Quantitative PET in the 2020s: a roadmap

Abstract

Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.

Figure 1.

Pictorial overview of the major PET functional domains investigated over the past four decades and cellular sources for the corresponding targets in the brain (based on figure 2 from [12]).

Figure 2.

The schematic on the left illustrates how lesions in the spinal cord or peripheral nerves can cause remote expression and imaging signals, here exemplified by the expression of mitochondrial TPSO using [¹¹C](R) PK11195-PET. Signals can be expected along the entire tract of the injured first neuron, via the ascending pathways of the second neuron and trans-synaptically at the site of the input-receiving third neuron. [¹¹C](R)PK11195-PET shows a patient with secondary progressive multiple sclerosis in whom all three levels of the neuronal connection between peripheral nerve, spinal cord and brain are expected to be altered by the disease [13]. Indeed, a small stretch of a larger neural pathway is discernible in the thalamus with contralateral continuation through the brainstem into the spinal cord which lies outside the field of view.

Figure 1.

(Left panel) Serial FDG PET/CT in a patient with diffuse bone metastases shows a dramatic decrease in FDG uptake in response to endocrine therapy. (Right panel) Measures of the change in FDG uptake with therapy, quantified and categorized according to PERCIST criteria, predict Progression Free Survival (left) and Time to Skeletal Related Event (middle), with a trend for predicting Overall Survival (right) in a group of patients undergoing treatment for metastatic breast cancer. (Right panel taken from [26]).

Figure 2.

(Left panel) Coronal PET images of ¹⁸F-fluoroestradiol (FES) uptake (left) and FDG uptake before (middle) and after (right) endocrine therapy are shown for two patients with metastatic disease from estrogen receptor positive (ER+) breast cancers. Patient 1 showed metastases (solid arrow) that were metabolically active by FDG PET with matched uptake of FES, indicating preserved ER expression. Patient 2 showed a site of bone metastasis by FDG PET (solid arrow) but no corresponding uptake by FES, suggesting a loss of ER expression. Patient 1 had an excellent objective response while Patient 2 had disease progression, as indicated by changes in the post therapy FDG scans. Normal liver (dashed arrows) and kidney uptake (dotted arrows) is also seen in the images for both radiopharmaceuticals. (Right panel) Quantitative analysis of FES PET showed that no patient with tumour SUV < 1.5, indicative of loss of ER expression, had an objective response (R) to endocrine therapy. (adapted from [30]).

Figure 1:

Generation and regulation of anti-tumour immunity-showing T-cell activation. From [38].

Figure 2:

Illustrates the opportunity Total Body PET provides for simultaneously recording whole body regional kinetics of an administered tracer. This includes the means to non-invasively record high quality arterial input functions from within the aorta used to derive kinetic modelling based, whole body parametric functional images.

Figure 1.

Simultaneous whole-body PET/MR systems from Siemens, GE, and United Imaging (The middle and right pictures were downloaded from GE and United Imaging’s web sites).

Figure 2.

Short-axis and horizontal long-axis ¹⁸F-FDG consumption rates (K_i) slices obtained with (MC) and without (NMC) MR-based PET motion correction for a human ¹⁸F-FDG-PET/MR study. MC yielded higher K_i values than NMC, especially in regions indicated by the white arrows. Structures such as papillary muscles are also easier to delineate in MC K_i maps (see red arrows).

Figure 1.

Cramér–Rao Lower Bound [80] calculations for CTR in a 2×2×3 mm3 LSO:Ce,Ca(0.4%) crystal for varying SiPM SPTR and the number of ‘prompt’ Cerenkov photons produced in the crystal [81].

Figure 1.

Concept of total-body PET with complete coverage of the human body approaching maximum geometric collection efficiency in comparison to conventional PET scanners which have an axial coverage of between 20 and 30 cm. (Reproduced with permission from [40]).

Figure 2.

Dynamic total-body PET images, each collected over just 100 milliseconds, showing the distribution of ¹⁸F-fluorodeoxyglucose through the vasculature shortly after injection. Changes between systole and diastole are apparent. Times indicated on the bottom are the time after initiation of bolus injection.

Figure 1.

Representative application-specific systems and their detector arrangements (a), and schematic illustrations of effect of depth-of-interaction (DOI) measurement and time-of-flight (TOF) measurement on localization accuracy (b).

Figure 2.

A whole gamma imaging (WGI) prototype, which is a PET system combined with a Compton camera (a). In a mouse imaging demonstration (1-hour measurement started 22 hours after 9.8 MBq ⁸⁹Zr injection), the Compton image of 909 keV gamma rays was comparative to that of a PET image (b).

Figure 1.

Coronal slices of In-phase Dixon MR (a), MR-based attenuation image using standard tissue segmentation (b), and attenuation images from TOF joint estimation without and with MR-based prior (c) and (d), respectively. Note that a metal hip implant caused a huge signal void in (a) that translated into (b). The shape and higher attenuation of that implant are nicely recovered in (c) and (d) leading to more correct local attenuation. Reprinted (part of Fig. 2) from [101].

