In this partial review and partial attempt at vision of what may be the future of dedicated brain PET scanners, the key implementations of the PET technique, we postulate that we are still on a development path and there is still a lot to be done in order to develop optimal brain imagers. Optimized for particular imaging tasks and protocols, and also mobile, that can be used outside the PET center, in addition to the expected improvements in sensitivity and resolution. For this multi-application concept to be more practical, flexible, adaptable designs are preferred. This task is greatly facilitated by the improved TOF performance that allows for more open, adjustable, limited angular coverage geometries without creating image artifacts. As achieving uniform very high resolution in the whole body is not practical due to technological limits and high costs, hybrid systems using a moderate-resolution total body scanner (such as J-PET) combined with a very high performing brain imager could be a very attractive approach. As well, as using magnification inserts in the total body or long-axial length imagers to visualize selected targets with higher resolution. In addition, multigamma imagers combining PET with Compton imaging should be developed to enable multitracer imaging.
The author is thankful to Professors Pawel Moskalik and Ewa Stępień for their invitation to contribute to this special edition of the journal to cover the PET brain imaging but the author wanted also to emphasize the common aspects of the next-generation PET imagers, that bring to focus the necessary pragmatic approaches like the J-PET concept.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Addendum 1: Limits of spatial resolution in PET
The basic idea of the next-generation PET systems is to take the spatial resolution of the system to its limit imposed by the physics of the PET process, the motion correction accuracy, and the statistical event limitations in creating high-resolution images composed of many small 3D pixels with high enough event statistics in each pixel to produce “good quality” images. The key physical contributing factors are: positron range and a-collinearity between the emission directions of the two 511 keV annihilation photons (Figure 22).
In addition, one needs to minimize the detector contribution by its proper design. In technical terms, it means to implement: (1) smallest possible physical size of the system, limiting the maximal distances between the opposed coincident radiation sensors detecting the two 511 keV annihilation photons from the coincidence pairs, and (2) high intrinsic spatial resolution of the detector modules, including the depth-of-interaction (DOI) measurement. And DOI resolution is especially important in compact systems.
Using the approximate theoretical formulas one can get an estimate that sub-mm resolution for a brain-sized or breast-sized imagers is possible. In addition, there is experimental evidence that implementing the above optimization strategy works in several successful implementations in small animal PET scanners, such as PawPET and few other [examples: 132], , . In these implementations, still not yet fully perfected, the reconstructed spatial resolution of 0.5–0.7 mm FWHM was achieved using F-18-based imaging agents.
Many papers dealt in the past with spatial resolution limits of the PET detectors [Example: 135]. An example of the empirical formula for the spatial resolution limit for a pixelated PET detector is:
where d is width of the pixel, D is the distance between the opposed coincidence detector modules in the contribution term expressing the a-collinearity of the two coincident annihilation photons, r is the positron range, and b is the blurring effect due to motion and the last term is due to parallax effect (p). 1.25 is the empirical factor related to the degradation of the point spread function (PSF) due to the non-uniform sampling of the LOR in the FOV and the image reconstruction process. This value is estimated assuming an analytical reconstruction algorithm such as the filtered back projection (FBP). From the formula, there are several key contributions to the reconstructed spatial resolution: physical processes (positron range and a-collinearity of the two annihilation photons), detector defined pixelation and DOI resolution, and also motion-induced error.
From the formula, an improvement can be achieved by making a more compact detector, using smaller-sized detector pixels, lowering the parallax effect, and avoiding blurring due to the motion. As formula provides, error due to the a-collinearity of the two annihilation photons increases with the distance between the modules. Therefore, closer structures are preferred, also from the sensitivity point of view, as well as due to smaller size, lower weight and reduced cost.
Below are shown two examples of the predictions from the above formula:
for a 25 cm diameter (tight brain size) detector and F18, 2 mm transversal detector resolution pixel size, and assuming no blurring due to motion or parallax effects, the resolution limit is 1.75 mm FWHM.
as in (1) but for 1 mm intrinsic detector resolution pixel this limit value changes to 1.19 mm FWHM.
In the paper by Shibuya et all, the authors calculated the non-collinearity contribution only: ”For example, the limit of PET spatial resolution are calculated to be 0.5 mm for a 10-cm diameter scanner, 1.2 mm for a 40-cm diameter scanner, and 2.1 mm for an 80-cm diameter scanner.” These results are consistent with the formula we use above. To achieve better resolution than formula predicts, modeling of the positron range and of the a-collinearity is expected to provide some improvement.
