DEVELOPMENTS OF NEW PET-CT TECHNOLOGY

There have been many technological advances made in commercially available, clinical PET-CT systems in recent years. Some of these advances are beyond the scope of this thesis; for example, the use of solid state photodetectors instead of PMTs [79], [80] and regularised reconstruction algorithms that incorporate penalty terms to suppress noise while reaching convergence [81]. However, there are two major developments that are of particular interest to this thesis: Time of Flight imaging and Point Spread Function modelling.

1.7.1 Time of Flight

In conventional PET imaging, a LOR contains no positional information for location of the annihilation event along the line: the event may have occurred at any point along the LOR. Time of Flight (TOF) techniques attempt to localise the annihilation event along the LOR using the difference in photon arrival times at the detectors [47], [59]. The difference in photon arrival times must be measured accurately, and therefore TOF-PET requires better timing resolution than non-TOF PET. Fast crystals, such as LSO, and faster electronics have enabled TOF to be used clinically in recent years. Figure 1.7 illustrates the principle of TOF-PET.

Figure 1.7: Principles of Time of Flight

An annihilation event originates at a distance Δd from the midpoint between two detectors, which are separated by distance D (d1 + d2). Photon 1 (red) travels distance d1 before being detected by Detector 1 and Photon 2 (green) travels distance d2 before being detected by Detector 2. Photon 1 therefore travels 2Δd further than Photon 2. If the difference in arrival times (Δt) can be measured, the distance Δd can be calculated:

Equation 1.2

With a fast enough detection system, TOF-PET could theoretically localise annihilation events to within a single image voxel: timing resolution of 10ps could isolate events to within a 3mm voxel. However, limitations in timing resolution introduce blurring in the estimation of Δd [59], and hence uncertainty of the position of the annihilation event. Modern LSO crystal-based PET scanners are currently capable of timing resolutions of between 500ps and 600ps [82], [83], giving a positional uncertainty of between 7.5cm and 9cm.

TOF restricts the backprojection of an acquired event to a small segment of the LOR, instead of blurring the event over the entire LOR, as illustrated in Figure 1.8. This reduces the statistical noise in the reconstructed image if the LOR segment is shorter than the size of the emission source [27]. The benefits of TOF-PET are increased Signal to Noise Ratios

2

t

c

d

=

D

(SNR), lower random coincidence rates and the ability to handle higher count rates [84]. TOF is of particular benefit when imaging larger patients, whose increased attenuation properties adversely affect image quality when conventional PET is used. TOF information can be used to improve image quality with standard acquisition times, or to achieve the same image quality with reduced acquisition times [85].

Figure 1.8: Positional uncertainty improved by TOF

The use of TOF means timing information is applied to each correction step within the iterative reconstruction loop (e.g. normalisation, randoms, deadtime, scatter, attenuation). As a result, TOF sinogram datasets are approximately 60 times larger than those containing non-TOF data [47]. The use of TOF data in clinical PET therefore requires significant storage space and processing power compared to non-TOF PET.

1.7.2 Point Spread Function Modelling

Techniques to correct for limitations in spatial resolution arising from the emission/detection process have been developed in recent years. A model of the system’s PSF can be used by the reconstruction algorithm for such corrections. This requires knowledge of the PSF at every point throughout the FOV, as PSF is spatially dependent. The PSF model can be constructed using analytical calculations, Monte Carlo simulations or experimental measurement using point sources [19], [49]. The model can be applied in image space [86], [87], but is more commonly applied in sinogram space [49], [88] by incorporating PSF information into the system matrix. Such corrections should improve the uniformity of spatial resolution throughout the FOV [89] and reduce partial volume effects [37].

A PSF system matrix relates each voxel to more LORs than a non-PSF system matrix. Consequently, the reconstruction problem becomes even more ill-posed and requires more

iterations to reach convergence [90] [91]. Furthermore, neighbouring voxels in PSF- corrected images demonstrate greater correlations than uncorrected images, which changes the noise texture of the images [49], [92], [93]. At early iterations the benefit of PSF modelling is noise reduction, and not increased resolution [94].

A significant disadvantage of PSF correction is the introduction of artefactual edge enhancements known as Gibbs artefacts, visualised at the borders of tissues with large differences in intensity. Such artefacts may be caused by imperfections in the system matrix [95] and/or recovery of frequencies attenuated by the initial blurring process [71]. High frequency data lost in the initial detector blurring process cannot be recovered by PSF correction; however, the amplitude of the retained frequency content can be corrected. This produces a ‘deblurred’ PSF, which is closer to the ideal point source response, and improves the resolution of the reconstructed images. However, a steep cut-off in the frequency domain creates oscillating tails (ringing artefacts) in the spatial domain, as shown in Figure 1.9.

Figure 1.9: PSF correction with steep frequency domain cut-off

The effect of Gibbs artefacts is dependent upon the size of the lesion being considered. Enhanced edges may cause lesions to have apparent reduced activity at their centre. Sufficiently small lesions may have overestimated activity concentrations, caused by the summation of edge artefacts from opposing lesion boundaries [37]. Post-reconstruction filtering can mitigate these artefacts; however, care must be taken when selecting the filter width, as excessive smoothing will negate any advantage gained from PSF modelling.