In computed tomography (CT), reconstruction is the core computational process of converting hundreds or thousands of 2D transmission images, known as projection data, into a 3D volumetric dataset representing the internal structure of the scanned object.
During a CT scan, X-rays pass through the sample as it rotates, typically over a full 360 degrees. Each captured projection image is essentially a conventional X-ray radiograph—a "shadow image" where internal structures overlap. Determining the precise location and geometry of internal features from a single 2D image is impossible.
Reconstruction algorithms bridge this gap by processing projection images from every angle. Using advanced mathematical techniques, such as Filtered Back Projection (FBP) or the Feldkamp-Davis-Kress (FDK) algorithm, the system "back-projects" the filtered data into 3D space. This process effectively recreates the complex 3D structure based on shadow data from multiple viewpoints.
The result is a 3D volume composed of voxels (volumetric pixels). This volumetric data enables comprehensive analysis, including virtual cross-sectioning, 3D model rendering, and precise internal dimensional measurement.
Matsusada Precision integrates high-performance GPU (Graphics Processing Unit) parallel computing into its reconstruction software. This technology significantly accelerates processing speeds, allowing for rapid image generation and more efficient inspection workflows.
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