Unraveling the Science Behind Positron Emission Tomography (PET) Imaging
Positron Emission Tomography (PET) is a medical imaging technique used to produce images of the metabolic processes and functions of the body. It works by injecting a small amount of radioactive material into the body and then using a scanner to detect the gamma rays produced when the radioactive material decays. The raw data collected by a PET scanner is a list of “coincidence events” that represent near-simultaneous detections of annihilation photons by a pair of detectors. This data is then processed to create images that can be used to diagnose and monitor various medical conditions.
The first step in processing the raw data from a PET scanner is to correct for various sources of noise, such as scattered photons and random events. This involves applying various correction algorithms to the data to remove these sources of noise and improve the overall quality of the image.
The next step in the image reconstruction process is to group the coincidence events into projection images, called sinograms. Sinograms are sorted by the angle of each view and tilt (for 3D images), and they are similar to the projections captured by computed tomography (CT) scanners. They can be reconstructed in a similar way, but due to the much poorer quality of the data collected by PET scanners, the reconstruction process is more challenging.
Filtered back projection (FBP) is a commonly used algorithm for reconstructing images from the projections. While FBP is simple and has a low requirement for computing resources, it can result in prominent shot noise in the reconstructed images and streaks across the image in areas of high tracer uptake.
Statistical, likelihood-based iterative algorithms, such as the Shepp-Vardi algorithm, are now the preferred method of reconstruction. These algorithms compute an estimate of the likely distribution of annihilation events that led to the measured data, based on statistical principles. The advantage of these algorithms is a better noise profile and resistance to the streaks that are common with FBP. However, they require more computing resources.
Attenuation correction is a critical component of quantitative PET imaging. It is based on a transmission scan using a 68Ge rotating rod source and is used to correct for the differential attenuation of photons as they pass through different thicknesses of tissue. Attenuation correction is necessary for accurate quantification of the radioactivity distribution, but it can also result in artifacts that can impact the quality of the image.
There are two approaches to reconstructing data from modern PET scanners that have multiple rings of detectors: 2D reconstruction, which treats each ring as a separate entity, and 3D reconstruction, which allows for coincidences to be detected between rings. 3D reconstruction has better sensitivity and less noise but is more sensitive to scatter and random events and requires more computing resources.
Time-of-flight (TOF) PET is a technique that uses fast gamma-ray detectors and data processing systems to improve the overall performance of PET scans. By measuring the difference in time between the detection of the two photons, TOF PET can improve the signal-to-noise ratio and provide better image quality.
In conclusion, PET is a powerful medical imaging technique that requires careful processing and reconstruction of the raw data to produce accurate and informative images. The various algorithms and techniques used in the reconstruction process help to remove noise and artifacts, improve image quality, and provide more accurate information about the metabolic processes and functions of the body.
