Simplified protocol for whole-body Patlak parametric imaging with 18 F-FDG PET/CT: Feasibility and error analysis

Validation of cardiac image-derived input functions for functional PET quantification

PURPOSE

Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations.

METHODS

Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard.

RESULTS

For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements.

CONCLUSION

Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.

Dual time-point [18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method

PURPOSE

We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy.

METHODS

We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data.

RESULTS

In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method.

CONCLUSIONS

Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives

BACKGROUND

Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades.

CHALLENGES

The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results.

CONCLUSIONS

In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies

Long axial field of view (LAFOV) PET-CT scanners have been recently developed and are already in clinical use in few centers worldwide. Although still limited, the hitherto acquired experience with these novel systems highlights an increased sensitivity as their main advantage, which results in an increased lesion detectability. This attribute, alternatively, allows a reduction in PET acquisition time and/or administered radiotracer dose, while it renders delayed scanning of satisfying diagnostic accuracy possible. Another potential advantage of the new generation scanners is CT-less approaches for attenuation correction with the impact of marked reduction of radiation exposure, which may in turn lead to greater acceptance of longitudinal PET studies in the oncological setting. Further, the possibility for the first time of whole-body dynamic imaging, improved compartment modeling, and whole-body parametric imaging represent unique characteristics of the LAFOV PET-CT scanners. On the other hand, the advent of the novel LAFOV scanners is linked to specific challenges, such as the high purchase price and issues related to logistics and their optimal operation in a nuclear medicine department. Moreover, with regard to its research applications in oncology, the full potential of the new scanners can only be reached if different radiopharmaceuticals, both short and long-lived ones, as well as novel tracers, are available for use, which would, in turn, require the appropriate infrastructure in the area of radiochemistry. Although the novel LAFOV scanners are not yet widely used, this development represents an important step in the evolution of molecular imaging. This review presents the advantages and challenges of LAFOV PET-CT imaging for oncological applications with respect to static and dynamic acquisition protocols as well as to new tracers, while it provides an overview of the literature in the field.

Multiparametric dynamic whole-body PSMA PET/CT using [68Ga]Ga-PSMA-11 and [18F]PSMA-1007

BACKGROUND

Routine prostate-specific membrane antigen (PSMA) positron emission tomography (PET) performed for primary staging or restaging of prostate cancer patients is usually done as a single static image acquisition 60 min after tracer administration. In this study, we employ dynamic whole-body (D-WB) PET imaging to compare the pharmacokinetics of [⁶⁸Ga]Ga-PSMA-11 and [¹⁸F]PSMA-1007 in various tissues and lesions, and to assess whether Patlak parametric images are quantitative and improve lesion detection and image readability.

METHODS

Twenty male patients with prostate cancer were examined using a D-WB PSMA PET protocol. Ten patients were scanned with [⁶⁸Ga]Ga-PSMA-11 and ten with [¹⁸F]PSMA-1007. Kinetic analyses were made using time-activity curves (TACs) extracted from organs (liver, spleen, bone, and muscle) and lesions. For each patient, three images were produced: SUV + Patlak parametric images (K_i and DV). All images were reviewed visually to compare lesion detection, image readability was quantified using target-to-background ratios (TBR), and Ki and DV values were compared.

RESULTS

The two PSMA tracers exhibited markedly different pharmacokinetics in organs: reversible for [⁶⁸Ga]Ga-PSMA-11 and irreversible for [¹⁸F]PSMA-1007. For both tracers, lesions kinetics were best described by an irreversible model. All parametric images were of good visual quality using both radiotracers. In general, Ki images were characterized by reduced vascular signal and increased lesion TBR compared with SUV images. No additional malignant lesions were identified on the parametric images.

CONCLUSION

D-WB PET/CT is feasible for both PSMA tracers allowing for direct reconstruction of parametric Ki images. The use of multiparametric PSMA images increased TBR but did not lead to the detection of more lesions. For quantitative whole-body Ki imaging, [¹⁸F]PSMA-1007 should be preferred over [⁶⁸Ga]Ga-PSMA-11 due to its irreversible kinetics in organs and lesions.

