Advances in time-of-flight PET

Abstract

This paper provides a review and an update on time-of-flight PET imaging with a focus on PET instrumentation, ranging from hardware design to software algorithms. We first present a short introduction to PET, followed by a description of TOF PET imaging and its history from the early days. Next, we introduce the current state-of-art in TOF PET technology and briefly summarize the benefits of TOF PET imaging. This is followed by a discussion of the various technological advancements in hardware (scintillators, photo-sensors, electronics) and software (image reconstruction) that have led to the current widespread use of TOF PET technology, and future developments that have the potential for further improvements in the TOF imaging performance. We conclude with a discussion of some new research areas that have opened up in PET imaging as a result of having good system timing resolution, ranging from new algorithms for attenuation correction, through efficient system calibration techniques, to potential for new PET system designs.

Keywords: Image quality; Image reconstruction; PET; Photo-sensors; Scintillators; Time-of-flight PET.

Figure 1

(A) Annihilation point occurring at a distance d from the scanner center within an object of diameter D. The coincident 511 keV photons are detected at times t₁ and t₂ in the PET scanner. (B) With poor timing resolution in a Non-TOF scanner, a uniform location probability along the LOR within the object is assumed for each annihilation point, leading to noise correlations over a portion of image space following reconstruction. (C) With improved timing resolution in a TOF scanner, the position of the annihilation event is localized along the LOR with a precision that is defined by a Gaussian distribution of width Δx. (D) Improved localization of the two annihilation events along their individual LORs leads to reduced noise correlation (or no noise correlation, as shown here for single LORs) of the events in image space.

Figure 2

Reconstructed transverse slices of a clinical ¹⁸F-FDG study. As indicated, images are shown for Non-TOF and TOF reconstruction and for iterations 3 and 10 of the reconstruction algorithm. The arrow indicates the lesion for which an accurate SUV is measured after 3 iterations of the TOF reconstruction algorithm.

Figure 3

(A) Reconstructed coronal slices of an ¹⁸F-FDG study for an average size (83 kg) patient with a history of metastatic small cell lung cancer. The images shown are (left) Non-TOF reconstruction of all collected counts, (middle) TOF reconstruction of all collected counts, and (right) TOF reconstruction of 1/3 of the collected counts. (B) Reconstructed coronal slices of an ¹⁸F-FDG study for a heavy (140 kg) patient diagnosed with non-Hodgkins lymphoma. The images are (left) Non-TOF reconstruction and (right) TOF reconstruction using all collected counts. Arrows indicate a lesion that has higher uptake and is better discriminated in the TOF image.

Figure 4

Reconstructed images from a NEMA image quality phantom using full or partial angular data acquired on a clinical TOF PET/CT. The six hot spheres in a ring have diameters of 37, 28, 22, 17, 13, and 10 mm and have an activity uptake of 9.7:1 with respect to background. The central cold region is a lung insert.