DOI contribution. The need for good DOI resolution is illustrated in the plots below showing calculated radial spatial resolution for several LSO crystal thickness designs (source: Johan Nuyts, Lowen, Belgium) (Figure 23).
In this compact brain scanner example, the inner diameter of 25 cm and 3 mm and 1.5 mm scintillation pixel sizes were assumed. DOI resolution in the 1–7 mm range was assumed. To maintain ∼1.5–2.00 mm resolution in the whole brain volume in such a tight detector structure, DOI resolution should be 2–3 mm. Many studies investigated how to achieve good DOI resolution [examples: 136], , .
In addition, it is important to take into account other contributing practical limiting to the usable spatial resolution, that are often underestimated, and are related to the brain position recording and motion correction through the duration of the scan, and the ability to apply these accurate time sensitive corrections to the recorded data stream, before the image reconstruction process. Patient comfort is an important contributing factor, especially during long scans during dynamic brain imaging sessions. It is critical in order for the high-resolution imaging protocols to be successful, to develop a robust, concurrent or complementary and yet partially overlapping motion correction system that can be used in different imaging situations. There is a lot of experimental evidence that excellent intrinsic spatial resolution cannot be realized as readily in clinical environment, unlike in the controlled phantom studies, if there is inadequate human motion correction.
A very important and often forgotten element of the spatial resolution discussion is the impact of the statistical power of the detected signal, limited by the injected doses, biological uptake of the particular imaging agent, sensitivity of the system, and timing of the imaging scan (when done and for how long). To benefit from the ∼1 mm range spatial resolution there has to be enough detected events during the scan to fill all the small 3D voxels in the reconstructed volumes and per each time bin (if dynamic analysis is performed). Insufficient number of events in the image voxels results in very noisy images, that cannot be utilized unless software filtering algorithms that add controlled image blurring, which is in fact negating the advantages of the intrinsically high spatial resolution. This argument can be also formulated as that achievable event statistics in fact sets the limit on the useful spatial resolution for the imaging tasks, also depending on the imaging agent.
High uptake of the specific imaging agents justifies the push for the best achievable detector performance, even if in the majority of cases the very high resolution will not be usable and the filtering of data in the processing phase, as mentioned above, will be equivalent to using system with moderate spatial resolution to start with.
However, as mentioned earlier, the practically achievable spatial resolution using the intrinsically high-resolution PET scanner, either in the clinic or even in the research center where better precautions can be taken, may still ultimately depend on how accurate is the motion correction of human subjects. Many novel approaches are investigated, with optical markerless techniques and using directly the PET scanner data leading, with sub-mm results reported , , , , , .
Addendum 2: The TOF advantage
PET is converting into TOF PET to benefit from the increased sensitivity and resolution. Many papers describe the progress and discuss the future , , , , , , . Just very recently, a paper was published showing first reconstructionless images obtained at 32 ps FWHM .
The variance reduction of a TOF-PET system over a non-TOF PET system can be computed analytically for the center of a uniform cylinder and, assuming D >> , and it equals:
, where D is the diameter of the cylinder and is the TOF spatial resolution. Therefore, the variance gain of one TOF-PET system over another is inversely proportional to the ratio of their TOF-resolutions. There is some confusion of the advantage that TOF offers due to the two different measures used to compare systems with different TOF performance. One measure is the “sensitivity”, proportional to the above-defined variance gain, and the second is the Signal-to-Noise ratio (SNR) gain, expressed as square root of the variance gain. The SNR measure is a more accurate description of the practical situation in detecting image features against the noise background in the reconstructed tomographic images. For comparing scanners, SNR is thought to be a more appropriate measure. Between 200 and 400 ps, the sensitivity gain is factor 2, and SNR gain is \sqrt(2) = 1.4. There is also an additional issue how SNR is defined. Usually for detection, SNR is defined as the ratio between contrast and background noise. While contrast is important for detection, it is more related to the tracer. Due to relatively small object size, in brain imaging, the advantage of TOF is not that large, and as discussed before, it comes at a high expense and may actually cause a net sensitivity decrease due to creation of additional mechanical issues, and resultant cracks in angular coverage.
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