Will extended field-of-view PET/CT depopulate the graveyard of failed PET radiopharmaceuticals?

With the rapid emergence of extended Field-of-View PET-cameras several new applications for radiopharmaceuticals become within reach. Main reason is the significant increase of the sensitivity of the PET-camera so that much less radioactivity can be administered. Issues that that hampered development or use of PET-radiopharmaceuticals become realistic again. Molar activity requirements can become less strict. New low-yielding radiochemistry methods may become applicable. Carbon-11 labelled compounds can revive and potentially be shipped to nearby PET-facilities. PET-radiopharmaceuticals with slow kinetics in comparison to their half life can still be used. As additional infrastructure and equipment will likely remain unchanged and keep the same sensitivity therefore there will be issues with kinetic modelling requiring analysis of plasma or metabolites samples with lower count rate. Besides the potential revival of failed radiopharmaceuticals, novel challenges are ahead to develop novel radiochemistry based on thus far unsuitable (low yielding or time consuming) reactions.

Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review.

PURPOSE: To systematically review the literature evaluating clinical utility of imaging metrics derived from baseline fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for prediction of progression-free (PFS) and overall survival (OS) in patients with classical Hodgkin lymphoma (HL) and diffuse large B cell lymphoma (DLBCL). METHODS: A search of MEDLINE/PubMed, Web of Science, Cochrane, Scopus and clinicaltrials.gov databases was undertaken for articles evaluating PET/CT imaging metrics as outcome predictors in HL and DLBCL. PRISMA guidelines were followed. Risk of bias was assessed using the Quality in Prognosis Studies (QUIPS) tool. RESULTS: Forty-one articles were included (31 DLBCL, 10 HL). Significant predictive ability was reported in 5/20 DLBCL studies assessing SUVmax (PFS: HR 0.13-7.35, OS: HR 0.83-11.23), 17/19 assessing metabolic tumour volume (MTV) (PFS: HR 2.09-11.20, OS: HR 2.40-10.32) and 10/13 assessing total lesion glycolysis (TLG) (PFS: HR 1.078-11.21, OS: HR 2.40-4.82). Significant predictive ability was reported in 1/4 HL studies assessing SUVmax (HR not reported), 6/8 assessing MTV (PFS: HR 1.2-10.71, OS: HR 1.00-13.20) and 2/3 assessing TLG (HR not reported). There are 7/41 studies assessing the use of radiomics (4 DLBCL, 2 HL); 5/41 studies had internal validation and 2/41 included external validation. All studies had overall moderate or high risk of bias. CONCLUSION: Most studies are retrospective, underpowered, heterogenous in their methodology and lack external validation of described models. Further work in protocol harmonisation, automated segmentation techniques and optimum performance cut-off is required to develop robust methodologies amenable for clinical utility.

Effectiveness of Respiratory-gated Positron Emission Tomography/Computed Tomography for Radiotherapy Planning in Patients with Lung Carcinoma - A Systematic Review.

AIMS: A systematic review of the literature evaluating the clinical use of respiratory-gated (four-dimensional; 4D) fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) compared with non-gated (three-dimensional; 3D) PET/CT for radiotherapy planning in lung cancer. MATERIALS AND METHODS: A search of MEDLINE, Cochrane, Web of Science, SCOPUS and clinicaltrials.gov databases was undertaken for articles comparing 3D and 4D PET/CT tumour volume or 4D PET/CT for radiotherapy planning. PRISMA guidelines were followed. RESULTS: Thirteen studies compared tumour volumes at 3D and 4D PET/CT; eight reported significantly smaller volumes (6.9-44.5%), three reported significantly larger volumes at 4D PET/CT (16-50%), one reported no significant difference and one reported mixed findings. Six studies, including two that reported differences in tumour volumes, compared target volumes or studied geographic misses. 4D PET/CT target volumes were significantly larger (19-40%) when compared with 3D PET/CT in all but one study, where they were smaller (3.8%). One study reported no significance in 4D PET/CT target volumes when compared with 4D CT, whereas another study reported significantly larger volumes (38.7%). CONCLUSION: The use of 4D PET/CT leads to differences in target volume delineation compared with 3D PET/CT. These differences vary depending upon technique and the clinical impact currently remains uncertain. Correlation of pretreatment target volumes generated at 3D and 4D PET/CT with postsurgical histology would be ideal but technically challenging. Evaluation of patient outcomes based on 3D versus 4D PET/CT derived treatment volumes warrants further investigation.

Impact of motion compensation and partial volume correction for 18F-NaF PET/CT imaging of coronary plaque.

