Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET

Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities

The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest. The resulting multiparametric PET images capture a tracer's net trapping rate (Ki) and apparent volume of distribution (VD), separating the contributions of bound and free tracer fractions to the PET signal captured in the SUV. Potential benefits of multiparametric PET include higher quantitative stability, superior lesion conspicuity, and greater accuracy for differentiating malignant and benign lesions. However, the imaging protocols necessary for multiparametric PET are inherently more complex and time-intensive, despite the recent introduction of automated or semiautomated scanner-based reconstruction packages. In this Review, we examine the current state of multiparametric PET in whole-body oncologic imaging. We summarize the Patlak methodology and relevant tracer kinetics, discuss clinical workflows and protocol considerations, and highlight clinical challenges and opportunities. We aim to help oncologic imagers make informed decisions about whether to implement multiparametric PET in their clinical practices.

Optimization and impact of sensitivity mode on abbreviated scan protocols with population-based input function for parametric imaging of [18F]-FDG for a long axial FOV PET scanner

BACKGROUND

The long axial field of view, combined with the high sensitivity of the Biograph Vision Quadra PET/CT scanner enables the precise deviation of an image derived input function (IDIF) required for parametric imaging. Traditionally, this requires an hour-long dynamic PET scan for [18F]-FDG, which can be significantly reduced by using a population-based input function (PBIF). In this study, we expand these examinations and include the scanner's ultra-high sensitivity (UHS) mode in comparison to the high sensitivity (HS) mode and evaluate the potential for further shortening of the scan time.

METHODS

Patlak Ki and DV estimates were determined by the indirect and direct Patlak methods using dynamic [18F]-FDG data of 6 oncological patients with 26 lesions (0-65 min p.i.). Both sensitivity modes for different number/duration of PET data frames were compared, together with the potential of using abbreviated scan durations of 20, 15 and 10 min by using a PBIF. The differences in parametric images and tumour-to-background ratio (TBR) due to the shorter scans using the PBIF method and between the sensitivity modes were assessed.

RESULTS

A difference of 3.4 ± 7.0% (Ki) and 1.2 ± 2.6% (DV) was found between both sensitivity modes using indirect Patlak and the full IDIF (0-65 min). For the abbreviated protocols and indirect Patlak, the UHS mode resulted in a lower bias and higher precision, e.g., 45-65 min p.i. 3.8 ± 4.4% (UHS) and 6.4 ± 8.9% (HS), allowing shorter scan protocols, e.g. 50-65 min p.i. 4.4 ± 11.2% (UHS) instead of 7.3 ± 20.0% (HS). The variation of Ki and DV estimates for both Patlak methods was comparable, e.g., UHS mode 3.8 ± 4.4% and 2.7 ± 3.4% (Ki) and 14.4 ± 2.7% and 18.1 ± 7.5% (DV) for indirect and direct Patlak, respectively. Only a minor impact of the number of Patlak frames was observed for both sensitivity modes and Patlak methods. The TBR obtained with direct Patlak and PBIF was not affected by the sensitivity mode, was higher than that derived from the SUV image (6.2 ± 3.1) and degraded from 20.2 ± 12.0 (20 min) to 10.6 ± 5.4 (15 min). Ki and DV estimate images showed good agreement (UHS mode, RC: 6.9 ± 2.3% (Ki), 0.1 ± 3.1% (DV), peak signal-to-noise ratio (PSNR): 64.5 ± 3.3 dB (Ki), 61.2 ± 10.6 dB (DV)) even for abbreviated scan protocols of 50-65 min p.i.

CONCLUSIONS

Both sensitivity modes provide comparable results for the full 65 min dynamic scans and abbreviated scans using the direct Patlak reconstruction method, with good Ki and DV estimates for 15 min short scans. For the indirect Patlak approach the UHS mode improved the Ki estimates for the abbreviated scans.

Glucose metabolic rate from four-dimensional [18F]FDG PET/CT to differentiate sarcoid lesions from malignant lesions

OBJECTIVES

On 18F-Fludeoxyglucose (FDG) PET/CT, active sarcoid lesions are often difficult to differentiate from malignant lesions. We investigated the potential of the glucose metabolic rate (MRglc, mg/min/100 mL), a new quantification of glucose metabolic kinetics derived from direct reconstruction based on linear Patlak analysis, to distinguish between sarcoidosis and malignant lesions.

MATERIALS AND METHODS

A total of 100 patients with cardiac sarcoidosis (CS) and 67 patients with cancer who underwent four-dimensional FDG PET/CT were enrolled. The lesions with a standardized uptake value (SUV) ≥ 2.7 on the standard scan were included as active lesions in the analysis. SUV and MRglc were derived using data acquired between 30 min and 50 min on four-dimensional FDG PET/CT. The mean value in the volume of interest (size 1.5 cm3) was measured. The diagnostic performance of sarcoidosis using MRglc and SUV was evaluated using receiver-operating-characteristic (ROC) analysis.

RESULTS

A total of 90 sarcoidosis lesions from 44 CS patients (18 males, 63.4 ± 12.2 years) and 87 malignant lesions from 57 cancer-bearing patients (32 males, 65 ± 14 years) were analyzed. SUV and MRglc for sarcoid lesions were significantly lower than those for malignant lesions (SUV, 4.98 ± 2.00 vs 6.21 ± 2.14; MRglc, 2.52 ± 1.39 vs 3.68 ± 1.61; p < 0.01). ROC analysis indicated that the ability to discriminate sarcoid patients from those with malignancy yielded areas under the curves of 0.703 and 0.754, with sensitivities of 64% and 77% and specificities of 75% and 72% for SUV 5.025 and MRglc 2.855, respectively.