Figure 1.

Left panel: Parametric images of F18-altanserin binding potential derived from dynamic PET data [101]. Top row: Without frame-to-frame motion correction. Lower row: With frame-to-frame motion correction showing better correspondence to the expected parameter distribution. Right panel: The same scan motion-corrected with LoR rebinning, shown in sagittal and coronal planes. In each pair the upper image is motion-corrected and the lower one is not.

Figure 2.

Examples of stereo-optical tracking techniques applied to PET for humans (top row), rats (middle row) and long-bore clinical scanners (bottom row).

Figure 1.

Kinetic analysis of dynamic ¹¹C-PiB PET radioligand-protein binding studies in controls and patients with mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). (A) Left: MRI template and Harvard-Oxford atlas labels with coregistered PET SUV_40–70 (MCI: 74 years); Right: Average TACs consistent with reversible binding (target: precuneus, PRC) and non-displaceable uptake (reference: cerbellum, CER). (B) Left: Metabolite-corrected arterial plasma IF (unmetabolized percentage: 86%, 12%, and 6% at 2, 30, and 90 min, respectively); Middle: 2T-4k compartmental model used to assess BBB transport and kinetics of free+nonspecific (ND) and specific radioligand binding; Right: 2T-4k model fits and distribution volume outcomes for target (V_T) and reference (V_ND). (C) Left: Tissue:plasma ratios reveal eventual plateau during study, across groups consistent with transient equilibrium and eventual linearity of the Logan graphical plot (Middle); Right: Agreement between nonlinear compartmental and linear graphical V_T values was verified. (D) Left: PET SUV image; Right: Parametric binding potential (BP_ND) images determined by reference-tissue modelling methods show good correspondence for ¹¹C-PiB, despite voxel noise and some violation of assumptions (i.e., SRTM based on 1T model but ¹¹C-PiB data fit by 2T model, Logan violations of least-squares assumptions, and methods may become unstable when kinetics in reference and target regions are similar). Data processing algorithms and advanced methodologies can help mitigate unwanted bias. See text and references [3,124] for definition of abbreviations and detail.

Figure 2.

Dynamic whole-body PET imaging (adapted from [127]). TOP: Comparison between (a) SUV images (70–90 min post FDG injection), (b) parametric images (0–90 min) of K_i and (c) V_E generated from a FDG scan using the Patlak method with an image-derived input function and a linear regression with spatial constraints. (d) Fusion of K_i and V_E images. BOTTOM: Parametric images for 30 min (6 × 5min/pass) time windows. (e) Indirect K_i and (f) direct K_i images for frames spanning ^~10–40 min post-injection. (g) Indirect K_i and (h) direct K_i images for frames spanning ^~60–90 min post-injection. Three iterations (21 subsets) were used, and 6 mm Gaussian filter post-smoothing applied. The small tumour (shown by arrow) at the dome of the liver is seen in early imaging (e, f) but only on direct K_i image for later imaging (h). [Ki: net FDG influx; VE: exchangeable volume of distribution]

Figure 1.

Impact of variation in PET image quality on several quantitative standard and radiomic parameters. Metrics were extracted using a 50% of SUV_peak isocontour. Images and data are presented for a standardised (STD=EARL compliant) and a high resolution reconstruction.

Figure 1.

An example of the use of metabolic functional imaging to monitor response to adjuvant pharmacotherapy in a subject with Non-Small Cell Lung Cancer (NSCLC) expressing the anaplastic lymphoma kinase (ALK) gene, and thus suitable for treatment with a protein kinase inhibitor. The post-baseline FDG PET/CT scans were performed regularly using a “low-dose” protocol after commencing therapy. A rapid response is seen on the PET images by 3 weeks, which endures to the one year time point. The subject remains alive and virtually disease-free at the time of writing, some 2.5 years after diagnosis of a very aggressive and advanced Stage IV cancer.

Figure 2.

Example of the role of PET imaging in liver-directed treatment for metastatic colorectal carcinoma using 1.8 GBq of [⁹⁰Y]-resin microspheres (SIR-Spheres, Sirtex Medical, Australia). The FDG PET scans on the top row show the baseline (left) with increased uptake seen in two predominant lesions and the resolution of the lesions seen 8 weeks after treatment in the follow-up scan (right). The images on the bottom row show the SPECT treatment planning scan (left) using [^99mTc]MAA and the distribution of the ⁹⁰Y microspheres after treatment (right). The ⁹⁰Y PET images are readily converted to dose maps in units of Gy. In this case the predominant lesion was measured to have received 143 Gy averaged over the lesion.

Figure 2:

Quantitative interpretation of PET images

Figure 3:

Comparison of denoising performance of different neural networks: (a) Full-count (b) low-count, (c) U-Net denoised, (d) GAN denoised, and (e) CycleGAN denoised images representing coronal slices from a whole-body clinical PET scan. (f) Peak signal-to-noise ratio (PSNR) comparison for the noisy and denoised images using the full-count image as the ground truth. (Adapted from [153]).