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How Does 3D Image Processing Work?

Acquired raw data from CT or MRI scanners must first be converted into tomography images via a reconstruction process, such that the images can be interpreted and understood. This is typically achieved within software accompanying the scanning device. The result, whether from CT or MRI is a 3D bitmap of greyscale intensities, consisting of a voxel (3D pixels) grid. In a CT scan, the greyscale intensity at a particular voxel relates to the X-ray absorption by the subject at that location (loosely the subject¡¯s density), while from MRI machines, it relates to the strength of signal emitted by proton particles during relaxation, post application of very strong magnetic fields - different tissues have different concentrations of these protons, thus different greyscale intensities arise in the image.

The reconstructed image volume serves as the typical input for 3D image processing, where the aim is usually to distinguish regions of interest within the image and build a digital 3D model of the structures ¨C This process is known as image segmentation and can involve varied approaches, depending on the subject, objectives and limitations of image quality. In ²ÝÁñÉçÇø Simpleware¡¯s 3D image processing software, for example, users can:

• Remove or reduce unwanted noise or artefacts from the images through image filtering, and crop or resample data to increase processing ease and efficiency.
• Carry out image segmentation using a range of efficient methods including highly automated and user-guided processes.
• Measure or statistically analyse resulting model volumes to quantify geometries.
• Introduce CAD components to model interactions with complex image-based models.
• Export to a range of formats for further simulation and/or design work, or for additive manufacture.
  

Go Beyond 3D Image Processing

Additional modules offer complimentary workflow-specific solutions. The flexibility of the tools available is arguably its strongest asset, allowing valuable models to be generated even from challenging images, and suitable model exports to go beyond 3D image processing into new realms of computer-aided decision making:

• Exporting STL data for additive manufacture of the prepared models.
• Generating a volume mesh for physics-based simulations such as Finite Element or Computational Fluid Dynamics.
• Exporting CAD-friendly NURBS files for further design work.
• Combining image data with CAD files to observe and plan component interactions with imaged subjects
• Calculating effective material properties of a material microstructure using FE-based homogenization
• Automate common segmentation and landmarking tasks with AI-enabled tools