CONCLUSION

MRglc was significantly lower in sarcoid lesions than malignant lesions, and improved sarcoid lesions identification over SUV alone.

CLINICAL RELEVANCE STATEMENT

MRglc improves sarcoid lymph node identification over SUV alone and is expected to shorten the examination time by eliminating delayed scans.

KEY POINTS

Active sarcoid lesions are sometimes associated with FDG accumulation and should be differentiated from malignant lesions. SUV and metabolic rate of glucose (MRglc) strongly positively correlated, and MRglc could differentiate sarcoid and malignant lesions. MRglc allows for accurate evaluation and staging of malignant lesions.

Non-invasive assessment of stimulation-specific changes in cerebral glucose metabolism with functional PET

PURPOSE

Functional positron emission tomography (fPET) with [18F]FDG allows quantification of stimulation-induced changes in glucose metabolism independent of neurovascular coupling. However, the gold standard for quantification requires invasive arterial blood sampling, limiting its widespread use. Here, we introduce a novel fPET method without the need for an input function.

METHODS

We validated the approach using two datasets (DS). For DS1, 52 volunteers (23.2 ± 3.3 years, 24 females) performed Tetris® during a [18F]FDG fPET scan (bolus + constant infusion). For DS2, 18 participants (24.2 ± 4.3 years, 8 females) performed an eyes-open/finger tapping task (constant infusion). Task-specific changes in metabolism were assessed with the general linear model (GLM) and cerebral metabolic rate of glucose (CMRGlu) was quantified with the Patlak plot as reference. We then estimated simplified outcome parameters, including GLM beta values and percent signal change (%SC), and compared them, region and whole-brain-wise.

RESULTS

We observed higher agreement with the reference for DS1 than DS2. Both DS resulted in strong correlations between regional task-specific beta estimates and CMRGlu (r = 0.763…0.912). %SC of beta values exhibited strong agreement with %SC of CMRGlu (r = 0.909…0.999). Average activation maps showed a high spatial similarity between CMRGlu and beta estimates (Dice = 0.870…0.979) as well as %SC (Dice = 0.932…0.997), respectively.

CONCLUSION

The non-invasive method reliably estimates task-specific changes in glucose metabolism without blood sampling. This streamlines fPET, albeit with the trade-off of being unable to quantify baseline metabolism. The simplification enhances its applicability in research and clinical settings.

A Comparison of Arterial Input Function Interpolation Methods for Patlak Plot Analysis of 68Ga-PSMA-11 PET Prostate Cancer Studies

Positron emission tomography (PET) imaging enables quantitative assessment of tissue physiology. Dynamic pharmacokinetic analysis of PET images requires accurate estimation of the radiotracer plasma input function to derive meaningful parameter estimates, and small discrepancies in parameter estimation can mimic subtle physiologic tissue variation. This study evaluates the impact of input function interpolation method on the accuracy of Patlak kinetic parameter estimation through simulations modeling the pharmacokinetic properties of [68Ga]-PSMA-11. This study evaluated both trained and untrained methods. Although the mean kinetic parameter accuracy was similar across all interpolation models, the trained node weighting interpolation model estimated accurate kinetic parameters with reduced overall variability relative to standard linear interpolation. Trained node weighting interpolation reduced kinetic parameter estimation variance by a magnitude approximating the underlying physiologic differences between normal and diseased prostatic tissue. Overall, this analysis suggests that trained node weighting improves the reliability of Patlak kinetic parameter estimation for [68Ga]-PSMA-11 PET.

Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint

Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

Mapping 18F-FDG Kinetics Together with Patient-Specific Bootstrap Assessment of Uncertainties: An Illustration with Data from a PET/CT Scanner with a Long Axial Field of View

The purpose of this study was to examine a nonparametric approach to mapping kinetic parameters and their uncertainties with data from the emerging generation of dynamic whole-body PET/CT scanners. Methods: Dynamic PET 18F-FDG data from a set of 24 cancer patients studied on a long-axial-field-of-view PET/CT scanner were considered. Kinetics were mapped using a nonparametric residue mapping (NPRM) technique. Uncertainties were evaluated using an image-based bootstrapping methodology. Kinetics and bootstrap-derived uncertainties are reported for voxels, maximum-intensity projections, and volumes of interest (VOIs) corresponding to several key organs and lesions. Comparisons between NPRM and standard 2-compartment (2C) modeling of VOI kinetics are carefully examined. Results: NPRM-generated kinetic maps were of good quality and well aligned with vascular and metabolic 18F-FDG patterns, reasonable for the range of VOIs considered. On a single 3.2-GHz processor, the specification of the bootstrapping model took 140 min; individual bootstrap replicates required 80 min each. VOI time-course data were much more accurately represented, particularly in the early time course, by NPRM than by 2C modeling constructs, and improvements in fit were statistically highly significant. Although 18F-FDG flux values evaluated by NPRM and 2C modeling were generally similar, significant deviations between vascular blood and distribution volume estimates were found. The bootstrap enables the assessment of quite complex summaries of mapped kinetics. This is illustrated with maximum-intensity maps of kinetics and their uncertainties. Conclusion: NPRM kinetics combined with image-domain bootstrapping is practical with large whole-body dynamic 18F-FDG datasets. The information provided by bootstrapping could support more sophisticated uses of PET biomarkers used in clinical decision-making for the individual patient.

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.

METHODS

The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.

RESULTS

The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.

CONCLUSION

The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Determining the optimal pharmacokinetic modelling and simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer (PPCa)

PURPOSE

This paper discusses the optimization of pharmacokinetic modelling and alternate simplified quantification method for [18F]AlF-P16-093, a novel tracer for in vivo imaging of prostate cancer.

