Clinical feasibility study of early 30-minute dynamic FDG-PET scanning protocol for patients with lung lesions.

PURPOSE: This study aimed to evaluate the clinical feasibility of early 30-minute dynamic 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) scanning protocol for patients with lung lesions in comparison to the standard 65-minute dynamic FDG-PET scanning as a reference. METHODS: Dynamic 18F-FDG PET images of 146 patients with 181 lung lesions (including 146 lesions confirmed by histology) were analyzed in this prospective study. Dynamic images were reconstructed into 28 frames with a specific temporal division protocol for the scan data acquired 65 min post-injection. Ki images and quantitative parameters Ki based on two different acquisition durations [the first 30 min (Ki-30 min) and 65 min (Ki-65 min)] were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. The two acquisition durations were compared for Ki image quality (including visual score analysis and number of lesions detected) and Ki value (including accuracy of Ki, the value of differential diagnosis of lung lesions and prediction of PD-L1 status) by Wilcoxon's rank sum test, Spearman's rank correlation analysis, receiver operating characteristic (ROC) curve, and the DeLong test. The significant testing level (alpha) was set to 0.05. RESULTS: The quality of the Ki-30 min images was not significantly different from the Ki-65 min images based on visual score analysis (P > 0.05). In terms of Ki value, among 181 lesions, Ki-65 min was statistically higher than Ki-30 min (0.027 ± 0.017 ml/g/min vs. 0.026 ± 0.018 ml/g/min, P < 0.05), while a very high correlation was obtained between Ki-65 min and Ki-30 min (r = 0.977, P < 0.05). In the differential diagnosis of lung lesions, ROC analysis was performed on 146 histologically confirmed lesions, the area under the curve (AUC) of Ki-65 min, Ki-30 min, and SUVmax was 0.816, 0.816, and 0.709, respectively. According to the Delong test, no significant differences in the diagnostic accuracies were found between Ki-65 min and Ki-30 min (P > 0.05), while the diagnostic accuracies of Ki-65 min and Ki-30 min were both significantly higher than that of SUVmax (P < 0.05). In 73 (NSCLC) lesions with definite PD-L1 expression results, the Ki-65 min, Ki-30 min, and SUVmax in PD-L1 positivity were significantly higher than that in PD-L1 negativity (P < 0.05). And no significant differences in predicting PD-L1 positivity were found among Ki-65 min, Ki-30 min, and SUVmax (AUC = 0.704, 0.695, and 0.737, respectively, P > 0.05), according to the results of ROC analysis and Delong test. CONCLUSIONS: This study indicates that an early 30-minute dynamic FDG-PET acquisition appears to be sufficient to provide quantitative images with good-quality and accurate Ki values for the assessment of lung lesions and prediction of PD-L1 expression. Protocols with a shortened early 30-minute acquisition time may be considered for patients who have difficulty with prolonged acquisitions to improve the efficiency of clinical acquisitions.

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.

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.