What Is X-ray Computed Tomography?

X-ray computed tomography (CT) is a medical and industrial imaging modality that reconstructs three-dimensional cross-sectional images of an object by combining multiple two-dimensional X-ray projection measurements taken from different angles. Unlike conventional projection radiography, which collapses a three-dimensional structure into a single planar image, CT preserves depth information by mathematically inverting the projection process, producing a volumetric dataset in which individual tissue types or material phases can be distinguished by their linear attenuation coefficients.

The technique was introduced clinically by Godfrey Hounsfield and Allan Cormack in the early 1970s, work that earned the 1979 Nobel Prize in Physiology or Medicine. CT draws on physics, detector engineering, signal processing, and computational mathematics, and it has expanded well beyond medicine into nondestructive industrial inspection and scientific materials characterization.

Image Acquisition and Scanner Design

A CT scanner consists of an X-ray tube and a detector array mounted in a rotating gantry, with the patient or specimen positioned on a motorized table that advances incrementally through the gantry. The X-ray tube rotates around the subject, emitting a fan- or cone-shaped beam that is attenuated along each path through the object before striking the detector. Modern multi-row detectors record data from multiple adjacent planes simultaneously, enabling helical CT in which the table translates continuously while the gantry rotates, dramatically reducing scan time compared to the original step-and-shoot approach. High-end clinical scanners operate at gantry rotation speeds of 0.27 seconds per revolution and use up to 320 detector rows to capture whole organs in a single rotation. As described in the NIBIB resource on computed tomography, this speed is critical for imaging moving structures such as the heart and for reducing motion artifacts.

Tube voltage, typically 80 to 140 kVp in clinical systems, controls the X-ray spectrum and therefore the contrast between tissues and materials with different atomic compositions. Dual-energy CT, which acquires data at two tube voltages or uses a photon-counting detector sensitive to photon energy, enables material decomposition and can distinguish iodine contrast agent from bone or calcium deposits without post-injection subtraction.

Reconstruction Algorithms

The mathematical problem of CT reconstruction is to recover a two-dimensional or three-dimensional function from its line integrals, a class of problem described by the Radon transform. The classical analytical solution, filtered back-projection (FBP), applies a ramp filter to each projection and sums the filtered projections back across all angles. FBP is computationally efficient and well-understood, and it remains widely used in clinical systems. As detailed in NCBI's treatment of CT mathematics and physics, iterative reconstruction methods offer improved noise reduction at lower dose by modeling the acquisition geometry and detector statistics explicitly, at the cost of substantially higher computation time. Deep learning-based reconstruction, in which convolutional neural networks trained on paired low-dose and full-dose datasets denoise or post-process CT images, has entered clinical practice at several major vendors and can achieve full-dose image quality from half-dose acquisitions.

Clinical and Industrial Uses

In medicine, CT is the primary modality for trauma assessment, cancer staging and treatment planning, cardiac evaluation, pulmonary embolism diagnosis, and virtual colonoscopy. Industrial CT, operating at higher tube voltages (up to 450 kV) to penetrate metal components, is used for internal inspection of turbine blades, castings, and electronic assemblies without disassembly, and for dimensional metrology of complex geometries. The range of applications is documented in a Nature Reviews Methods Primers overview of X-ray computed tomography, which covers both medical and materials science uses.

Applications

X-ray computed tomography has applications across a wide range of fields, including:

• Oncology staging, treatment planning, and response assessment
• Cardiac and vascular imaging with contrast enhancement
• Trauma and emergency medicine evaluation
• Nondestructive testing of aerospace and automotive components
• Additive manufacturing quality control and internal inspection
• Archaeological artifact imaging and paleontological specimen analysis