Recent studies have suggested that 18F-NaF-PET enables visualization and quantification of plaque micro-calcification in the coronary tree. However, PET imaging of plaque calcification in the coronary arteries is challenging because of the respiratory and cardiac motion as well as partial volume effects. The objective of this work is to implement an image reconstruction framework, which incorporates compensation for respiratory as well as cardiac motion (MoCo) and partial volume correction (PVC), for cardiac 18F-NaF PET imaging in PET/CT. We evaluated the effect of MoCo and PVC on the quantification of vulnerable plaques in the coronary arteries. Realistic simulations (Biograph TPTV, Biograph mCT) and phantom acquisitions (Biograph mCT) were used for these evaluations. Different uptake values in the calcified plaques were evaluated in the simulations, while three 'plaque-type' lesions of 36, 31 and 18 mm3 were included in the phantom experiments. After validation, the MoCo and PVC methods were applied in four pilot NaF-PET patient studies. In all cases, the MoCo-based image reconstruction was performed using the STIR software. The PVC was obtained from a local projection (LP) method, previously evaluated in preclinical and clinical PET. The results obtained show a significant increase of the measured lesion-to-background ratios (LBR) in the MoCo + PVC images. These ratios were further enhanced when using directly the tissue-activities from the LP method, making this approach more suitable for the quantitative evaluation of coronary plaques. When using the LP method on the MoCo images, LBR increased between 200% and 1119% in the simulated data, between 212% and 614% in the phantom experiments and between 46% and 373% in the plaques with positive uptake observed in the pilot patients. In conclusion, we have built and validated a STIR framework incorporating MoCo and PVC for 18F-NaF PET imaging of coronary plaques. First results indicate an improved quantification of plaque-type lesions.

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring.

PURPOSE: Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera. METHODS: The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in in vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction. RESULTS: Images of the numerical phantom exhibited blurring and edge artifacts on the bone-tissue and air-tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the in vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV max for simulated lesions in the range of 7.17%-12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and -0.21% to +1.81% (lesions). CONCLUSIONS: Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.

The effect of regularization in motion compensated PET image reconstruction: a realistic numerical 4D simulation study.

Following continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.

Analysis and comparison of two methods for motion correction in PET imaging.

PURPOSE: Although there have been various proposed methods for positron emission tomography (PET) motion correction, there is not sufficient evidence to answer which method is better in practice. This investigation aims to characterize the behavior of the two main motion-correction approaches in terms of convergence and image properties. METHODS: For the first method, reconstruct-transform-average (RTA), reconstructions of each gate are transformed to a reference gate and averaged. In the second method, motion-compensated image reconstruction (MCIR), motion information is incorporated within the reconstruction. Both techniques studied were based on the ordered subsets expectation maximization algorithm. Motion information was obtained from a dynamic MR acquisition performed on a human volunteer and concurrent PET data were simulated from the dynamic MR data. The two approaches were assessed statistically using multiple realizations to accurately define the noise properties of the reconstructed images. RESULTS: MCIR successfully recovers the true values of all regions, whereas RTA has high bias due to the limited count-statistics and interpolation errors during the transformation step. In addition, RTA noise is very small and stabilized, whereas in MCIR noise becomes progressively greater with the number of iterations and therefore MCIR outperforms RTA in terms of MSE only if noise is treated. For example, MCIR with postfiltering results in MSE up to 42% lower than RTA. CONCLUSIONS: This study indicates that MCIR may provide superior performance overall to RTA if noise is minimized. However, in applications where quantification is not the main objective RTA can be a practical and simple method to correct for motion.

Wavelet-based resolution recovery using an anatomical prior provides quantitative recovery for human population phantom PET [¹¹C]raclopride data.

The objective of this study was to evaluate a resolution recovery (RR) method using a variety of simulated human brain [¹¹C]raclopride positron emission tomography (PET) images. Simulated datasets of 15 numerical human phantoms were processed by a wavelet-based RR method using an anatomical prior. The anatomical prior was in the form of a hybrid segmented atlas, which combined an atlas for anatomical labelling and a PET image for functional labelling of each anatomical structure. We applied RR to both 60 min static and dynamic PET images. Recovery was quantified in 84 regions, comparing the typical 'true' value for the simulation, as obtained in normal subjects, simulated and RR PET images. The radioactivity concentration in the white matter, striatum and other cortical regions was successfully recovered for the 60 min static image of all 15 human phantoms; the dependence of the solution on accurate anatomical information was demonstrated by the difficulty of the technique to retrieve the subthalamic nuclei due to mismatch between the two atlases used for data simulation and recovery. Structural and functional synergy for resolution recovery (SFS-RR) improved quantification in the caudate and putamen, the main regions of interest, from -30.1% and -26.2% to -17.6% and -15.1%, respectively, for the 60 min static image and from -51.4% and -38.3% to -27.6% and -20.3% for the binding potential (BP(ND)) image, respectively. The proposed methodology proved effective in the RR of small structures from brain [¹¹C]raclopride PET images. The improvement is consistent across the anatomical variability of a simulated population as long as accurate anatomical segmentations are provided.

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator.

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.

Fast generation of 4D PET-MR data from real dynamic MR acquisitions.

We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150× slower computation time compared to the analytic method for ten respiratory positions and is 7000× slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1% for tissue, 17% for the lungs and 18% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.