METHODS

Dynamic PET/CT scans were conducted on eight primary prostate cancer patients, followed by a whole-body scan at 60 min post-injection. Time-activity curves (TACs) were obtained by drawing volumes of interest for primary prostatic and metastatic lesions. Optimal kinetic modelling involved evaluating three compartmental models (1T2K, 2T3K, and 2T4K) accounting for fractional blood volume (Vb). The simplified quantification method was then determined based on the correlation between the static uptake measure and total distribution volume (Vt) obtained from the optimal pharmacokinetic analysis.

RESULTS

In total, 17 intraprostatic lesions, 10 lymph nodes, and 36 osseous metastases were evaluated. Visually, the contrast of the tumor increased and showed the steepest incline within the first few minutes, whereas background activity decreased over time. Full pharmacokinetic analysis revealed that a reversible two-compartmental (2T4K) model is the preferred kinetic model for the given tracer. The kinetic parameters K1, k3, Vb, and Vt were all significantly higher in lesions when compared with normal tissue (P < 0.01). Several simplified protocols were tested for approximating comprehensive dynamic quantification in tumors, with image-based SURmean (the ratio of tumor SUVmean to blood SUVmean) within the 28-34 min window found to be sufficient for approximating the total distribution Vt values (R2 = 0.949, P < 0.01). Both Vt and SURmean correlated significantly with the total serum prostate-specific antigen (tPSA) levels (P < 0.01).

CONCLUSIONS

This study introduced an optimized pharmacokinetic modelling approach and a simplified acquisition method for [18F]AlF-P16-093, a novel PSMA-targeted radioligand, highlighting the feasibility of utilizing one static PET imaging (between 30 and 60 min) for the diagnosis of prostate cancer. Note that the image-derived input function in this study may not reflect the true corrected plasma input function, therefore the interpretation of the associated kinetic parameter estimates should be done with caution.

Automated extraction of the arterial input function from brain images for parametric PET studies

BACKGROUND

Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images.

RESULTS

Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07.

CONCLUSIONS

Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Test-Retest Repeatability of Patlak Slopes versus Standardized Uptake Values for Hypermetabolic Lesions and Normal Organs in an Oncologic PET/CT Population

PURPOSE

We aimed to determine the test-retest repeatability of quantitative metrics based on the Patlak slope (PS) versus the standardized uptake value (SUV) among lesions and normal organs on oncologic [18F]FDG-PET/CT.

PROCEDURES

This prospective, single-center study enrolled adults undergoing standard-of-care oncologic [18F]FDG-PET/CTs. Early (35-50 min post-injection) and late (75-90 min post-injection) SUV and PS images were reconstructed from dynamic whole-body PET data. Repeat imaging occurred within 7 days. Relevant quantitative metrics were extracted from lesions and normal organs. Repeatability was assessed via mean test-retest percent changes [T-RT %Δ], within-subject coefficients of variation (wCVs), and intra-class correlation coefficients (ICCs).

RESULTS

Nine subjects (mean age, 61.7 ± 6.2 years; 6 females) completed the test-retest protocol. Four subjects collectively had 17 [18F]FDG-avid lesions. Lesion wCVs were higher (i.e., worse repeatability) for PS-early-max (16.2%) and PS-early-peak (15.6%) than for SUV-early-max (8.9%) and SUV-early-peak (8.1%), with similar early metric ICCs (0.95-0.98). Lesion wCVs were similar for PS-late-max (8.5%) and PS-late-peak (6.4%) relative to SUV-late-max (9.7%) and SUV-late-peak (7.2%), with similar late metric ICCs (0.93-0.98). There was a significant bias toward higher retest SUV and PS values in the lesion analysis (T-RT %Δ [95% CI]: SUV-late-max, 10.0% [2.6%, 17.0%]; PS-late-max, 20.4% [14.3%, 26.4%]) but not in the normal organ analysis.

CONCLUSIONS

Among [18F]FDG-avid lesions, the repeatability of PS-based metrics is similar to equivalent SUV-based metrics at late post-injection time points, indicating that PS-based metrics may be suitable for tracking response to oncologic therapies. However, further validation is required in light of our study's limitations, including small sample size and bias toward higher retest values for some metrics.

Kinetic Modeling of Brain [18-F]FDG Positron Emission Tomography Time Activity Curves with Input Function Recovery (IR) Method

Accurate positron emission tomography (PET) data quantification relies on high-quality input plasma curves, but venous blood sampling may yield poor-quality data, jeopardizing modeling outcomes. In this study, we aimed to recover sub-optimal input functions by using information from the tail (5th-100th min) of curves obtained through the frequent sampling protocol and an input recovery (IR) model trained with reference curves of optimal shape. Initially, we included 170 plasma input curves from eight published studies with clamp [18F]-fluorodeoxyglucose PET exams. Model validation involved 78 brain PET studies for which compartmental model (CM) analysis was feasible (reference (ref) + training sets). Recovered curves were compared with original curves using area under curve (AUC), max peak standardized uptake value (maxSUV). CM parameters (ref + training sets) and fractional uptake rate (FUR) (all sets) were computed. Original and recovered curves from the ref set had comparable AUC (d = 0.02, not significant (NS)), maxSUV (d = 0.05, NS) and comparable brain CM results (NS). Recovered curves from the training set were different from the original according to maxSUV (d = 3) and biologically plausible according to the max theoretical K1 (53//56). Brain CM results were different in the training set (p < 0.05 for all CM parameters and brain regions) but not in the ref set. FUR showed reductions similarly in the recovered curves of the training and test sets compared to the original curves (p < 0.05 for all regions for both sets). The IR method successfully recovered the plasma inputs of poor quality, rescuing cases otherwise excluded from the kinetic modeling results. The validation approach proved useful and can be applied to different tracers and metabolic conditions.

