Annals of Nuclear Medicine最新文献

Purpose: This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in ^99mTc-macroagregated albumin (^99mTc-MAA) SPECT imaging, with the goal of enhancing quantitative accuracy for improved treatment planning and pre-therapy dosimetry in ⁹⁰Y-selctive internal radiation therapy (SIRT).

Materials and methods: Data from 222 patients who underwent ^99mTc-MAA SPECT imaging prior to ⁹⁰Y-SIRT were included in this study. Uncorrected SPECT images (without attenuation and/or scatter correction) were used as input to a modified 3D shifted-window UNet Transformer (Swin UNETR) architecture. Three separate models were trained to predict attenuation corrected (AC), scatter corrected (SC), and joint attenuation and scatter corrected (ASC) SPECT images. The dataset was split into a training set (~ 80%) and an independent test set (~ 20%). Model training was performed using a five-fold cross-validation framework, with final evaluation conducted on the blind test set. To clinically assess model performance, 3D voxel-wise dosimetry was performed on the test set using the local energy deposition method, assuming ^99mTc-MAA as a surrogate for ⁹⁰Y distribution. Quantitative evaluation included organ- and voxel-level metrics, along with Gamma analysis using three combinations of distance-to-agreement (DTA, mm) and dose-difference (DD, %) criteria.

Results: The average (± SD) of the voxel-wise mean error (ME) was ≤ 0.003 Gy for all tasks. The Relative Error (RE (%)) for AC, SC, and ASC tasks were 4.64 ± 7.52%, 8.99 ± 26.35%, and 16.45 ± 25.83%, respectively. Voxel-level Gamma evaluations within the whole body using three different criteria sets, including "DTA: 4.79 mm, DD: 1%"; "DTA: 10 mm, DD: 5%"; and "DTA: 15 mm, DD: 10%" yielded pass rates of over 99.60%. The mean absolute error (MAE) for lesions, normal liver and lungs across all tasks were 3.16 ± 3.39, 0.35 ± 0.36, 0.41 ± 0.47 Gy for AC, 1.97 ± 2.79, 0.19 ± 0.16, 0.22 ± 0.20 Gy, for SC and 5.16 ± 7.10, 0.45 ± 0.51, and 0.34 ± 0.37 Gy for ASC, respectively.

Conclusion: Multiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.

Objective: To determine whether software-based de-filtering can restore the quantitative accuracy of the bone scan index (BSI) and the number of hot spots (HSn) in whole-body scintigraphy images degraded by the Clarity2D noise-reduction filter.

Methods: In this IRB-approved retrospective study, 101 adults (mean age ± SD: 67 ± 13 years) who underwent ^99mTc-HMDP whole-body scintigraphy on a cadmium-zinc-telluride (CZT) SPECT/CT system were analyzed. For each patient, three planar datasets were obtained: (i) unfiltered images, (ii) 40%-blend Clarity2D-filtered images, and (iii) software de-filtered images reconstructed with a deep learning-based inverse filter in VSBONE BSI v3.0. Quantitative indices (BSI and HSn) and lesion masks were automatically extracted. Agreement with the unfiltered reference was evaluated using Pearson correlation, Bland-Altman analysis (bias ± 95% limits), Dice coefficient, and the Hausdorff distance (p < 0.05). Additionally, lesion detection accuracy was quantified using intersection over union (IoU)-based matching to calculate precision, recall, and F1-score.

Results: Clarity2D filtering significantly impaired quantitative concordance (BSI r = 0.23, bias = - 1.55 [-6.20 to 3.10]; HSn r = 0.23, bias = - 14.4 lesions). In contrast, de-filtering restored concordance (BSI r = 0.99, bias = - 0.04 [-0.26 to 0.17]; HSn r = 0.98, bias = - 0.04 lesions) and improved spatial overlap (Dice 0.40 to 0.82) while reducing the median Hausdorff distance from 103 pixels (IQR 85-188) to 39 pixels (IQR 1-40) (all p < 0.001). The de-filtered method demonstrated superior lesion detection accuracy compared to Clarity2D (Precision: 0.77 ± 0.37 vs. 0.19 ± 0.29, Recall: 0.81 ± 0.37 vs. 0.43 ± 0.44, F1-score: 0.78 ± 0.36 vs. 0.23 ± 0.30). Furthermore, de-filtering achieved high inter-case stability (median F1-score: 1.0), whereas Clarity2D showed substantial variability (median F1-score: 0.057).

