Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement.

PURPOSE: Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS: Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS: The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION: The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.

Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

PURPOSE: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space. METHODS: Clinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (- 3 to + 3) grading scheme. RESULTS: The highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland-Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively. CONCLUSION: The noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.

Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy.

PURPOSE: To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. METHODS: Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT , CTVCT ), PET (GTVPET40 , CTVPET40 ), and RFMs (GTVRFM , CTVRFM ,). Intratumoral heterogeneity areas were segmented as GTVPET50-Boost and radiomic boost target volume (RTVBoost ) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. RESULTS: Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM-entropy , GTVRFM-contrast , and GTVRFM-H-correlation with GTVGS , respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM-entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM-contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM-entropy and CTVRFM-contrast with CTVGS , respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost-entropy , RTVBoost-contrast , and RTVBoost-H-correlation , respectively. CONCLUSIONS: FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network.

OBJECTIVE: We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. METHODS: Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). RESULTS: Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were - 1.72 ± 4.22%, 3.75 ± 6.91% and - 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96%) and liver/lung (11.18 ± 3.23%) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42% and 11.69 ± 2.71%, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. CONCLUSION: This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images.

Synthesis, biological evaluation and preclinical study of a novel 99mTc-peptide: A targeting probe of amyloid-ß plaques as a possible diagnostic agent for Alzheimer's disease.

With respect to the main role of amyloid-ß (Aß) plaques as one of the pathological hallmarks in the brain of Alzheimer's patients, the development of new imaging probes for targeted detection of Aß plaques has attracted considerable interests. In this study, a novel cyclopentadienyl tricarbonyl Technetium-99 m (99mTc) agent with peptide scaffold, 99mTc-Cp-GABA-D-(FPLIAIMA)-NH2, for binding to the Aß plaques was designed and successfully synthesized using the Fmoc solid-phase peptide synthesis method. This radiopeptide revealed a good affinity for Aß42 aggregations (Kd = 20 µM) in binding affinity study and this result was confirmed by binding to Aß plaques in brain sections of human Alzheimer's disease (AD) and rat models using in vitro autoradiography, fluorescent staining, and planar scintigraphy. Biodistribution studies of radiopeptide in AD and normal rats demonstrated a moderate initial brain uptake about 0.38 and 0.35% (ID/g) 2 min post-injection, respectively. Whereas, AD rats showed a notable retention time in the brain (0.23% ID/g at 30 min) in comparison with fast clearance in normal rat brains. Normal rats following treatment with cyclosporine A as a p-glycoprotein inhibitor showed a significant increase in the radiopeptide brain accumulation compared to non-treated ones. There was a good correlation between data gathered from single-photon emission computed tomography/computed tomography (SPECT/CT) imaging and biodistribution studies. Therefore, these findings showed that this novel radiopeptide could be a potential SPECT imaging agent for early detection of Aß plaques in the brain of patients with AD.

Design, preparation and biological evaluation of a 177Lu-labeled somatostatin receptor antagonist for targeted therapy of neuroendocrine tumors.

Somatostatin receptor-targeted radionuclide therapy has become an effective treatment in patients with neuroendocrine tumors. Recently, investigations on the development of antagonistic peptides are increasing with possible superior biological properties as opposed to the agonists. Herein, we have reported the development of a new somatostatin receptor peptide ligand labeled with 177Lu to achieve a therapeutic ligand for tumor treatment. The interactions of selected and drown ligands using Avogadro software were docked on somatostatin receptor by Dink algorithm. The best docked peptide-chelator conjugate (DOTA-p-Cl-Phe-Cyclo(d-Cys-l-BzThi-d-Aph-Lys-Thr-Cys)-d-Tyr-NH2) (DOTA-Peptide 2) was synthesized using the Fmoc solid-phase method. DOTA-Peptide 2 was radiolabeled with the 177Lu Trichloride (177LuCl3) solution at 95 °C for 30 min and radiochemical purity (RCP) of 177Lu-DOTA-Peptide 2 solution was monitored by radio-HPLC and radio-TLC procedures. The new radiolabeled peptide was evaluated for stability, receptor binding, internalization, biodistribution and single-photon emission computed tomography (SPECT) imaging using C6 glioma cells and C6 tumor-bearing rats. DOTA-Peptide 2 was obtained with 98% purity and efficiently labeled with 177Lu (RCP > 99%). 177Lu-DOTA-Peptide 2 showed a high value of stability in acetate buffer (91.4% at 312 h) and human plasma (>97% at 24 h). Radioconjugate exhibited low internalization (<5%) and high affinity for somatostatin receptors (Kd = 12.06 nM, Bmax = 0.20 pmol/106 cells) using saturation binding assay. Effective tumor uptake of 7.3% ID/g (percentage of injected dose per gram of tumor) at 4 h post-injection and fast clearance of radiopeptide from blood and other organs led to a high tumor-to-normal organ ratios. SPECT/CT imaging clearly showed the activity localization in tumor. The favorable antagonistic properties of 177Lu-DOTA-Peptide 2 on the somatostatin receptors can make it a suitable candidate for peptide receptor radionuclide therapy (PRRT). In the future study, the therapeutic application of this radiopeptide will be evaluated.