Comparison of cardiac image-derived input functions for quantitative whole body [18F]FDG imaging with arterial blood sampling

Introduction: Dynamic positron emission tomography (PET) and the application of kinetic models can provide important quantitative information based on its temporal information. This however requires arterial blood sampling, which can be challenging to acquire. Nowadays, state-of-the-art PET/CT systems offer fully automated, whole-body (WB) kinetic modelling protocols using image-derived input functions (IDIF) to replace arterial blood sampling. Here, we compared the validity of an automatic WB kinetic model protocol to the reference standard arterial input function (AIF) for both clinical and research settings. Methods: Sixteen healthy participants underwent dynamic WB [18F]FDG scans using a continuous bed motion PET/CT system with simultaneous arterial blood sampling. Multiple processing pipelines that included automatic and manually generated IDIFs derived from the aorta and left ventricle, with and without motion correction were compared to the AIF. Subsequently generated quantitative images of glucose metabolism were compared to evaluate performance of the different input functions. Results: We observed moderate to high correlations between IDIFs and the AIF regarding area under the curve (r = 0.49-0.89) as well as for the cerebral metabolic rate of glucose (CMRGlu) (r = 0.68-0.95). Manual placing of IDIFs and motion correction further improved their similarity to the AIF. Discussion: In general, the automatic vendor protocol is a feasible approach for the quantification of CMRGlu for both, clinical and research settings where expertise or time is not available. However, we advise on a rigorous inspection of the placement of the volume of interest, the resulting IDIF, and the quantitative values to ensure valid interpretations. In protocols requiring longer scan times or where cohorts are prone to involuntary movement, manual IDIF definition with additional motion correction is recommended, as this has greater accuracy and reliability.

Shortened duration whole body 18F-FDG PET Patlak imaging on the Biograph Vision Quadra PET/CT using a population-averaged input function

Background

Excellent performance characteristics of the Vision Quadra PET/CT, e.g. a substantial increase in sensitivity, allow for precise measurements of image-derived input functions (IDIF) and tissue time activity curves. Previously we have proposed a method for a reduced 30 min (as opposed to 60 min) whole body ¹⁸F-FDG Patlak PET imaging procedure using a previously published population-averaged input function (PIF) scaled to IDIF values at 30–60 min post-injection (p.i.). The aim of the present study was to apply this method using the Vision Quadra PET/CT, including the use of a PIF to allow for shortened scan durations.

Methods

Twelve patients with suspected lung malignancy were included and received a weight-based injection of ¹⁸F-FDG. Patients underwent a 65-min dynamic PET acquisition which were reconstructed using European Association of Nuclear Medicine Research Ltd. (EARL) standards 2 reconstruction settings. A volume of interest (VOI) was placed in the ascending aorta (AA) to obtain the IDIF. An external PIF was scaled to IDIF values at 30–60, 40–60, and 50–60 min p.i., respectively, and parametric ¹⁸F-FDG influx rate constant (K_i) images were generated using a t* of 30, 40 or 50 min, respectively. Herein, tumour lesions as well as healthy tissues, i.e. liver, muscle tissue, spleen and grey matter, were segmented.

Results

Good agreement between the IDIF and corresponding PIF scaled to 30–60 min p.i. and 40–60 min p.i. was obtained with 7.38% deviation in K_i. Bland–Altman plots showed excellent agreement in K_i obtained using the PIF scaled to the IDIF at 30–60 min p.i. and at 40–60 min p.i. as all data points were within the limits of agreement (LOA) (− 0.004–0.002, bias: − 0.001); for the 50–60 min p.i. K_i, all except one data point fell in between the LOA (− 0.021–0.012, bias: − 0.005).

Conclusions

Parametric whole body ¹⁸F-FDG Patlak K_i images can be generated non-invasively on a Vision Quadra PET/CT system. In addition, using a scaled PIF allows for a substantial (factor 2 to 3) reduction in scan time without substantial loss of accuracy (7.38% bias) and precision (image quality and noise interference).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00504-9.

