Detectors in positron emission tomography

Positron emission tomography is a highly sensitive molecular imaging modality, based on the coincident detection of annihilation photons after positron decay. The most used detector is based on dense, fast, and luminous scintillators read out by light sensors. This review covers the various detector concepts for clinical and preclinical systems.

Timing, Energy, and 3-D Spatial Resolution of the BING PET Detector Module

We evaluated the 3D spatial, energy, and timing resolution of the Brain (or Breast)-Initiative Next-Generation (BING) PET detector. The BING detector is an array of 1-mm-thick slats of LYSO scintillator with lapped specular-reflective faces (15-mm by 52-mm) that are stacked together and oriented with their long-narrow edges normal to the imaging field of view. Interaction positions are determined from the signals of silicon-photomultiplier (SiPM) arrays placed on the entrance (top) and exit (bottom) faces. The SiPM arrays are offset to determine the slat of interaction (SOI) without requiring any optical light sharing between slats. Maximum likelihood 2D location within the SOI is determined using the sensor signals. Interaction time is determined with a modified first-optical-photon pickoff method. Performance of the BING detector was measured as a function of position using a sideways coincidence-collimated beam. Slats were accurately identified, with an effective tangential detector resolution of 1 mm. Average resolutions (and ranges) are: 0.96 mm (0.85 mm to 1.11 mm) for lateral (axial) detector resolution, 1.6 mm (1.0 mm to 2.1 mm) for depth resolution, 13.6% (12.7% to 16.0%) for energy resolution, and 317 ps (241 ps to 404 ps) for coincidence timing resolution. Initial spatial and timing resolution results demonstrated that the BING detector can be effective in a small field-of view (e.g., brain or breast) PET system.

Fast γ Photon Imaging for Inner Surface Defects Detecting

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.

Reducing Calibration Time in PET Systems Based on Monolithic Crystals

In the past years, the gamma-ray detector designs based on the monolithic crystals have demonstrated to be excellent candidates for the design of high-performance PET systems. The monolithic crystals allow to achieve the intrinsic detector resolutions well below state-of-the-art; to increase packing fraction thus, increasing the system sensitivity; and to improve lesion detectability at the edges of the scanner field of view (FOV) because of their intrinsic depth of interaction (DOI) capabilities. The bottleneck to translate to the clinical PET systems based on a large number of monolithic detectors is eventually the requirement of mechanically complex and time-consuming calibration processes. To mitigate this drawback, several methods have been already proposed, such as using non-physically collimated radioactive sources or implementing the neuronal networks (NN) algorithms trained with simulated data. In this work, we aimed to simplify and fasten a calibration process of the monolithic based systems. The Normal procedure consists of individually acquiring a 11 × 11 ²²Na source array for all the detectors composing the PET system and obtaining the calibration map for each module using a method based on the Voronoi diagrams. Two reducing time methodologies are presented: (i) TEST1, where the calibration map of one detector is estimated and shared among all others, and (ii) TEST2, where the calibration map is slightly modified for each module as a function of their detector uniformity map. The experimental data from a dedicated prostate PET system was used to compare the standard calibration procedure with both the proposed methods. A greater similarity was exhibited between the TEST2 methodology and the Normal procedure; obtaining spatial resolution variances within 0.1 mm error bars and count rate deviations as small as 0.2%. Moreover, the negligible reconstructed image differences (13% deviation at most in the contrast-to-noise ratio) and almost identical contrast values were reported. Therefore, this proposed method allows us to calibrate the PET systems based on the monolithic crystals reducing the calibration time by approximately 80% compared with the Normal procedure.

Artificial neural networks for positioning of gamma interactions in monolithic PET detectors

To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, monolithic detectors are investigated as an alternative to pixelated detectors due to increased sensitivity, resolution and intrinsic DOI encoding. Monolithic detectors, however, are challenging because of complicated calibration setups and edge effects. In this work, we evaluate the use of neural networks to estimate the 3D first (Compton or photoelectric) interaction position. Using optical simulation data of a 50×50×16 mm³LYSO crystal, performance is evaluated as a function of network complexity (two to five hidden layers with 64 to 1024 neurons) and amount of training data (1000 to 8000 training events per calibration position). We identify and address the potential pitfall of overfitting on the training grid through evaluation on intermediate positions that are not in the training set. Additionally, the performance of neural networks is directly compared with nearest neighbour positioning. Optimal performance was achieved with a network containing three hidden layers of 256 neurons trained on 1000 events/position. For more complex networks, the performance degrades at intermediate positions and overfitting starts to occur. A median 3D positioning error of 0.77 mm and a 2D FWHM of 0.46 mm is obtained. This is a 17% improvement in terms of FWHM compared to the nearest neighbour algorithm. Evaluation only on events that are not Compton scattered results in a 3D positioning error of 0.40 mm and 2D FWHM of 0.42 mm. This reveals that Compton scatter results in a considerable increase of 93% in positioning error. This study demonstrates that very good spatial resolutions can be achieved with neural networks, superior to nearest neighbour positioning. However, potential overfitting on the training grid should be carefully evaluated.