Patlak Reconstruction Using Dynamic 18 F-FDG PET Imaging for Evaluation of Malignant Liver Tumors : A Comparison of HCC, ICC, and Metastatic Liver Tumors

PURPOSE OF THE REPORT

The aim of this study was to explore the different patterns of dynamic whole-body (D-WB) FDG PET/CT parameters among liver malignancy types as potential diagnostic clues and investigate the association between static and dynamic PET/CT parameters for each tumor histology.

PATIENTS AND METHODS

Seventy-one patients with intrahepatic cholangiocarcinoma (ICC), metastatic liver tumor (MLT), or hepatocellular carcinoma (HCC) who underwent D-WB and static dual-time-point FDG PET/CT were enrolled. We obtained Pearson correlation coefficients between the metabolic rate of FDG (MR FDG ; mg/min/ 100ml) or distribution volume of free FDG (DV FDG , %) and static PET/CT parameters. We compared MR FDG and DV FDG values by tumor type and performed receiver operating characteristic analyses for MR FDG and static images.

RESULTS

A total of 12 ICC, 116 MLT, and 36 HCC lesions were analyzed. MR FDG and DV FDG showed excellent correlation with early (SUV e ) and delayed SUV max (SUV d ) ( r = 0.71~0.97), but DV FDG in the HCC lesions did not ( r = 0.62 and 0.69 for SUV e and SUV d , respectively) ( P < 0.001 for all). HCC lesions showed significantly lower MR FDG (2.43 ± 1.98) and DV FDG (139.95 ± 62.58) than ICC (5.02 ± 3.56, 207.06 ± 97.13) and MLT lesions (4.51 ± 2.47, 180.13 ± 75.58) ( P < 0.01 for all). The optimal MR FDG could differentiate HCC from ICC and MLT with areas under the curve of 0.84 and 0.80, respectively. Metastatic liver tumor lesions showed the widest distribution of MR FDG and DV FDG values but with no significant difference among most primary sites.

CONCLUSIONS

MR FDG was strongly correlated with SUV max in the 3 malignancies and showed utility for differentiating HCC from ICC and MLT. Each tumor type has a different glucose metabolism, and D-WB FDG PET/CT imaging has the potential to visualize those differences.

The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions

OBJECTIVES

Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner.

MATERIALS & METHODS

In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson's correlation and Kruskal-Wallis tests were used to compare parameters.

RESULTS

The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P < 0.01), but this is not the case for the delayed images (P > 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index.

CONCLUSION

The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future.

CLINICAL RELEVANCE

Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

A review of digital PET-CT technology: Comparing performance parameters in SiPM integrated digital PET-CT systems

OBJECTIVE

The objective of this study was to perform a narrative review of digital Positron emission tomography-computed tomography (PET-CT) scanners, focussing on the current development in the technology of optimized crystal size and design, the time of flight (ToF) resolution, sensitivity, and axial field of view (AFOV).

KEY FINDINGS

It was observed that significant developments were carried out on the optimization of scintillation crystal size which results in the improvement of spatial resolution. such developments include the upgrade in the AFOV after the integration of SiPM technology, which results in dynamic parametric imaging acquisition in PET and sensitivity boost. The improvement in ToF resolution and the better ToF resolution values, which result in a boost in adequate sensitivity and signal-to-noise ratio (SNR). Other upgrades include the use of the smallest crystal size of 2.76 × 2.76 mm, and the use of the lowest ToF resolution of 214 ps. The use of the largest AFOV of 194 cm with the highest observed NEMA sensitivity of 225 cps/kBq for the total body PET-CT system.

CONCLUSION

Digital PET-CT systems offer various advantages such as a reduction in radiation dose from injected radiopharmaceuticals doses and the overall PET acquisition time with an improved diagnostic certainty. This is because of the better performance of the SiPM detector. Digital PET-CT also has added benefits of the dynamic acquisition and Patlak modeling capabilities into routine clinical practice with the advancement in higher AFOV PET systems.

IMPLICATION

This will help the users choose the best system during the evaluation of the PET-CT for purchase in clinical and research applications. This review will further help in teaching the latest technology and developments in PET-CT systems.

Impact of patient characteristic factors on the dynamics of liver glucose metabolism: Evaluation of multiparametric imaging with dynamic whole-body 18 F-fluorodeoxyglucose-positron emission tomography

AIMS

To assess the impact of various patient characteristics on the dynamics of liver glucose metabolism using automated multiparametric imaging with whole-body dynamic 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET).

MATERIALS AND METHODS

We retrospectively enrolled 540 patients who underwent whole-body dynamic FDG-PET. Three quantitative indices representing hepatic glucose metabolism [mean standardized uptake value normalized by lean body mass (SULmean), metabolic glucose rate (kinetic index) and distribution volume (DV)] were measured from multiparametric PET images produced automatically based on the Patlak plot model. Patient characteristics including age, sex, body mass index, fasting time, blood glucose level, and the presence of diabetes mellitus (DM) or hepatic steatosis (HS) were collected. We examined the correlations between the characteristic factors and three quantitative indices using multiple regression analysis.

RESULTS

The success rate of kinetic analysis using multiparametric PET imaging was 93.3% (504/540). Hepatic SULmean was significantly correlated with age (p < .001), sex (p < .001) and blood glucose level (p = .002). DV was significantly correlated with age (p = .033), sex (p < .001), body mass index (p = .002), fasting time (p = .043) and the presence of HS (p = .002). The kinetic index was significantly correlated with age (p < .001) and sex (p = .004). In the comparison of the healthy, DM and HS groups, patients with DM had a significantly increased SULmean, whereas patients with HS had a significantly decreased DV.

