Photon-counting computed tomography

In contrast, more conventional CT scanners use energy-integrating detectors (EIDs), where the total energy (generally from a large number of photons as well as electronic noise) deposited in a pixel during a fixed period of time is registered.

[2][3] Due to the large volumes and rates of data required (up to several hundred million photon interactions per mm2 and second[4]) the use of PCDs in CT scanners has become feasible only with recent improvements in detector technology.

Therefore, an energy threshold cannot be applied, making this technique susceptible to noise and other factors which can affect the linearity of the voltage to X-ray intensity relationship.

Each registered photon is thus assigned to a specific bin depending on its energy, such that each pixel measures a histogram of the incident X-ray spectrum.

It turns out such a material base decomposition, using at least two energy bins, can adequately account for all elements found in the body and increases the contrast between tissue types.

Many challenges are related to demands on detector material and electronics resulting from large data volumes and count rates.

Even before saturation, the detector functionality starts to deteriorate because of pulse pileup (see figure to the left), where two (or more) photon interactions take place in the same pixel too close in time to be resolved as discrete events.

Such quasi-coincident interactions lead to a loss of photon counts and distorts the pulse shape, skewing the recorded energy spectrum.

Using smaller image pixels decreases the per-pixel count rate and thus alleviates the demands on pulse resolving time at the expense of requiring more electronics.

The effects mentioned take place also in EIDs but cause additional problems in PCDs since they result in a distorted energy spectrum.

In contrast to saturation and pileup effects, problems caused by partial energy deposition and multiply interacting photons is aggravated by smaller pixel size.

This is typically performed by writing each pixel as a linear combination of M base materials of known properties such as water, calcium, and a contrast agent such as iodine.

Research in the field of deep learning has also introduced possibilities of performing material decomposition using convolutional neural networks.

[16] Experimental PCDs for use in CT systems use semiconductor detectors based on either cadmium (zinc) telluride or silicon, neither of which need cryogenic cooling to operate.

However, detectors made of Cadmium telluride (zinc) have longer collection times due to low charge carrier mobility, and thus suffer more from pileup effects.

[17] Silicon detectors, on the other hand, are more easily manufactured and less prone to pileup due to high charge carrier mobility.

Practical animation of signal generation in a PCD
An animated representation of the practical principles of PCCT detection. The left image depicts the arrival of photons at the surface of the PCD while the right image shows a simplified version of the generated signal. Some key things to learn from this image include: the discrete nature of photon detection, the energy-dependent height of electrical pulses, the ability to theoretically eliminate the effects of electronic noise by using a high enough base threshold, and the ability to determine the energy of a photon using energy thresholds.
Simplified illustration of pulse-pileup, one of the fundamental contributors to spectral distortion within PCDs. In this case, two photons that impact the detector at the same time or within a very small, indiscernible time window are recorded as a single high energy photon rather than as two lower energy photons. This creates an incorrect spectral reading.
A simplified illustration of charge-sharing, one of the fundamental contributors to spectral distortion within PCDs. An incident photon is identified as two individual photons of smaller energies rather than as a singular photon of the actual higher energy.