysis

By quantifying the texture of the segmented area, complex information from medical images get analyzed.
The data postulated can be used to find prognosis under various conditions.