Clinical validation of a population-based input function for 20-min dynamic whole-body 18F-FDG multiparametric PET imaging

Purpose

Contemporary PET/CT scanners can use 70-min dynamic whole-body (D-WB) PET to generate more quantitative information about FDG uptake than just the SUV by generating parametric images of FDG metabolic rate (MR_FDG). The analysis requires the late (50–70 min) D-WB tissue data combined with the full (0–70 min) arterial input function (AIF). Our aim was to assess whether the use of a scaled population-based input function (sPBIF) obviates the need for the early D-WB PET acquisition and allows for a clinically feasible 20-min D-WB PET examination.

Methods

A PBIF was calculated based on AIFs from 20 patients that were D-WB PET scanned for 120 min with simultaneous arterial blood sampling. MR_FDG imaging using PBIF requires that the area under the curve (AUC) of the sPBIF is equal to the AUC of the individual patient’s input function because sPBIF AUC bias translates into MR_FDG bias. Special patient characteristics could affect the shape of their AIF. Thus, we validated the use of PBIF in 171 patients that were divided into 12 subgroups according to the following characteristics: diabetes, cardiac ejection fraction, blood pressure, weight, eGFR and age. For each patient, the PBIF was scaled to the aorta image-derived input function (IDIF) to calculate a sPBIF, and the AUC bias was calculated.

Results

We found excellent agreement between the AIF and IDIF at all times. For the clinical validation, the use of sPBIF led to an acceptable AUC bias of 1–5% in most subgroups except for patients with diabetes or patients with low eGFR, where the biases were marginally higher at 7%. Multiparametric MR_FDG images based on a short 20-min D-WB PET and sPBIF were visually indistinguishable from images produced by the full 70-min D-WB PET and individual IDIF.

Conclusions

A short 20-min D-WB PET examination using PBIF can be used for multiparametric imaging without compromising the image quality or precision of MR_FDG. The D-WB PET examination may therefore be used in clinical routine for a wide range of patients, potentially allowing for more precise quantification in e.g. treatment response imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00490-y.

FDG PET Scan Durations via Effective Data Processing

BACKGROUND

Total-body dynamic PET (dPET) imaging using ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 hour seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate K_i estimations of tumor lesions.

METHODS

Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using ¹⁸ F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 hour PI. Patlak analysis-based K_i parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubit Hermite interpolation (THI) was applied. The resultant K_i images acquired by 30-min dynamic PET data and the standard 1-hour K_i images were compared in terms of visual imaging effect, region signal-to-noise ratio (SNR), and K_i estimation accuracy to evaluate the performance of the proposed K_i imaging method with a shortened scan duration.

RESULTS

With the help of Patlak data processing, acceptable K_i parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with K_i images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman (BA) plot and Person correlation coefficient (PPC) analysis showed that that 30-min K_i images obtained from the processed Patlak data had higher accuracy for tumor lesions.

CONCLUSION

Satisfactory K_i parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher K_i imaging quality and higher accuracy regarding tumor lesion K_i values. Clinically, it is possible to shorten the dynamic scan duration of ¹⁸ F-FDG PET to 30 min to acquire an accurate tumor K_i and further effective tumor detection with uEXPLORER scanners. This article is protected by copyright. All rights reserved.

Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions

Objective

We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET.

Methods

Twenty-one patients underwent whole-body dynamic ¹⁸F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MR_FDG and DV_FDG images (MR_FDG-_std and DV_FDG-_std) and two short dynamic MR_FDG and DV_FDG images (MR_FDG-_tsd and DV_FDG-_tsd), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis.

Results

High correlations were obtained between MR_FDG-_tsd and MR_FDG-_std, and between DV_FDG-_tsd and DV_FDG-_std for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MR_FDG-max, MR_FDG-mean, and MR_FDG-peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DV_FDG-max, DV_FDG-mean, and DV_FDG-peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MR_FDG-max, MR_FDG-mean, and MR_FDG-peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DV_FDG-max, DV_FDG-mean, and DV_FDG-peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively.

Conclusions

This study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis.

Total-body parametric imaging using the patlak model: Feasibility of reduced scan time

PURPOSE

This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER.

