From Projections to Images
A CT scanner rotates an X-ray source around the patient, recording attenuation profiles at hundreds of angles. Each profile is a line integral of the tissue's linear attenuation coefficient along the beam path. The mathematical challenge — recovering a 2D map from its 1D projections — was solved by Johann Radon in 1917 and implemented in hardware by Godfrey Hounsfield in 1971, earning him the Nobel Prize.
Filtered Back-Projection
Simple back-projection smears each projection back across the image, producing a blurred result. Applying a ramp filter in Fourier space before back-projection corrects for the 1/r blurring, yielding sharp cross-sectional images. This filtered back-projection (FBP) algorithm remains the workhorse of clinical CT, though iterative methods are gaining ground for dose reduction.
Hounsfield Units & Tissue Contrast
The Hounsfield scale normalises attenuation to water (0 HU) and air (−1000 HU). Fat sits near −100 HU, muscle at +40 HU, and cortical bone above +1000 HU. Iodinated contrast agents push vascular HU values to 200-400, enabling angiography. This simulation lets you see how changing μ shifts the HU reading and alters the reconstructed image contrast.
Resolution, Dose & Artifacts
More projections improve resolution but increase radiation dose. Under-sampling creates streak artifacts; metal implants cause beam hardening; patient motion blurs edges. Modern scanners balance these trade-offs with dual-energy acquisition, iterative reconstruction, and sub-second rotation. Adjusting parameters here reveals how each factor shapes the final diagnostic image.