Akaike information criterion statistics

To investigate the feasibility of ultra-low-activity total-body positron emission tomography (PET) dynamic imaging for quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) in normal organs and to verify its clinical relevance with full-activity imaging. Dynamic total-body PET imaging was performed in 20 healthy volunteers, with eight using full activity (3.7 MBq/kg) of 18F-FDG and 12 using 10× activity reduction (0.37 MBq/kg). Image contrast, in terms of liver-to-muscle ratio (LMR), liver-to-blood ratio (LBR), and blood-to-muscle ratio (BMR) of radioactivity concentrations were assessed. A two-tissue compartment model was fitted to the time-to-activity curves in organs based on regions of interest (ROIs) delineation using PMOD, and constant rates (k1, k2, and k3) were generated. Kinetic constants, corresponding coefficients of variance (CoVs), image contrast, radiation dose, prompt counts, and data size were compared between full- and low-activity groups. All constant rates, corresponding CoVs, and image contrast in different organs were comparable with none significant differences between full- and ultra-low-activity groups. PET images in the ultra-low-activity group generated significantly lower effective radiation dose (median, 0.419 vs. 4.886 mSv, P < 0.001), reduced prompt counts (median, 2.79 vs. 55.68 billion, P < 0.001), and smaller data size (median, 71.11 vs. 723.18 GB, P < 0.001). Total-body dynamic PET imaging using 10× reduction of injected activity could achieve relevant kinetic metrics of 18F-FDG and comparable image contrast with full-activity imaging. Activity reduction results in significant decrease of radiation dose and data size, rendering it more acceptable and easier for data reconstruction, transmission, and storage for clinical practice.

Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18F-FDG in healthy volunteers.

In this contribution, several opportunities and challenges for long axial field of view (LAFOV) PET are described. It is an anthology in which the main issues have been highlighted. A consolidated overview of the camera system implementation, business and financial plan, opportunities and challenges is provided. What the nuclear medicine and molecular imaging community can expect from these new PET/CT scanners is the delivery of more comprehensive information to the clinicians for advancing diagnosis, therapy evaluation and clinical research.

/pdf/long-axial-field-of-view-pet-scanners-a-road-map-to-6spxaqkbr0.pdf

Long axial field of view PET scanners : a road map to implementation and new possibilities

Purpose Since the 1990s, PET has been successfully combined with MR or CT systems. In the past years, especially PET systems have seen a trend towards an enlarged axial field of view (FOV), up to a factor of ten. Methods Conducting a thorough literature research, we summarize the status quo of contemporary total-body (TB) PET/CT scanners and give an outlook on possible future developments. Results Currently, three human TB PET/CT systems have been developed: The PennPET Explorer, the uExplorer, and the Biograph Vision Quadra realize aFOVs between 1 and 2 m and show a tremendous increase in system sensitivity related to their longer gantries. Conclusion The increased system sensitivity paves the way for short-term, low-dose, and dynamic TB imaging as well as new examination methods in almost all areas of imaging.

/pdf/hybrid-total-body-pet-scanners-current-status-and-future-tiqqfcu1b5.pdf

Hybrid total-body pet scanners-current status and future perspectives

[68 Ga]Ga-PSMA-11 is a promising radiopharmaceutical for detecting tumour lesions in prostate cancer, but knowledge of the pharmacokinetics is limited. Dynamic PET-CT was performed to investigate the tumour detection and differences in temporal distribution, as well as in kinetic modelling of [68 Ga]Ga-PSMA-11 by tissue type. Dynamic PET-CT over the lower abdomen and static whole-body PET-CT 80–90 min p.i. from 142 patients with biochemical recurrence were retrospectively analysed. Detection rates were compared to PSA levels. Average time-activity curves were calculated from tumour lesions and normal tissue. A three-compartment model and non-compartment model were used to calculate tumour kinetics. Overall detection rate was 70.42%, and in patients with PSA > 0.4 ng/mL 76.67%. All tumour lesions presented the steepest standardised uptake value (SUV) incline in the first 7–8 min before decreasing to different degrees. Normal tissue presented with a low uptake, except for the bladder, which accumulated activity the steepest 15–16 min. p.i.. While all tumour lesions continuously increased, bone metastases showed the steepest decline, resulting in a significantly lower SUV than lymph node metastases (60 and 80–90 min). Transport rate from the blood and tracer binding and internalisation rate were lower in bone metastases. Heterogeneity (fractal dimension) and vascular density were significantly lower in bone metastases. Even at low PSA between 0.51 and 0.99 ng/mL, detection rate was 57%. Dynamic imaging showed a time window in the first 10 min where tumour uptake is high, but no bladder activity is measured, aiding accuracy in distinction of local recurrence. Kinetic modelling provided additional information for tumour characterisation by tissue type.

