European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

To evaluate right ventricular (RV) uptake measured by quantitative [^99mTc]Tc-DPD SPECT/CT to investigate its role in predicting and evaluating prognosis and therapeutic outcomes in patients with transthyretin amyloid cardiomyopathy (ATTR-CA).

Methods

Patients with ATTR-CA were consecutively enrolled for quantitative [^99mTc]Tc-DPD SPECT/CT. Ventricular amyloid burden was quantified by SUV_max and TBR. Differences in RV uptake (focal or diffuse) and associations with clinical characteristics and CMR data were evaluated. The primary endpoint was major adverse cardiac events (MACEs), including all-cause deaths, heart failure hospitalizations, complete atrioventricular block, sustained ventricular tachycardia, and atrial fibrillation/flutter. Prognostic associations were evaluated using Cox regression and Kaplan-Meier survival analysis. A secondary endpoint involved a longitudinal SPECT/CT analysis during Tafamidis therapy.

Results

The study included 76 patients, all showing both RV and LV uptake on SPECT imaging. Compared with patients with focal RV uptake, patients with diffuse RV uptake had higher serum troponin T levels (P < 0.05), septal thickness (P < 0.01), and external cardiac circulation volume (ECV) (P < 0.05). RV uptake was correlated with septal thickness, ECV, LV uptake, NT-proBNP and troponin-T (all P < 0.05). Among 53 patients, high LV and RV uptake significantly predicted MACEs (P < 0.001), with a median follow-up time of 16 months. A subgroup of 20 patients showed significant reductions in LV and RV uptake after Tafamidis treatment (P < 0.001).

Conclusion

Increasing RV amyloid burden quantified by SPECT/CT is associated with advanced disease stage and predicts MACEs, serving as valuable markers for prognosis and treatment monitoring in ATTR-CA.

Purpose

Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose PET imaging equivalent to transatlantic flight with the assistance of deep learning (DL)-based methods. However, conventional DL approaches face limitations in addressing the heterogeneous domain of PET imaging. This study aims to develop a wavelet-based DL method capable of restoring high-quality imaging from ultra-low-dose PET scans.

Materials and methods

In contrast to conventional DL techniques that denoise images in the spatial domain, we introduce WaveNet, a novel approach that inputs wavelet-decomposed frequency components of PET imaging to perform denoising in the frequency domain. A dataset comprising total-body ¹⁸F -FDG PET images of 1447, acquired using total-body PET scanners including Biograph Vision Quadra (Siemens Healthineers) and uEXPLORER (United Imaging) in Bern and Shanghai, was utilized for developing and testing the proposed method. The quality of enhanced images was assessed using a customized scoring system, which incorporated weighted global physical metrics and local indices.

Results

Our proposed WaveNet consistently outperforms the baseline UNet model across all levels of dose reduction factors (DRF), with greater improvements observed as image quality decreases. Statistical analysis (p < 0.05) and visual inspection validated the superiority of WaveNet. Moreover, WaveNet demonstrated superior generalizability when applied to two cross-scanner datasets (p < 0.05).

Conclusion

WaveNet developed with total-body PET scanners may offer a computational-friendly and robust approach to recover image quality from ultra-low-dose PET imaging. Its adoption may enhance the reliability and clinical acceptance of DL-based dose reduction techniques.

Objective

To investigate the relationship of synaptic loss with glucose metabolism and dopaminergic transporters in Parkinson’s disease (PD) patients.

Methods

A total of 16 patients with PD and 11 age-matched healthy controls underwent positron emission tomography (PET) with the tracers [¹⁸F]SynVesT-1, a ligand for the presynaptic terminal marker synaptic vesicle protein 2 A (SV2A), and FDG. PD patients also underwent PET with the dopamine transporter (DAT) ligand [18F]FP-CIT. The difference in synaptic density between PD patients and age-matched normal controls(NCs) was determined in the selected regions of interest, and the correlations of the [¹⁸F]SynVesT-1 PET SUVRs with [¹⁸F]FP-CIT PET SUVRs and [¹⁸F]FDG PET SUVRs were evaluated.

Results

Compared with that in the NC group, the synaptic density in the caudate region was significantly lower in the PD group (SUVR: 2.51 ± 0.36 vs. 3.18 ± 0.32, p < 0.001), especially in the pre-commissural caudate and post-commissural caudate (SUVR: 2.42 ± 0.29 vs. 2.63 ± 0.32, p < 0.01; 0.76 ± 0.31 vs. 0.97 ± 0.33, p < 0.001). A reduced synaptic density was significantly correlated with DAT (r = 0.61, p < 0.001) and glucose metabolism (r = 0.73, p < 0.001) in the post-commissural caudate. In the post-commissural regions of the caudate, there was a partial mediating effect of synaptic density on the relationship between glucose metabolism and DAT availability (indirect effect: β₄ = 0.039, p = 0.024).

Conclusion

[¹⁸F]SynVesT-1 binds specifically to SV2A, reflecting synaptic density, and there is a positive correlation metabolic pattern related to the changes reflected by [¹⁸F]SynVesT-1 and [¹⁸F]FDG.