CONCLUSIONS

Our results showed that liver glucose metabolism was influenced by various patient characteristic factors. Multiparametric FDG-PET imaging can be used to analyse the kinetics of liver glucose metabolism in routine clinical practice.

Challenges and innovations in brain PET analysis of neurodegenerative disorders: a mini-review on partial volume effects, small brain region studies, and reference region selection

Positron Emission Tomography (PET) brain imaging is increasingly utilized in clinical and research settings due to its unique ability to study biological processes and subtle changes in living subjects. However, PET imaging is not without its limitations. Currently, bias introduced by partial volume effect (PVE) and poor signal-to-noise ratios of some radiotracers can hamper accurate quantification. Technological advancements like ultra-high-resolution scanners and improvements in radiochemistry are on the horizon to address these challenges. This will enable the study of smaller brain regions and may require more sophisticated methods (e.g., data-driven approaches like unsupervised clustering) for reference region selection and to improve quantification accuracy. This review delves into some of these critical aspects of PET molecular imaging and offers suggested strategies for improvement. This will be illustrated by showing examples for dopaminergic and cholinergic nerve terminal ligands.

Single time point quantitation of cerebral glucose metabolism by FDG-PET without arterial sampling

BACKGROUND

Until recently, quantitation of the net influx of 2-[18F]fluorodeoxyglucose (FDG) to brain (Ki) and the cerebrometabolic rate for glucose (CMRglc) required serial arterial blood sampling in conjunction with dynamic positron emission tomography (PET) recordings. Recent technical innovations enable the identification of an image-derived input function (IDIF) from vascular structures, but are frequently still encumbered by the need for interrupted sequences or prolonged recordings that are seldom available outside of a research setting. In this study, we tested simplified methods for quantitation of FDG-Ki by linear graphic analysis relative to the descending aorta IDIF in oncology patients examined using a Biograph Vision 600 PET/CT with continuous bed motion (Aarhus) or using a recently installed Biograph Vision Quadra long-axial field-of-view (FOV) scanner (Bern).

RESULTS

Correlation analysis of the coefficients of a tri-exponential decomposition of the IDIFs measured during 67 min revealed strong relationships among the total area under the curve (AUC), the terminal normalized arterial integral (theta(52-67 min)), and the terminal image-derived arterial FDG concentration (Ca(52-67 min)). These relationships enabled estimation of the missing AUC from late recordings of the IDIF, from which we then calculated FDG-Ki in brain by two-point linear graphic analysis using a population mean ordinate intercept and the single late frame. Furthermore, certain aspects of the IDIF data from Aarhus showed a marked age-dependence, which was not hitherto reported for the case of FDG pharmacokinetics.

CONCLUSIONS

The observed interrelationships between pharmacokinetic parameters in the IDIF measured during the PET recording support quantitation of FDG-Ki in brain using a single averaged frame from the interval 52-67 min post-injection, with minimal error relative to calculation from the complete dynamic sequences.

An update on the use of image-derived input functions for human PET studies: new hopes or old illusions?

BACKGROUND

The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome.

MAIN BODY

This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF.

CONCLUSION

Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.

Current and Future Use of Long Axial Field-of-View Positron Emission Tomography/Computed Tomography Scanners in Clinical Oncology

The latest technical development in the field of positron emission tomography/computed tomography (PET/CT) imaging has been the extension of the PET axial field-of-view. As a result of the increased number of detectors, the long axial field-of-view (LAFOV) PET systems are not only characterized by a larger anatomical coverage but also by a substantially improved sensitivity, compared with conventional short axial field-of-view PET systems. In clinical practice, this innovation has led to the following optimization: (1) improved overall image quality, (2) decreased duration of PET examinations, (3) decreased amount of radioactivity administered to the patient, or (4) a combination of any of the above. In this review, novel applications of LAFOV PET in oncology are highlighted and future directions are discussed.

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction

In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.

Generation of Whole-Body FDG Parametric Ki Images from Static PET Images Using Deep Learning

FDG parametric Ki images show great advantage over static SUV images, due to the higher contrast and better accuracy in tracer uptake rate estimation. In this study, we explored the feasibility of generating synthetic Ki images from static SUV ratio (SUVR) images using three configurations of U-Nets with different sets of input and output image patches, which were the U-Nets with single input and single output (SISO), multiple inputs and single output (MISO), and single input and multiple outputs (SIMO). SUVR images were generated by averaging three 5-min dynamic SUV frames starting at 60 minutes post-injection, and then normalized by the mean SUV values in the blood pool. The corresponding ground truth Ki images were derived using Patlak graphical analysis with input functions from measurement of arterial blood samples. Even though the synthetic Ki values were not quantitatively accurate compared with ground truth, the linear regression analysis of joint histograms in the voxels of body regions showed that the mean R2 values were higher between U-Net prediction and ground truth (0.596, 0.580, 0.576 in SISO, MISO and SIMO), than that between SUVR and ground truth Ki (0.571). In terms of similarity metrics, the synthetic Ki images were closer to the ground truth Ki images (mean SSIM = 0.729, 0.704, 0.704 in SISO, MISO and MISO) than the input SUVR images (mean SSIM = 0.691). Therefore, it is feasible to use deep learning networks to estimate surrogate map of parametric Ki images from static SUVR images.