METHODS

A total of 65 patients (27 females and 38 males, age 56.1±10.4) were recruited in this study. ¹⁸ F-FDG was injected and its dose was adjusted by body weight (4.07 MBq / kg).Total-body dynamic scanning was performed on the uEXPLORER Total-Body PET/CT scanner with a total scan time of 60 minutes from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the K_i images designated as G_IDIF with different acquisition times (20-60, 30-60, 40-60, and 44-60 min). The population-based input function (PBIF) was constructed from the mean value of the IDIF from the population, and K_i images designated as G_PBIF were generated using the PBIF. Non-localmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: G_IDIF+NLM and G_PBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the K_i images from different groups. The 20-60 min scans in G_IDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. K_i values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy.

RESULTS

The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all 4 groups (all p<0.001). The K_i images in the G_IDIF and G_PBIF groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of G_IDIF . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between K_i values estimated with G_IDIF and G_PBIF in muscle and lesion regions (all p>0.5). After applying the NLM filter, The coefficient of variation could be reduced on the order of [1%, 15%, 19%,37%] and [110%, 125%, 94%, 69%] with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The K_i images in the G_IDIF+NLM and G_PBIF+NLM groups generated from the 20-min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16-min scans. No significant difference was found between K_i values produced with G_IDIF+NLM and G_PBIF+NLM in muscle and lesion regions(all p>0.7).

CONCLUSIONS

The K_i images generated by the PBIF-based Patlak model using a 20-min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with G_IDIF from 40-min dynamic data, and there is no significant difference between K_i images generated using IDIF or PBIF (p>0.5). This article is protected by copyright. All rights reserved.

Theranostics – present and future

Theragnostics in nuclear medicine constitute an essential element of precision medicine. This notion integrates radionuclide diagnostics procedures and radionuclide therapies using appropriate radiopharmaceutics and treatment targeting specific biological pathways or receptors. The term theragnostics should also include another aspect of treatment: not only whether a given radioisotopic drug can be used, but also in what dose it ought to be used. Theragnostic procedures also allow predicting the effects of treatment based on the assessment of specific receptor density or the metabolic profile of neoplastic cells. The future of theragnostics depends not only on the use of new radiopharmaceuticals, but also on new gamma cameras. Modern theragnostics already require unambiguous pharmacokinetic and pharmacodynamic measurements based on absolute values. Only dynamic studies provide such a possibility. The introduction of the dynamic total-body PET-CT will enable this type of measurements characterizing metabolic processes and receptor expression on the basis of Patlak plot.

Parametric Imaging With Dynamic PET for Oncological Applications: Protocols, Interpretation, Current Applications and Limitations for Clinical Use

Nuclear medicine imaging modalities, and in particular positron emission tomography (PET), provide functional images that demonstrate the mean radioactivity distribution at a defined point in time. With the help of mathematical model's, it is possible to depict isolated parameters of the radiotracers' pharmacokinetics and to visualize them. These so called parametric images add a new dimension to the existing conventional PET images and provide more detailed information about the tracer distribution over time and space. Prerequisite for the calculation of parametric images, which reflect specific pharmacokinetic parameters, is the dynamic PET (dPET) data acquisition. Hitherto, PET parametric imaging has mainly found use for research purposes. However, it has not been yet implemented into clinical routine, since it is more time-consuming, it requires a complicated analysis and still lacks a clear benefit over conventional PET imaging. However, the recent introduction of new PET-CT scanners with an ultralong field of view, which allow a faster data acquisition and are associated with higher sensitivity, as well as the development of more sophisticated evaluation software packages will probably lead to a renaissance of dPET and parametric maps even of the whole body. The implementation of dPET imaging in daily routine with appropriate acquisition protocols, as well as the calculation, interpretation and potential clinical applications of parametric images will be discussed in this review article.

Whole-body Parametric Imaging of FDG PET using uEXPLORER with Reduced Scan Time

Parametric imaging of the net influx rate (K_i) in ¹⁸F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for K_i imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0–4 min and 54–60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the K_i images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion K_i values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the K_i images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired K_i images using different protocols were visually comparable. The average K_i difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire K_i images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.

Use of population input functions for reduced scan duration whole-body Patlak 18F-FDG PET imaging

Abstract

Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated.

Methods

Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K₁, k₃, and both K₁ and k₂) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (K_i) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in K_i was evaluated.

Results

Percentage bias in K_i observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these K_i biases in most cases (287 out of 290) to < 5%.

Conclusions

Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible.

PET Parametric Imaging: Past, Present, and Future

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.