/pdf/pharmacokinetic-studies-of-68-ga-ga-psma-11-in-patients-with-rgwdhsgjx6.pdf

Pharmacokinetic studies of [68 Ga]Ga-PSMA-11 in patients with biochemical recurrence of prostate cancer: detection, differences in temporal distribution and kinetic modelling by tissue type

Parametric imaging has been shown to provide better quantitation physiologically compared with SUV imaging in PET. With the increased sensitivity from a recently developed total-body PET scanner, whole-body scans with higher temporal resolution become possible for dynamic analysis and parametric imaging. In this paper, we focus on deriving the parameter k1 using compartmental modeling, and on developing a method to acquire whole-body FDG-PET parametric images using only the first 90 seconds of the post-injection scan data with the total-body PET system.
Dynamic projections were acquired with a time interval of 1 second for the first 30 seconds and 2 seconds for the following minute. Image-derived input functions were acquired from the reconstructed dynamic sequences in the ascending aorta. The one-tissue compartment model with the total of 4 parameters (k1, k2, blood fraction, delay time) was used. A maximum-likelihood based estimation method was developed with the 1-tissue compartment model solution. The accuracy of the acquired parameters was compared with the ones estimated using a 2-tissue irreversible model with 1-hour long data.
All four parametric images were successfully calculated using data from two volunteers. By comparing the time-activity-curves acquired from the volume of interests, it was shown that the parameters estimated using our method were able to predict the time-activity curves of the early dynamics of FDG in different organs. The time delay effects for different organs were also clearly visible in the reconstructed time delay image with delay variations as large as 40 seconds. The estimated parameters using both 90 seconds data and 1-hour long data were in good agreement for k1 and blood fraction, while a large difference of k2 was found between the 90 seconds and 1-hour data, suggesting k2 can’t be reliably estimated from the 90 second scan.
We have shown that with the use of total-body PET and the increased sensitivity, it is possible to estimate parametric images based on the very early dynamics following FDG injection. The estimated k1 could potentially be used clinically as an indicator for identifying abnormalities.

https://jnm.snmjournals.org/content/jnumed/early/2020/09/18/jnumed.119.238113.full.pdf

Total-Body Quantitative Parametric Imaging of Early Kinetics of 18F-FDG.

For a dedicated brain scan, the carotid artery is the best location for acquiring an image-based input function. With improvements in PET spatial resolution, accurate quantitation may be achieved with PET data alone. With the ability to cover both the carotid artery and the thorax at high spatial resolution, the uEXPLORER datasets provide a unique opportunity to develop and validate input functions in multiple regions such as the carotid artery. The regions containing the carotid arteries were first manually identified using reconstructed images consisting of the first 60 seconds of data post-injection. The image-based point spread function (PSF) was measured using off-center phantom scans to approximate the locations of the carotid artery. The same reconstruction approach was used for both the phantom scans and the volunteer scans. The structure of the carotid artery at each slice was generated using a deconvolution approach. An additional constraint of a uniform activity distribution within the carotid artery was added in the deconvolution approach. The acquired carotid artery structure was then applied to the dynamic frames (1-hour data) from volunteer scans for partial volume correction to acquire the input function (CA-IF). The input function from the descending aorta (DA-IF) was also extracted as a gold standard. The area-under-curve (AUC) ratio between the two input functions was used to evaluate the accuracy of the method. Without correction, there was a significant visual difference between CA-IF and the DA-IF, which was reduced dramatically after correction. The quantitation difference was dramatically reduced with the proposed correction method. The AUC ratio between the two input functions was 0.78+-0.04 (original), and was 1.00+-0.03 after correction, suggesting much improved quantitative accuracy. The results demonstrated that with improved image resolution and sensitivity, it is possible to accurately acquire the input function from carotid arteries without reliance on extra anatomical imaging approaches such as MRI.