Design, synthesis, radiolabeling and biological evaluation of new urea-based peptides targeting prostate specific membrane antigen.

Early diagnosis of Prostate cancer (PCa) plays a vital role in successful treatment increasing the survival rate of patients. Prostate Specific Membrane Antigen (PSMA) is over-expressed in almost all types of PCa. The goal of present study is to introduce new 99mTc-labeled peptides as a PSMA inhibitor for specific detection of PCa at early stages. Based on published PSMA-targeting compounds, a set of peptides bearing the well-known Glu-Urea-Lys pharmacophore and new non-urea containing pharmacophore were designed and assessed by in silico docking studies. The selected peptides were synthesized and radiolabeled with 99mTc. The in-vitro tests (log P, stability in normal saline and fresh human plasma, and affinity toward PSMA-positive LNCaP cell line) and in-vivo characterizations of radiopeptides (biodistribution and Single Photon Emission Computed Tomography-Computed Tomography (SPECT-CT) imaging in normal and tumour-bearing mice) were performed. The peptides 1-3 containing Glu-Urea-Lys and Glu-GABA-Asp as pharmacophores were efficiently interacted with crystal structure of PSMA and showed the highest binding energies range from -8 to -11.2 kcal/mol. Regarding the saturation binding test, 99mTc-labeled peptide 1 had the highest binding affinity (Kd = 13.58 nM) to PSMA-positive cells. SPECT-CT imaging and biodistribution studies showed high kidneys and tumour uptake 1 h post-injection of radiopeptide 1 and 2 (%ID/g tumour = 3.62 ± 0.78 and 1.8 ± 0.32, respectively). 99mTc-peptide 1 (Glu-urea-Lys-Gly-Ala-Asp-Naphthylalanine-HYNIC-99mTc) exhibited the highest binding affinity, high radiochemical purity, the most stability and high specific accumulation in prostate tumour lesions. 99mTc-peptide 1 being of comparable efficacy and pharmacokinetic properties with the well-known PET tracer (68Ga-PSMA-11) seems to be applied as a promising SPECT imaging agent to early diagnose of PCa and consequently increase survival rate of patients.

Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC).

OBJECTIVE: To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. METHODS: Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued measured attenuation-corrected (MAC) PET images via an encoder-decoder architecture. Image quality was evaluated using various evaluation metrics. Image quantification was assessed for 19 radiomic features in 83 brain regions as delineated using the Hammersmith atlas (n30r83). Reliability of measurements was determined using pixel-wise relative errors (RE; %) for radiomic feature values in reference MAC PET images. RESULTS: Peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) values were 39.2 ± 3.65 and 0.989 ± 0.006 for the external validation set, respectively. RE (%) of SUVmean was - 0.10 ± 2.14 for all regions, and only 3 of 83 regions depicted significant differences. However, the mean RE (%) of this region was 0.02 (range, - 0.83 to 1.18). SUVmax had mean RE (%) of - 3.87 ± 2.84 for all brain regions, and 17 regions in the brain depicted significant differences with respect to MAC images with a mean RE of - 3.99 ± 2.11 (range, - 8.46 to 0.76). Homogeneity amongst Haralick-based radiomic features had the highest number (20) of regions with significant differences with a mean RE (%) of 7.22 ± 2.99. CONCLUSIONS: Direct AC of PET images using deep convolutional encoder-decoder networks is a promising technique for brain PET images. The proposed deep learning method shows significant potential for emission-based AC in PET images with applications in PET/MRI and dedicated brain PET scanners. KEY POINTS: • We demonstrate direct emission-based attenuation correction of PET images without using anatomical information. • We performed radiomics analysis of 83 brain regions to show robustness of direct attenuation correction of PET images. • Deep learning methods have significant promise for emission-based attenuation correction in PET images with potential applications in PET/MRI and dedicated brain PET scanners.

