Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT.

PURPOSE: The image-derived input function (IDIF) from the descending aorta has demonstrated performance comparable to arterial blood sampling while avoiding its invasive nature in parametric imaging. However, in conventional PET, large vessels may not always be within the imaging field of view (FOV). This study aims to evaluate the efficacy of dynamic parametric Ki imaging using image-derived input functions (IDIFs) extracted from various arteries, facilitated by total-body PET/CT. METHOD: Twenty-three participants underwent a 60-minute total-body [18F]FDG PET scan. Data from each subject were used to reconstruct both total-body PET images and short-axis field-of-view PET images at different bed positions, each with a 25 cm axial field-of-view (AFOV). Partial volume correction (PVC) was performed using the blurred Van Cittert iterative deconvolution. IDIFs extracted from the descending aorta, carotid artery, abdominal aorta, and iliac artery were employed for Patlak analysis. The resulting Ki images were compared using quantification indicators and subjective assessment. Linear regression analysis was conducted to examine the correlation of Ki values among IDIFs in normal organ and lesion regions of interest (ROIs). RESULT: High similarities were observed in Ki images derived from the IDIFs from the descending aorta and other arteries, with a median structural similarity index measure (SSIM) above 0.98 and a median peak signal-to-noise ratio (PSNR) above 37dB. Linear regression analysis revealed strong correlations in Ki values (r² > 0.88) between the descending aorta and the three alternative vessels, with slopes of the linear fits close to 1. No significant difference in lesion detectability among IDIFs was found, as assessed visually and using metrics such as tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) (P < 0.05). CONCLUSION: IDIFs from smaller vessels can reliably reconstruct parametric Ki images without compromising lesion detectability, providing clinically relevant information.

Patient-specific arterial input function for accurate perfusion assessment in intraoperative fluorescence imaging.

Significance: The arterial input function (AIF) plays a crucial role in correcting the time-dependent concentration of the contrast agent within the arterial system, accounting for variations in agent injection parameters (speed, timing, etc.) across patients. Understanding the significance of the AIF can enhance the accuracy of tissue vascular perfusion assessment through indocyanine green-based dynamic contrast-enhanced fluorescence imaging (DCE-FI). Aim: We evaluate the impact of the AIF on perfusion assessment through DCE-FI. Approach: A total of 144 AIFs were acquired from 110 patients using a pulse dye densitometer. Simulation and patient intraoperative imaging were conducted to validate the significance of AIF for perfusion assessment based on kinetic parameters extracted from fluorescence images before and after AIF correction. The kinetic model accuracy was evaluated by assessing the variability of kinetic parameters using individual AIF versus population-based AIF. Results: Individual AIF can reduce the variability in kinetic parameters, and population-based AIF can potentially replace individual AIF for estimating wash-out rate ( k ep ), maximum intensity ( I max ), ingress slope with lower differences compared with those in estimating blood flow, volume transfer constant ( K trans ), and time to peak. Conclusions: Individual AIF can provide the most accurate perfusion assessment compared with assessment without AIF or based on population-based AIF correction.

Accurate brain pharmacokinetic parametric imaging using the blood input function extracted from the cavernous sinus.

Brain pharmacokinetic parametric imaging based on dynamic positron emission tomography (PET) scan is valuable in the diagnosis of brain tumor and neurodegenerative diseases. For short-axis PET system, standard blood input function (BIF) of the descending aorta is not acquirable during the dynamic brain scan. BIF extracted from the intracerebral vascular is inaccurate, making the brain parametric imaging task challenging. This study introduces a novel technique tailored for brain pharmacokinetic parameter imaging in short-axis PET in which the head BIF (hBIF) is acquired from the cavernous sinus. The proposed method optimizes the hBIF within the Patlak model via data fitting, curve correction and Patlak graphical model rewriting. The proposed method was built and evaluated using dynamic PET datasets of 67 patients acquired by uEXPLORER PET/CT, among which 64 datasets were used for data fitting and model construction, and 3 were used for method testing; using cross-validation, a total of 15 patient datasets were finally used to test the model. The performance of the new method was evaluated via visual inspection, root-mean-square error (RMSE) measurements and VOI-based accuracy analysis using linear regression and Person's correlation coefficients (PCC). Compared to directly using the cavernous sinus BIF directly for parameter imaging, the new method achieves higher accuracy in parametric analysis, including the generation of Patlak plots closer to the standard plots, better visual effects and lower RMSE values in the Ki (P = 0.0012) and V (P = 0.0042) images. VOI-based analysis shows regression lines with slopes closer to 1 (P = 0.0019 for Ki ) and smaller intercepts (P = 0.0085 for V). The proposed method is capable of achieving accurate brain pharmacokinetic parametric imaging using cavernous sinus BIF with short-axis PET scan. This may facilitate the application of this imaging technology in the clinical diagnosis of brain diseases.

Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI.

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

Automated Quantitative Image-Derived Input Function for the Estimation of Cerebral Blood Flow Using Oxygen-15-Labelled Water on a Long-Axial Field-of-View PET/CT Scanner.

The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.

Assessment of [18F]PI-2620 Tau-PET Quantification via Non-Invasive Automatized Image Derived Input Function.

PURPOSE: [18F]PI-2620 positron emission tomography (PET) detects misfolded tau in progressive supranuclear palsy (PSP) and Alzheimer's disease (AD). We questioned the feasibility and value of absolute [18F]PI-2620 PET quantification for assessing tau by regional distribution volumes (VT). Here, arterial input functions (AIF) represent the gold standard, but cannot be applied in routine clinical practice, whereas image-derived input functions (IDIF) represent a non-invasive alternative. We aimed to validate IDIF against AIF and we evaluated the potential to discriminate patients with PSP and AD from healthy controls by non-invasive quantification of [18F] PET. METHODS: In the first part of the study, we validated AIF derived from radial artery whole blood against IDIF by investigating 20 subjects (ten controls and ten patients). IDIF were generated by manual extraction of the carotid artery using the average and the five highest (max5) voxel intensity values and by automated extraction of the carotid artery using the average and the maximum voxel intensity value. In the second part of the study, IDIF quantification using the IDIF with the closest match to the AIF was transferred to group comparison of a large independent cohort of 40 subjects (15 healthy controls, 15 PSP patients and 10 AD patients). We compared VT and VT ratios, both calculated by Logan plots, with distribution volume (DV) ratios using simplified reference tissue modelling and standardized uptake value (SUV) ratios. RESULTS: AIF and IDIF showed highly correlated input curves for all applied IDIF extraction methods (0.78 < r < 0.83, all p < 0.0001; area under the curves (AUC): 0.73 < r ≤ 0.82, all p ≤ 0.0003). Regarding the VT values, correlations were mainly found between those generated by the AIF and by the IDIF methods using the maximum voxel intensity values. Lowest relative differences (RD) were observed by applying the manual method using the five highest voxel intensity values (max5) (AIF vs. IDIF manual, avg: RD = -82%; AIF vs. IDIF automated, avg: RD = -86%; AIF vs. IDIF manual, max5: RD = -6%; AIF vs. IDIF automated, max: RD = -26%). Regional VT values revealed considerable variance at group level, which was strongly reduced upon scaling by the inferior cerebellum. The resulting VT ratio values were adequate to detect group differences between patients with PSP or AD and healthy controls (HC) (PSP target region (globus pallidus): HC vs. PSP vs. AD: 1.18 vs. 1.32 vs. 1.16; AD target region (Braak region I): HC vs. PSP vs. AD: 1.00 vs. 1.00 vs. 1.22). VT ratios and DV ratios outperformed SUV ratios and VT in detecting differences between PSP and healthy controls, whereas all quantification approaches performed similarly in comparing AD and healthy controls. CONCLUSION: Blood-free IDIF is a promising approach for quantification of [18F]PI-2620 PET, serving as correlating surrogate for invasive continuous arterial blood sampling. Regional [18F]PI-2620 VT show large variance, in contrast to regional [18F]PI-2620 VT ratios scaled with the inferior cerebellum, which are appropriate for discriminating PSP, AD and healthy controls. DV ratios obtained by simplified reference tissue modeling are similarly suitable for this purpose.