Purpose

There is a need for biomarkers in psychiatry to improve diagnosis, prognosis and management, and with confirmed value in follow-up care. Radionuclide imaging, given its molecular imaging characteristics, is well-positioned for translation to the clinic. This systematic review lays the groundwork for integrating PET and SPECT imaging in the clinical management of schizophrenia-spectrum disorders.

Methods

Systematic search of PubMed, Embase, Web of Science and Cochrane library databases was conducted from the earliest date available until February 2024. The focus was on longitudinal studies evaluating PET or SPECT imaging in individuals with a schizophrenia-spectrum or another psychotic disorders. Quality assessment was done using the Newcastle-Ottawa Scale (NOS), NIH scale for before-after studies and Cochrane Risk of Bias tool version 2 (Cochrane RoB2). Studies were further categorised into three groups: preclinical and diagnosis, predicting disease course or personalising treatment.

Results

Fifty-six studies were included in the systematic review investigating in total 1329 patients over a median of 3 months. Over two-thirds used PET tracers, whereas the remaining studies employed SPECT tracers. The most frequently investigated system was dopaminergic transmission, followed by cerebral metabolism and blood flow. [¹⁸F]FDOPA demonstrated large effect size in predicting conversion of subjects at risk and treatment response. Additionally, treatment dosage could be optimised to reduce side effects using [¹²³I]IBZM or [¹¹C]raclopride.

Conclusion

Molecular imaging holds significant promise for real-life application in schizophrenia, with two particularly encouraging avenues being the prediction of conversion/response to antipsychotic medication and the improved management of antipsychotic dosage. Further longitudinal studies and clinical trials will be essential for validating both the clinical effectiveness and economic sustainability, as well as for exploring new applications.

Purpose

Radiolabeled probes targeting prostate-specific membrane antigen (PSMA) have been used in prostate cancer. Moreover, PSMA is also overexpressed on neovessels in hepatocellular carcinoma (HCC). This study aimed to preliminarily evaluate the diagnostic effectiveness of [⁶⁸Ga]Ga-PSMA-617 PET/MRI for HCC.

Methods

Patients suspected of HCC were prospectively enrolled in this single-center study (NCT05006326, 2021-08-16) to perform [⁶⁸Ga]Ga-PSMA-617 PET/MRI, along with contrast enhanced CT (ceCT) or ceMRI. The main suspicious intrahepatic lesions were resected and pathologically verified. Visual evaluation of [⁶⁸Ga]Ga-PSMA-617 PET/MRI images was performed on all lesions. Maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), tumor-to-liver ratio (TLR), tumor-to-blood ratio (TBR), and tumor-to-parotid ratio (TPR) were measured or calculated. The diagnostic efficiency of different modalities was summarized. PSMA expression was evaluated by immunohistochemistry and the correlation of PSMA expression and [⁶⁸Ga]Ga-PSMA-617 uptake in HCC primary tumors was quantitatively analyzed.

Results

A total of 12 patients (ten men and two women; mean age 58.75 ± 12.08 years) were included. Ten patients were diagnosed with HCC, 2 with intrahepatic cholangiocarcinoma (ICC), and 4 with hemangioma. The SUVmax, TLR, TBR, and TPR of HCC primary tumors were higher than those of ICC and hemangioma. The diagnostic accuracy of [⁶⁸Ga]Ga-PSMA-617 PET/MRI for primary HCC was 82.4%. When combined with ceCT or ceMRI, the accuracy increased to 88.2%. A moderate correlation was observed between SUVmax and mean PSMA expression in HCC primary tumors (R = 0.788).

Conclusion

Utilizing a hybrid PET/MRI system to combine [⁶⁸Ga]Ga-PSMA-617 PET/MRI with ceMRI is a promising one-stop solution for the accurate diagnosis of HCC.

Trial Registration

NCT05006326. Registered August 16, 2021, https://clinicaltrials.gov/study/NCT05006326.

Purpose: Dopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [¹¹C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [¹²³I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [¹¹C]CFT PET images to [¹²³I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements.

Methods: A CycleGAN was trained on [¹¹C]CFT PET (n = 602, 72%PD) and [¹²³I]FP-CIT SPECT (n = 1152, 85%PD) images from PD and non-parkinsonian control (NC) subjects. The model generated synthetic SPECT images from a real PET test set (n = 67, 75%PD). Synthetic images were quantitatively and visually evaluated.

Results: Fréchet Inception Distance indicated higher similarity between synthetic and real SPECT than between synthetic SPECT and real PET. A deep learning classification model trained on synthetic SPECT achieved sensitivity of 97.2% and specificity of 90.0% on real SPECT images. Striatal specific binding ratios of synthetic SPECT were not significantly different from real SPECT. The striatal left-right differences and putamen binding ratio were significantly different only in the PD cohort. Real PET and real SPECT had higher contrast-to-noise ratio compared to synthetic SPECT. Visual grading analysis scores showed no significant differences between real and synthetic SPECT, although reduced diagnostic performance on synthetic images was observed.

Conclusion: CycleGAN generated synthetic SPECT images visually indistinguishable from real ones and retained disease-specific information, demonstrating the feasibility of translating [¹¹C]CFT PET to [¹²³I]FP-CIT SPECT. This cross-modality synthesis could enhance further AI classification accuracy, supporting the diagnosis of PD and APS.