Radiolabeling of Preformed Niosomes with [99mTc]: In Vitro Stability, Biodistribution, and In Vivo Performance.

Nanocarriers radiolabeled with [99mTc] can be used for diagnostic imaging and radionuclide therapy, as well as tracking their pharmacokinetic and biodistribution characteristics. Due to the advantages of niosomes as an ideal drug delivery system, in this study, the radiolabeling procedure of niosomes by [99mTc]-HMPAO complexes was investigated and optimized. Glutathione (GSH)-loaded niosomes were prepared using a thin-film hydration method. To label the niosomes with [99mTc], the preformed GSH-loaded niosomes were incubated with the [99mTc]-HMPAO complex and were characterized for particle size, size distribution, zeta potential, morphology, and radiolabeling efficiency (RE). The effects of GSH concentration, incubation time, incubation temperature, and niosomal composition on RE were investigated. The biodistribution profile and in vivo SPECT/CT imaging of the niosomes and free [99mTc]-HMPAO were also studied. Based on the results, all vesicles had nano-sized structure (160-235 nm) and negative surface charge. Among the different experimental conditions that were tested, including various incubation times, incubation temperatures, and GSH concentrations, the optimum condition that resulted in a RE of 92% was 200-mM GSH and 15-min incubation at 40°C. The in vitro release study in plasma showed that about 20% of radioactivity was released after 24 h, indicating an acceptable radiolabeling stability in plasma. The biodistribution of niosomes was clearly different from the free radiolabel. Niosomes carrying radionuclide were successfully used for tracking the in vivo disposition of these carriers and SPECT/CT imaging in rats. Furthermore, biodistribution studies in tumor-bearing mice revealed higher tumor accumulation of the niosomal formulation as compared with [99mTc]-HMPAO.

Prediction of dose to the relatives of patients treated with radioiodine-131 using neural networks.

In this study, the effective dose received by the family members and caregivers of 52 thyroid cancer patients, who had been treated with radioiodine I-131, was measured to investigate the ability of the neural network to predict the doses to the relatives. The effectiveness of this method to predict the relatives who will receive doses of more than 1 mSv was evaluated. The effective doses were measured by TLD. The inputs of the neural network include 13 different parameters that can potentially affect the dose, and the output was the dose to the family members. The neural networks in this study were feed-forward with a sigmoid activation function and one hidden layer. The mean and median of the measured doses were 0.45 and 0.28 mSv and its range was 0.1-3.64 mSv. The mean square error of the predicted doses by the neural network and the measured doses by TLD (mean squared error) for 99 individuals was 0.142. The optimum neural network was able to predict all the relatives who received doses of more than 1 mSv. The area under the receiver operating characteristic curve for the trained neural network was 0.957, showing its ability to distinguish these groups. Predicting the dose to a patient's relatives before release is a helpful strategy for future optimisation. Using neural networks is a promising method for predicting the dose to the family members and defining high-risk patients and relatives. Patient-specific criteria for release and patient-specific advice and consultation can be used to reduce the dose to each family member.

The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies.