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.

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET.

BACKGROUND: Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. METHODS: 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM). RESULTS: Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P < 2[Formula: see text] with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated ([Formula: see text]= 0.65, P < 2[Formula: see text]) with Logan graphical VT estimation. CONCLUSION: Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to [Formula: see text]in amyloid-positive and amyloid-negative older individuals.

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.

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.

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.

High-Temporal-Resolution Kinetic Modeling of Lung Tumors with Dual-Blood Input Function Using Total-Body Dynamic PET.

The lungs are supplied by both the pulmonary arteries carrying deoxygenated blood originating from the right ventricle and the bronchial arteries carrying oxygenated blood downstream from the left ventricle. However, this effect of dual blood supply has never been investigated using PET, partially because the temporal resolution of conventional dynamic PET scans is limited. The advent of PET scanners with a long axial field of view, such as the uEXPLORER total-body PET/CT system, permits dynamic imaging with high temporal resolution (HTR). In this work, we modeled the dual-blood input function (DBIF) and studied its impact on the kinetic quantification of normal lung tissue and lung tumors using HTR dynamic PET imaging. Methods: Thirteen healthy subjects and 6 cancer subjects with lung tumors underwent a dynamic 18F-FDG scan with the uEXPLORER for 1 h. Data were reconstructed into dynamic frames of 1 s in the early phase. Regional time-activity curves of lung tissue and tumors were analyzed using a 2-tissue compartmental model with 3 different input functions: the right ventricle input function, left ventricle input function, and proposed DBIF, all with time delay and dispersion corrections. These models were compared for time-activity curve fitting quality using the corrected Akaike information criterion and for differentiating lung tumors from lung tissue using the Mann-Whitney U test. Voxelwise multiparametric images by the DBIF model were further generated to verify the regional kinetic analysis. Results: The effect of dual blood supply was pronounced in the high-temporal-resolution time-activity curves of lung tumors. The DBIF model achieved better time-activity curve fitting than the other 2 single-input models according to the corrected Akaike information criterion. The estimated fraction of left ventricle input was low in normal lung tissue of healthy subjects but much higher in lung tumors (∼0.04 vs. ∼0.3, P < 0.0003). The DBIF model also showed better robustness in the difference in 18F-FDG net influx rate [Formula: see text] and delivery rate [Formula: see text] between lung tumors and normal lung tissue. Multiparametric imaging with the DBIF model further confirmed the differences in tracer kinetics between normal lung tissue and lung tumors. Conclusion: The effect of dual blood supply in the lungs was demonstrated using HTR dynamic imaging and compartmental modeling with the proposed DBIF model. The effect was small in lung tissue but nonnegligible in lung tumors. HTR dynamic imaging with total-body PET can offer a sensitive tool for investigating lung diseases.

Can Internal Carotid Arteries Be Used for Noninvasive Quantification of Brain PET Studies?

Because of the limited axial field of view of conventional PET scanners, the internal carotid arteries are commonly used to obtain an image-derived input function (IDIF) in quantitative brain PET. However, time-activity curves extracted from the internal carotids are prone to partial-volume effects due to the limited PET resolution. This study aimed to assess the use of the internal carotids for quantifying brain glucose metabolism before and after partial-volume correction. Methods: Dynamic [18F]FDG images were acquired on a 106-cm-long PET scanner, and quantification was performed with a 2-tissue-compartment model and Patlak analysis using an IDIF extracted from the internal carotids. An IDIF extracted from the ascending aorta was used as ground truth. Results: The internal carotid IDIF underestimated the area under the curve by 37% compared with the ascending aorta IDIF, leading to Ki values approximately 17% higher. After partial-volume correction, the mean relative Ki differences calculated with the ascending aorta and internal carotid IDIFs dropped to 7.5% and 0.05%, when using a 2-tissue-compartment model and Patlak analysis, respectively. However, microparameters (K 1, k 2, k 3) derived from the corrected internal carotid curve differed significantly from those obtained using the ascending aorta. Conclusion: These results suggest that partial-volume-corrected internal carotids may be used to estimate Ki but not kinetic microparameters. Further validation in a larger patient cohort with more variable kinetics is needed for more definitive conclusions.