Inter-pass motion correction for whole-body dynamic PET and parametric imaging

Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) has been commonly used in many oncological areas. High-resolution PET permits a three-dimensional analysis of FDG distributions on various lesions in vivo, which can be applied for tissue characterization, risk analysis, and treatment monitoring after chemoradiotherapy and immunotherapy. Metabolic changes can be assessed using the tumor absolute FDG uptake as standardized uptake value (SUV) and metabolic tumor volume (MTV). In addition, tumor heterogeneity assessment can potentially estimate tumor aggressiveness and resistance to chemoradiotherapy. Attempts have been made to quantify intratumoral heterogeneity using radiomics. Recent reports have indicated the clinical feasibility of a dynamic FDG PET-computed tomography (CT) in pilot cohort studies of oncological cases. Dynamic imaging permits the assessment of temporal changes in FDG uptake after administration, which is particularly useful for differentiating pathological from physiological uptakes with high diagnostic accuracy. In addition, several new parameters have been introduced for the in vivo quantitative analysis of FDG metabolic processes. Thus, a four-dimensional FDG PET-CT is available for precise tissue characterization of various lesions. This review introduces various new techniques for the quantitative analysis of FDG distribution and glucose metabolism using a four-dimensional FDG analysis with PET-CT. This elegant study reveals the important role of tissue characterization and treatment strategies in oncology.

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.

Evaluation of Data-Driven Respiration Gating in Continuous Bed Motion in Lung Lesions

Respiration gating is used in PET to prevent image quality degradation due to respiratory effects. In this study, we evaluated a type of data-driven respiration gating for continuous bed motion, OncoFreeze AI, which was implemented to improve image quality and the accuracy of semiquantitative uptake values affected by respiratory motion. Methods: ¹⁸F-FDG PET/CT was performed on 32 patients with lung lesions. Two types of respiration-gated images (OncoFreeze AI with data-driven respiration gating, device-based amplitude-based OncoFreeze with elastic motion compensation) and ungated images (static) were reconstructed. For each image, we calculated SUV and metabolic tumor volume (MTV). The improvement rate (IR) from respiration gating and the contrast-to-noise ratio (CNR), which indicates the improvement in image noise, were also calculated for these indices. IR was also calculated for the upper and lower lobes of the lung. As OncoFreeze AI assumes the presence of respiratory motion, we examined quantitative accuracy in regions where respiratory motion was not present using a ⁶⁸Ge cylinder phantom with known quantitative accuracy. Results: OncoFreeze and OncoFreeze AI showed similar values, with a significant increase in SUV and decrease in MTV compared with static reconstruction. OncoFreeze and OncoFreeze AI also showed similar values for IR and CNR. OncoFreeze AI increased SUV_max by an average of 18% and decreased MTV by an average of 25% compared with static reconstruction. From the IR results, both OncoFreeze and OncoFreeze AI showed a greater IR from static reconstruction in the lower lobe than in the upper lobe. OncoFreeze and OncoFreeze AI increased CNR by 17.9% and 18.0%, respectively, compared with static reconstruction. The quantitative accuracy of the ⁶⁸Ge phantom, assuming a region of no respiratory motion, was almost equal for the static reconstruction and OncoFreeze AI. Conclusion: OncoFreeze AI improved the influence of respiratory motion in the assessment of lung lesion uptake to a level comparable to that of the previously launched OncoFreeze. OncoFreeze AI provides more accurate imaging with significantly larger SUVs and smaller MTVs than static reconstruction.

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.

Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study

BACKGROUND

To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic ¹⁸F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10-60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40-60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon's rank sum test, McNemar's test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables.

RESULTS

Both readers similarly rated 51 scans as positive (scores = 1-3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001).

CONCLUSIONS

Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.

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.

MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET

Inter-frame patient motion introduces spatial misalignment and degrades parametric imaging in whole-body dynamic positron emission tomography (PET). Most current deep learning inter-frame motion correction works consider only the image registration problem, ignoring tracer kinetics. We propose an inter-frame Motion Correction framework with Patlak regularization (MCP-Net) to directly optimize the Patlak fitting error and further improve model performance. The MCP-Net contains three modules: a motion estimation module consisting of a multiple-frame 3-D U-Net with a convolutional long short-term memory layer combined at the bottleneck; an image warping module that performs spatial transformation; and an analytical Patlak module that estimates Patlak fitting with the motion-corrected frames and the individual input function. A Patlak loss penalization term using mean squared percentage fitting error is introduced to the loss function in addition to image similarity measurement and displacement gradient loss. Following motion correction, the parametric images were generated by standard Patlak analysis. Compared with both traditional and deep learning benchmarks, our network further corrected the residual spatial mismatch in the dynamic frames, improved the spatial alignment of Patlak Ki/Vb images, and reduced normalized fitting error. With the utilization of tracer dynamics and enhanced network performance, MCP-Net has the potential for further improving the quantitative accuracy of dynamic PET. Our code is released at https://github.com/gxq1998/MCP-Net.

Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions?

Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB [18F]FDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.

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.

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric Ki and Vb images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.

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.

Non-invasive quantification of cerebral glucose metabolism using Gjedde-Patlak plot and image-derived input function from the aorta

INTRODUCTION

We aimed at evaluating a Gjedde-Patlak plot and non-invasive image-derived input functions (IDIF) from the aorta to quantify cerebral glucose metabolic rate (CMRglc) in comparison to the reference standard based on sampling the arterial input function (AIF).

METHOD

Six healthy subjects received 200 MBq [¹⁸F]FDG simultaneously with the initiation of a three-part dynamic PET recording consisting of a 15 min-recording of the aorta, a 40 min-recording of the brain and finally 2 min-recording of the aorta. Simultaneously, the arterial ¹⁸F concentration was measured via arterial cannulation. Regions of interest were drawn in the aorta and the brain and time-activity curves extracted. The IDIF was obtained by fitting a triple exponential function to the aorta time-activity curve after the initial peak including the late aorta frame, thereby interpolating the arterial blood activity concentration during the brain scan. CMRglc was calculated from Gjedde-Patlak plots using AIF and IDIF, respectively and the predictive value was examined. Results from frontal cortex, insula, hippocampus and cerebellum were compared by paired t-test and agreement between the methods was analyzed by Bland-Altman plot statistics.