Conclusions: The proposed de-filtering algorithm reliably reverses Clarity2D-induced distortions, enabling accurate BSI and HSn measurements and robust lesion detection without additional radiation or acquisition time. This technique has the potential to broaden the clinical adoption of noise-reduction filters while preserving the integrity of downstream quantitative analyses.

Background: Quantitative analysis of amyloid positron emission tomography (PET) is increasingly applied in clinical and research settings; however, its consistency across software platforms remains uncertain. This study aimed to compare standardized uptake value ratio (SUVr) measurements obtained from CortexID Suite and VIZCalc, to evaluate their concordance with expert visual assessment, and to assess the concordance of Centiloid values derived from VIZCalc with the visual reference.

Methods: We retrospectively analyzed 116 patients who underwent ¹⁸F-flutemetamol PET at a single institution. SUVr values were calculated using both CortexID Suite and VIZCalc, while Centiloid values were derived from VIZCalc only. Visual assessments were performed by two nuclear medicine physicians. Correlations among indices were examined using Pearson's correlation. Agreement between SUVr values was assessed with Bland-Altman analysis. Agreement with the non-independent visual reference was evaluated using receiver operating characteristic (ROC) analysis, and areas under the curves (AUCs) were compared with DeLong's test.

Results: SUVr values from CortexID and VIZCalc were strongly correlated (r = 0.986, p < 0.001), with a small mean difference of + 0.0397. Both platforms showed high concordance with the non-blinded visual assessment (AUC: 0.991 for CortexID; 0.989 for VIZCalc). Centiloid values also showed high agreement with the visual reference (AUC: 0.994) and were strongly correlated with SUVr values (r = 0.975 for CortexID; r = 0.965 for VIZCalc, p < 0.001). No significant difference was observed between platforms (p = 0.84).

Conclusions: CortexID Suite and VIZCalc demonstrated high concordance with the non-blinded visual assessment and showed consistent quantitative trends. Both platforms can be reliably applied for amyloid burden quantification, provided that software-specific characteristics are appropriately considered.

Purpose: Although papillary thyroid carcinoma (PTC) is generally associated with a favorable prognosis, progression to radioactive iodine-refractory (RAIR) disease in metastatic cases leads to significantly poorer clinical outcomes. This study aimed to analyze the clinical value of clinicopathological, pre-operative ultrasonographic features, and fibroblast activation protein (FAP) immunoreactivity scores for the pretherapeutic prediction of the efficacy of radioiodine (RAI, ¹³¹I) treatment in PTC.

Methods: A retrospective analysis was conducted on the medical records, clinicopathological data, and pre-operative ultrasonographic imaging of 167 PTC patients treated with ¹³¹I (113 clinical complete remission group, 54 RAIR group). Their specimens were collected for FAP immunohistochemical staining and scoring. Statistical analyses were performed to identify RAIR risk factors and a predictive model for RAIR PTC was established.

Results: Binary logistic regression analysis revealed that a maximum tumor diameter of ≥ 17.5 mm, microcalcifications, and a FAP immunoreactivity score of ≥ 3.44 were identified as independent risk factors for RAIR PTC. The combined model showed high sensitivity (75.9%), specificity (77.0%), and accuracy (AUC = 0.812) in the pretherapeutic prediction of ¹³¹I therapeutic efficacy in PTC. Furthermore, calibration curve and decision curve analysis (DCA) confirmed that the combined predictive model exhibited good accuracy and clinical utility.

Conclusion: Clinical significance was observed in the clinicopathological characteristics, ultrasonographic features of PTC and FAP immunoreactivity scores for evaluating ¹³¹I therapeutic efficacy. High sensitivity, specificity, and diagnostic accuracy were achieved when a maximum tumor diameter of ≥ 17.5 mm, microcalcifications, and a FAP immunoreactivity score of ≥ 3.44 were combined for assessment.