KAHIBARO
33.4. Detector Array
Table of Contents
Detector pixels
In a CT or general X‑ray simulation, the detector array is modeled as a grid of individual pixels. Each pixel acts as a small, independent detector that counts or measures the intensity of transmitted photons. In GATE this is usually implemented as a repeated volume pattern that forms a flat panel or curved arc facing the X‑ray source.
To describe a detector array in a GATE simulation, you specify the overall detector plane geometry, then define a basic pixel volume and repeat it in the horizontal and vertical directions. The pixel volume is typically a box with a defined width, height, and thickness, and is filled with an appropriate detector material such as a scintillator layer or a semiconductor. The pixel pitch is the distance from the center of one pixel to the center of the next, and it may include a dead region or gap between neighboring pixels. This pitch together with the number of pixels in each direction determines the field of view and the spatial sampling of your CT system.
The spatial resolution of the CT projection data is directly linked to the pixel size and geometry. Smaller pixels can resolve finer structures, but they increase the number of channels which can raise memory use and output size. When you configure the array, you must balance the clinical or research resolution requirements against computational cost. In addition, the relative position of the detector array to the source and the isocenter affects the magnification and effective sampling of the patient or phantom. In a typical CT geometry, the detector pixels lie on a circular arc opposite the rotating X‑ray source, so that for each projection angle the line between the source and a pixel defines a sampling ray through the object.
The material choice for the pixel volume controls how efficiently the detector converts incident X‑ray photons into measurable signals. Scintillator materials coupled to photodiodes and direct conversion semiconductors are modeled differently at a detailed physics level, but at the geometric level both appear as arrays of small sensitive cells. You can also include non sensitive layers such as support structures or protective windows as additional volumes in front of or behind the active pixel layer when you need a more realistic model.
In GATE, each pixel is usually given a unique identifier through the volume hierarchy, so that you can later relate a detected signal to the correct pixel position. This mapping from volume index to pixel coordinates is essential when you reconstruct projection images and sinograms from simulation data. By carefully defining the pixel grid, pitch, and material, you lay the foundation for realistic CT detector modeling and for accurate simulation of spatial resolution and geometric sampling effects.
Detector response
Once the detector pixels are defined geometrically, you must describe how they respond to incident X‑ray photons. The detector response connects the underlying particle interactions in each pixel to the measured signal used for image reconstruction. In a GATE CT simulation this response is typically modeled with hits recorded in the detector material, followed by digitization steps that transform deposited energy into pixel values.
At the physics level, each X‑ray photon that reaches the detector may interact through photoelectric absorption or Compton scattering in the pixel material. Every interaction deposits some energy, and the total energy deposited in a pixel during one event is related to the signal that would be produced by that pixel in a real scanner. The simplest detector response model treats the sum of deposited energies in a pixel as directly proportional to the output signal. You obtain this by accumulating hit energies per pixel and per event, then using the resulting values to form the projection data.
Real detector systems are not ideal, so GATE includes digitizer components that introduce realistic effects such as energy blurring, electronic noise, and dead thresholds. You can apply an energy resolution model so that the measured energy in a pixel is not exactly equal to the true deposited energy, but is instead sampled from a Gaussian with a given full width at half maximum. This mimics the finite energy resolution of scintillation or semiconductor detectors and is important if you want to simulate spectral CT or study the effect of beam hardening and scatter rejection.
Detector efficiency is another key aspect of the response. Not every photon incident on the pixel plane will produce a detectable signal. Some photons may pass through without interaction, especially at higher energies or for thin detectors. Others may interact in non sensitive layers or in the detector housing. In your simulation, this appears naturally through the transport physics. Only interactions within the active pixel material contribute to the signal, so the overall efficiency is determined by pixel thickness, material composition, and photon energy. You can analyze this efficiency by comparing the number of photons reaching the detector plane to the number that generate energy deposition in the pixels.
Scattered radiation within the detector or from surrounding materials can also influence the detector response. Photons that undergo Compton scattering in the detector may deposit energy in more than one pixel, or deposit less energy than the primary photon energy, which leads to signal spreading and low energy tails in the detected spectrum. For CT simulation, these effects contribute to noise and artifacts in the projection data. You can decide how detailed your model should be. For high level system studies you may simply integrate total deposited energy per pixel, while for more detailed detector studies you might analyze individual hits and model light sharing or charge collection effects.
After digitization, you typically apply thresholds to reject very low signals that would be buried in electronic noise, and you may set an upper cut to exclude unphysical values. The remaining signals are then mapped to pixel indices to construct 2D projection images for each projection angle. The quality of these images depends strongly on how realistically you model the detector response. If your goal is accurate CT performance evaluation, you should include at least basic energy blurring and thresholds, and ensure that the detector material, thickness, and geometry resemble the intended system.
For CT and X‑ray simulations, the detector response is driven by three essential ingredients: realistic detector material and thickness, correct accumulation of deposited energy per pixel, and appropriate digitization that includes resolution and thresholds. An incorrect detector response model can produce projection data that look plausible but yield inaccurate estimates of noise, contrast, or dose.
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