RESULTS

There was a strong linear relationship and an excellent agreement between the methods (mean±SD of CMRglcIDIF (μmol 100 g^-1 min^-1), mean difference, mean relative difference, 95% limits of agreement): frontal cortex: 30.8 ± 3.3, 0.5, 2.2%, [-1,6:2.5], insula: 25.4 ± 2.2, 0.4, 2.4%, [-1.4:2.2], hippocampus: 16.9 ± 1.2, 0.4, 3.8%, [-1.1:2.0] and cerebellum: 23.4 ± 1.9, 0.5, 3.1%, [-1.4:2.5]).

CONCLUSION

We found excellent agreement between CMRglc obtained with an IDIF from the aorta and the reference standard with AIF. A non-invasive three-part dynamic [¹⁸F]FDG PET recording is feasible as a non-invasive alternative for reliable quantification of cerebral glucose metabolism in all scanner systems. This is useful in patients with presumed global cerebral changes owing to systemic disease or for the monitoring of treatment effects.

Dynamic whole-body FDG-PET imaging for oncology studies

Introduction

Recent PET/CT systems have improved sensitivity and spatial resolution by smaller PET detectors and improved reconstruction software. In addition, continuous-bed-motion mode is now available in some PET systems for whole-body PET imaging. In this review, we describe the advantages of dynamic whole-body FDG-PET in oncology studies.

Methods

PET–CT imaging was obtained at 60 min after FDG administration. Dynamic whole-body imaging with continuous bed motion in 3 min each with flow motion was obtained over 400 oncology cases. For routine image analysis, these dynamic phases (usually four phases) were summed as early FDG imaging. The image quality of each serial dynamic imaging was visually evaluated. In addition, changes in FDG uptake were analyzed in consecutive dynamic imaging and also in early delayed (90 min after FDG administration) time point imaging (dual-time-point imaging; DTPI). Image interpretation was performed by consensus of two nuclear medicine physicians.

Result

All consecutive dynamic whole-body PET images of 3 min duration had acceptable image quality. Many of the areas with physiologically high FDG uptake had altered uptake on serial images. On the other hand, most of the benign and malignant lesions did not show visual changes on serial images. In the study of 60 patients with suspected colorectal cancer, unchanged uptake was noted in almost all regions with pathologically proved FDG uptake, indicating high sensitivity with high negative predictive value on both serial dynamic imaging and on DTPI. We proposed another application of serial dynamic imaging for minimizing motion artifacts for patients who may be likely to move during PET studies.

Discussion

Dynamic whole-body imaging has several advantages over the static imaging. Serial assessment of changes in FDG uptake over a short period of time is useful for distinguishing pathological from physiological uptake, especially in the abdominal regions. These dynamic PET studies may minimize the need for DPTI. In addition, continuous dynamic imaging has the potential to reduce motion artifacts in patients who are likely to move during PET imaging. Furthermore, kinetic analysis of the FDG distribution in tumor areas has a potential for precise tissue characterization.

Conclusion

Dynamic whole-body FDG-PET imaging permits assessment of serial FDG uptake change which is particularly useful for differentiation of pathological uptake from physiological uptake with high diagnostic accuracy. This imaging can be applied for minimizing motion artifacts. Wide clinical applications of such serial, dynamic whole-body PET imaging is expected in oncological studies in the near future.

Source-to-Target Automatic Rotating Estimation (STARE) - a publicly-available, blood-free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

INTRODUCTION

Full quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (K_i) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with human [¹⁸F]FDG PET scans and assess its performance using simulations.

METHODS

STARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple "target" regions are expressed at once as a function of a "source" region, based on the two-tissue irreversible compartment model, and separates target region K_i from source K_i by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the field of view (FOV) that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final K_i is estimated in each region by averaging the estimates obtained in each source rotation.

RESULTS

In a large dataset of human [¹⁸F]FDG scans (N=69), STARE K_i estimates were correlated with corresponding arterial blood-based K_i estimates (r=0.80), with an overall regression slope of 0.88, and were precisely estimated, as assessed by comparing STARE K_i estimates across several runs of the algorithm (coefficient of variation across runs=6.74 ± 2.48%). In simulations, STARE K_i estimates were largely robust to factors that influence the individualized anchoring used within its algorithm.

CONCLUSION

Through simulations and application to [¹⁸F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of K_i. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

Purpose: To test a short 2-[¹⁸F]Fluoro-2-deoxy-D-glucose (2-[¹⁸F]FDG) PET dynamic acquisition protocol to calculate K_i using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).

Methods: 24 patients with NSCLC who underwent standard dynamic 2-[¹⁸F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), K_i was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain K_i,s using IDIF_Fitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIF_Fitted curves were modeled to assume the value of 2-[¹⁸F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between K_i and K_i,s. Finally, K_i,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.

Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min⁻¹. The LOOCV error was 0.4%. In patients of group 2, the mean values of K_i and K_i,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R² = 0.9970). The Passing–Bablok regression for comparison between K_i and K_i,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).

Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[¹⁸F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional K_i estimation.