Development and Validation of an Accurate Input Function from Carotid Arteries using the uEXPLORER

Dynamic positron emission tomography (PET) imaging has a limited temporal resolution, and always suffers from poor signal to noise ratio (SNR) due to low count statistics from short time frames and the ill-posed nature of expectation-maximization (EM) based reconstruction algorithm. Increasing acquisition time for each frame improves image SNR, but may introduce motion artefacts to the region of heart, lung and head. So, a trade-off has to be made between temporal resolution and image quality. Previous studies have demonstrated that aided by prior information from long time frames and/or anatomical images, the kernel method can effectively improve the SNR of the dynamic PET images from very low-count data. In this study, we have investigated the performance of kernel method in long range and simulated short range scanners. We implemented a spatial-temporal kernel method (ST-KEM) and applied it to the data collected from the 194-cm PET/CT scanner (uEXPLORER). Two short range PET scanners were simulated by using partial of the total eight units of uEXPLORER. Additionally, we exploited the way to improve prior images of short range scanners through deep learning. The results showed that ST-KEM outperformed the kernelized Expectation-Maximization (KEM) and conventional Ordered Subsets Expectation Maximization (OSEM) for frames as short as 0.1 second in uEXPLORER, from which noiseless cardiac motion signal could be extracted. In simulated short range scanners, similar image quality could be achieved when the frame duration extends to 1 to 2-second.

Investigation of Spatial-Temporal Kernel Method for Dynamic Imaging in Short and Long Range PET Scanners

The recently developed total-body PET scanner enables high temporal resolution in dynamic imaging. Due to the much improved temporal resolution and large field of view, delay and the dispersion effects in the image-derived input function, which vary for different tissues and organs, may affect accuracy in parametric imaging. In this paper, the delay effect was studied using the early kinetics of an FDG scan, which may be approximated using a 1-tissue compartment model. Dynamic reconstructed frames were acquired using the total-body PET scanner with 1-second frames for the first 30 seconds and 2 seconds for the subsequent 60 seconds. The image-derived input function was acquired from the reconstructed dynamic sequence using volumes of interest in the ascending and descending aorta. Voxel-specific delay times for the plasma input function were also modeled within the 1-tissue compartment model. A total of 4 parametric images were generated. Image-based parametric image generation was achieved with a maximum likelihood estimation method. Parametric images with and without the modeling of delay time in the input function were compared. Additional image denoising techniques including Gaussian denoising and non-local-mean denoising were employed. Quantitative evaluation was achieved by the calculation of the Akaike Information Criterion (AIC). The voxel-specific parameters of the 1-tissue compartment together with the delay time were successfully reconstructed using the proposed method. The estimated delay time showed variations as large as 40 seconds. The non-local-mean filter was shown to be able to reduce the image noise of the generated parametric images. Various image artifacts were observed when no delay time model was included. We have shown that with the use of total-body PET and the increased sensitivity, it is possible to estimate parametric images using the very early stages of the FDG injection. The combined effects of delay and dispersion will be studied in the future.

Yizhang Zhao

Papers

Total-Body Quantitative Parametric Imaging of Early Kinetics of 18F-FDG.

Development and Validation of an Accurate Input Function from Carotid Arteries using the uEXPLORER

Investigation of Spatial-Temporal Kernel Method for Dynamic Imaging in Short and Long Range PET Scanners

The Effects of Delay on the Input Function for Early Dynamics in Total Body Parametric Imaging