Evaluation of Non-Invasive Methods for (R)-[11C]PK11195 PET Image Quantification in Multiple Sclerosis.

This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[11C]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.

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.

Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches.

Background and purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) describes tissue microvasculature and has prognostic and predictive potential in radiotherapy for head and neck cancer (HNC). However, lack in standardization of DCE-MRI hinders comparison of studies and clinical implementation. This study investigated the accuracy and robustness of the population arterial input function (AIF), correlations between pharmacokinetic parameters and their association to T stage and human papillomavirus (HPV) status for HNC. Materials and methods: DCE-MRI was acquired for 44 HNC patients. Population AIFs were calculated with six different approaches. DCE-MRI was analysed in primary and lymph node tumours using Tofts model (TM) with population AIFs and individual AIFs, extended TM (ETM) with individual AIFs, Brix model (BM), and areas under the curve (AUCs). Intraclass correlation, concordance correlation, Pearson correlation and Whitney Mann U test helped examining the robustness and accuracy of population AIF, correlations between DCE-MRI parameters and their association to T stage and HPV status, respectively. Results: The population AIF was robust but differed from individual AIFs. There was significant correlation between KtransTM/ETM and ve, TM/ETM, and KtransTM/ETM and Kep, TM/ETM. ABrix and AUCs correlated for lymph nodes. Kep, Brix correlated with ABrix, KtransTM/ETM and Kep, TM/ETM for primary tumours. Kep, TM significantly decreased with increasing T stage. Both the correlations and the parameters' association to T stage were stronger for HPV negative lesions. Conclusions: Individual AIF was preferred for accurate pharmacokinetic modelling of DCE-MRI. DCE-MRI parameters and their correlations were affected by the lesion type, HPV status and T staging.

A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET.

BACKGROUND: In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS: To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS: Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION: The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.

Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography.

Pharmacokinetic positron emission tomography (PET) studies rely on the measurement of the arterial input function (AIF), which represents the time-activity curve of the radiotracer concentration in the blood plasma. Traditionally, obtaining the AIF requires invasive procedures, such as arterial catheterization, which can be challenging, time-consuming, and associated with potential risks. Therefore, the development of non-invasive techniques for AIF measurement is highly desirable. This study presents a detector for the non-invasive measurement of the AIF in PET studies. The detector is based on the combination of scintillation fibers and silicon photomultipliers (SiPMs) which leads to a very compact and rugged device. The feasibility of the detector was assessed through Monte Carlo simulations conducted on mouse tail and human wrist anatomies studying relevant parameters such as energy spectrum, detector efficiency and minimum detectable activity (MDA). The simulations involved the use of 18F and 68Ga isotopes, which exhibit significantly different positron ranges. In addition, several prototypes were built in order to study the different components of the detector including the scintillation fiber, the coating of the fiber, the SiPMs, and the operating configuration. Finally, the simulations were compared with experimental measurements conducted using a tube filled with both 18F and 68Ga to validate the obtained results. The MDA achieved for both anatomies (approximately 1000 kBq/mL for mice and 1 kBq/mL for humans) falls below the peak radiotracer concentrations typically found in PET studies, affirming the feasibility of conducting non-invasive AIF measurements with the fiber detector. The sensitivity for measurements with a tube filled with 18F (68Ga) was 1.2 (2.07) cps/(kBq/mL), while for simulations, it was 2.81 (6.23) cps/(kBq/mL). Further studies are needed to validate these results in pharmacokinetic PET studies.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET.

BACKGROUND: Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. METHODS: Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. RESULTS: Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. CONCLUSIONS: A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.