OBJECTIVES: The purpose of this study was to investigate the robustness of different PET/CT image radiomic features over a wide range of different reconstruction settings. METHODS: Phantom and patient studies were conducted, including two PET/CT scanners. Different reconstruction algorithms and parameters including number of sub-iterations, number of subsets, full width at half maximum (FWHM) of Gaussian filter, scan time per bed position and matrix size were studied. Lesions were delineated and one hundred radiomic features were extracted. All radiomics features were categorized based on coefficient of variation (COV). RESULTS: Forty seven percent features showed COV ≤ 5% and 10% of which showed COV > 20%. All geometry based, 44% and 41% of intensity based and texture based features were found as robust respectively. In regard to matrix size, 56% and 6% of all features were found non-robust (COV > 20%) and robust (COV ≤ 5%) respectively. CONCLUSIONS: Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent, and different settings have different effects on different features. Radiomic features with low COV can be considered as good candidates for reproducible tumour quantification in multi-center studies. KEY POINTS: • PET/CT image radiomics is a quantitative approach assessing different aspects of tumour uptake. • Radiomic features robustness is an important issue over different image reconstruction settings. • Variability and robustness of PET/CT image radiomics in advanced reconstruction settings is feature-dependent. • Robust radiomic features can be considered as good candidates for tumour quantification.

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68 Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector with Random Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved by MRMR feature selector + Linear Discriminant Analysis (LDA) and Logistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes.

INTRODUCTION: To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes. METHODS: 131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count. RESULTS: In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05. CONCLUSION: In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens.

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

A step toward simplified dosimetry of radiopharmaceutical therapy via SPECT frame duration reduction.

Despite being time-consuming, SPECT/CT data is necessary for accurate dosimetry in patient-specific radiopharmaceutical therapy. We investigated how reducing the frame duration (FD) during SPECT acquisition can simplify the dosimetry workflow for [177Lu]Lu-PSMA radioligand therapy (RLT). We aimed to determine the impact of shortened acquisition times on dosimetric precision. Three SPECT scans with FD of 20, 10, and 5 second/frame (sec/fr) were obtained 48 h post-RLT from one metastatic castration-resistant prostate cancer (mCRPC) patient's pelvis. Planar images at 4, 48, and 72 h post-therapy were used to calculate time-integrated activities (TIAs). Using accurate activity calibrations and GATE Monte Carlo (MC) dosimetry, absorbed doses in tumor lesions and kidneys were estimated. Dosimetry precision was assessed by comparing shorter FD results to the 20 sec/fr reference using relative percentage difference (RPD). We observed consistent calibration factors (CFs) across different FDs. Using the same CF, we obtained marginal RPD deviations less than 4% for the right kidney and tumor lesions and less than 7% for the left kidney. By reducing FD, simulation time was slightly decreased. This study shows we can shorten SPECT acquisition time in RLT dosimetry by reducing FD without sacrificing dosimetry accuracy. These findings pave the way for streamlined personalized internal dosimetry workflows.

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

PURPOSE: We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy. METHODS: We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data. RESULTS: In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method. CONCLUSIONS: Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR.

BACKGROUND: Prostate-specific membrane antigen (PSMA) PET/CT imaging is widely used for quantitative image analysis, especially in radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC). Unknown features influencing PSMA biodistribution can be explored by analyzing segmented organs at risk (OAR) and lesions. Manual segmentation is time-consuming and labor-intensive, so automated segmentation methods are desirable. Training deep-learning segmentation models is challenging due to the scarcity of high-quality annotated images. Addressing this, we developed shifted windows UNEt TRansformers (Swin UNETR) for fully automated segmentation. Within a self-supervised framework, the model's encoder was pre-trained on unlabeled data. The entire model was fine-tuned, including its decoder, using labeled data. METHODS: In this work, 752 whole-body [68Ga]Ga-PSMA-11 PET/CT images were collected from two centers. For self-supervised model pre-training, 652 unlabeled images were employed. The remaining 100 images were manually labeled for supervised training. In the supervised training phase, 5-fold cross-validation was used with 64 images for model training and 16 for validation, from one center. For testing, 20 hold-out images, evenly distributed between two centers, were used. Image segmentation and quantification metrics were evaluated on the test set compared to the ground-truth segmentation conducted by a nuclear medicine physician. RESULTS: The model generates high-quality OARs and lesion segmentation in lesion-positive cases, including mCRPC. The results show that self-supervised pre-training significantly improved the average dice similarity coefficient (DSC) for all classes by about 3%. Compared to nnU-Net, a well-established model in medical image segmentation, our approach outperformed with a 5% higher DSC. This improvement was attributed to our model's combined use of self-supervised pre-training and supervised fine-tuning, specifically when applied to PET/CT input. Our best model had the lowest DSC for lesions at 0.68 and the highest for liver at 0.95. CONCLUSIONS: We developed a state-of-the-art neural network using self-supervised pre-training on whole-body [68Ga]Ga-PSMA-11 PET/CT images, followed by fine-tuning on a limited set of annotated images. The model generates high-quality OARs and lesion segmentation for PSMA image analysis. The generalizable model holds potential for various clinical applications, including enhanced RLT and patient-specific internal dosimetry.