Comparison of [11C]UCB-J and [18F]FDG PET in Alzheimer's disease: A tracer kinetic modeling study

[11C]UCB-J PET for synaptic vesicle glycoprotein 2 A (SV2A) has been proposed as a suitable marker for synaptic density in Alzheimer's disease (AD). We compared [11C]UCB-J binding for synaptic density and [18F]FDG uptake for metabolism (correlated with neuronal activity) in 14 AD and 11 cognitively normal (CN) participants. We assessed both absolute and relative outcome measures in brain regions of interest, i.e., K1 or R1 for [11C]UCB-J perfusion, VT (volume of distribution) or DVR to cerebellum for [11C]UCB-J binding to SV2A; and Ki or KiR to cerebellum for [18F]FDG metabolism. [11C]UCB-J binding and [18F]FDG metabolism showed a similar magnitude of reduction in the medial temporal lobe of AD -compared to CN participants. However, the magnitude of reduction of [11C]UCB-J binding in neocortical regions was less than that observed with [18F]FDG metabolism. Inter-tracer correlations were also higher in the medial temporal regions between synaptic density and metabolism, with lower correlations in neocortical regions. [11C]UCB-J perfusion showed a similar pattern to [18F]FDG metabolism, with high inter-tracer regional correlations. In summary, we conducted the first in vivo PET imaging of synaptic density and metabolism in the same AD participants and reported a concordant reduction in medial temporal regions but a discordant reduction in neocortical regions.

Multiparametric cardiac18F-FDG PET in humans: pilot comparison of FDG delivery rate with82Rb myocardial blood flow

Myocardial blood flow (MBF) and flow reserve are usually quantified in the clinic with positron emission tomography (PET) using a perfusion-specific radiotracer (e.g.⁸²Rb-chloride). However, the clinical accessibility of existing perfusion tracers remains limited. Meanwhile,¹⁸F-fluorodeoxyglucose (FDG) is a commonly used radiotracer for PET metabolic imaging without similar limitations. In this paper, we explore the potential of¹⁸F-FDG for myocardial perfusion imaging by comparing the myocardial FDG delivery rateK₁with MBF as determined by dynamic⁸²Rb PET in fourteen human subjects with heart disease. Two sets of FDGK₁were derived from one-hour dynamic FDG scans. One was the original FDGK₁estimates and the other was the correspondingK₁values that were linearly normalized for blood glucose levels. A generalized Renkin-Crone model was used to fit FDGK₁with Rb MBF, which then allowed for a nonlinear extraction fraction correction for converting FDGK₁to MBF. The linear correlation between FDG-derived MBF and Rb MBF was moderate (r= 0.79) before the glucose normalization and became much improved (r> 0.9) after glucose normalization. The extraction fraction of FDG was also similar to that of Rb-chloride in the myocardium. The results from this pilot study suggest that dynamic cardiac FDG-PET with tracer kinetic modeling has the potential to provide MBF in addition to its conventional use for metabolic imaging.

Generation of parametric Ki images for FDG PET using two 5-min scans

PURPOSE

The net uptake rate constant (K_i ) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating K_i images for FDG PET using only two 5-min scans with population-based input function (PBIF).

METHODS

Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single-bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole-body dynamic FDG PET scan in continuous-bed-motion mode. For each subject, a standard K_i image was generated using the complete 0-90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual-time-point K_i image was generated from two 5-min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual-time-point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole-body-imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard K_i (gold standard) and dual-time-point K_i , as well as between standard K_i and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual-time-point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual-time-point K_i estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R² ) between standard K_i and dual-time-point K_i is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard K_i versus dual-time-point K_i is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard K_i versus dual-time-point K_i normalized by the mean of the standard K_i across all subjects for each ROI is smaller than 10%. Using Williams' one-tailed t test, the correlation coefficient (R) between standard K_i and dual-time-point K_i was further compared with that between standard K_i and relative SUV change, for each dual-time-point protocol and for each ROI.

RESULTS

Reliable dual-time-point K_i images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual-time-point K_i estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual-time-point K_i estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate K_i estimation in general. Using the protocol of 20-25 min plus 80-85 or 85-90 min, very high correlations were obtained between standard K_i and dual-time-point K_i (R²  = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm³ from 0. The corresponding correlations were much lower between standard K_i and relative SUV change (R²  = 0.673, 0.684, 0.065, 0.246). Dual-time-point K_i showed a significantly higher quantification accuracy with respect to standard K_i than relative SUV change for all the 36 protocols (p < 0.05 using Williams' one-tailed t test).

CONCLUSIONS

Our proposed approach can obtain reliable K_i images and accurate K_i quantification from dual-time-point scans (5-min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice.

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.

Value of Patlak Ki images from 18F-FDG-PET/CT for evaluation of the relationships between disease activity and clinical events in cardiac sarcoidosis

The association between ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) myocardial uptake and clinical presentations in cardiac sarcoidosis (CS) has not yet been clarified. The Patlak slope, Ki, which represents the rate of ¹⁸F-FDG uptake is a quantitative index of ¹⁸F-FDG metabolism. This study aims to investigate the usefulness of standardized uptake value (SUV) and Patlak Ki images (Ki images) extracted from dynamic ¹⁸F-FDG-PET/CT for evaluating the risk of clinical events (CEs) in CS. The SUV and Ki myocardial images were generated from 30 dynamic ¹⁸F-FDG-PET/CT scans of 21 CS patients. The SUV and Ki images both were rated as positive in 19 scans and negative in 11 scans with the same incidence of CEs which were significantly higher in positive than negative scans [cardiac dysfunction: 78.9% (15/19) vs. 27.2% (3/11); arrhythmic events: 65.5% (10/19) vs. 0% (0/11)]. In 19 positive scans, the three Ki parameters (Ki max, Ki mean and Ki volume) were significantly higher in scans for patients with arrhythmic events than in those without. Logistic regression analysis showed that the Ki volume alone was significantly associated with the risk of arrhythmic events. Our study suggests that Ki images may add value to SUV images for evaluating the risk of CEs in CS patients.