An Update on the Role of mpMRI and 68Ga-PSMA PET Imaging in Primary and Recurrent Prostate Cancer.

The objective of this work was to review comparisons of the efficacy of 68Ga-PSMA-11 (prostate-specific membrane antigen) PET/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer among patients undergoing initial staging prior to radical prostatectomy or experiencing recurrent prostate cancer, based on histopathological data. A comprehensive search was conducted in PubMed and Web of Science, and relevant articles were analyzed with various parameters, including year of publication, study design, patient count, age, PSA (prostate-specific antigen) value, Gleason score, standardized uptake value (SUVmax), detection rate, treatment history, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and PI-RADS (prostate imaging reporting and data system) scores. Only studies directly comparing PSMA-PET and mpMRI were considered, while those examining combined accuracy or focusing on either modality alone were excluded. In total, 24 studies comprising 1717 patients were analyzed, with the most common indication for screening being staging, followed by relapse. The findings indicated that 68Ga-PSMA-PET/CT effectively diagnosed prostate cancer in patients with suspected or confirmed disease, and both methods exhibited comparable efficacy in identifying lesion-specific information. However, notable heterogeneity was observed, highlighting the necessity for standardization of imaging and histopathology systems to mitigate inter-study variability. Future research should prioritize evaluating the combined diagnostic performance of both modalities to enhance sensitivity and reduce unnecessary biopsies. Overall, the utilization of PSMA-PET and mpMRI in combination holds substantial potential for significantly advancing the diagnosis and management of prostate cancer.

Vimentin-targeted radiopeptide 99m Tc-HYNIC-(tricine/EDDA)-VNTANST: a promising drug for pulmonary fibrosis imaging.

OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) is a fatal disease characterized by the accumulation of extracellular matrix. Because there is no effective treatment for advanced IPF to date, its early diagnosis can be critical. Vimentin is a cytoplasmic intermediate filament that is significantly up-regulated at the surface of fibrotic foci with a crucial role in fibrotic morphological changes. METHODS: In the present study, VNTANST sequence as a known vimentin-targeting peptide was conjugated to hydrazinonicotinic acid (HYNIC) and labeled with 99m Tc. The stability test in saline and human plasma and log P determination were performed. Next, the biodistribution study and single photon emission computed tomography (SPECT) integrated with computed tomography (CT) scanning were performed in healthy and bleomycin-induced fibrosis mice models. RESULTS: The 99m Tc-HYNIC-(tricine/EDDA)-VNTANST showed a hydrophilic nature (log P  = -2.20 ±â€…0.38) and high radiochemical purity > 97% and specific activity (336 Ci/mmol). The radiopeptide was approximately 93% and 86% intact in saline and human plasma within 6 h, respectively. The radiopeptide was substantially accumulated in the pulmonary fibrotic lesions (test vs. control = 4.08 ±â€…0.08% injected dose per gram (ID/g) vs. 0.36 ±â€…0.01% ID/g at 90 min postinjection). SPECT-CT images in fibrosis-bearing mice also indicated the fibrotic foci and kidneys. CONCLUSION: Because there is no available drug for the treatment of advanced pulmonary fibrosis, early diagnosis is the only chance. The 99m Tc-HYNIC-(tricine/EDDA)-VNTANST could be a potential tracer for SPECT imaging of pulmonary fibrosis.