European Journal of Nuclear Medicine and Molecular Imaging最新文献

The incidence of Intracranial Metastatic Disease (IMD) continues to increase in part due to improvements in systemic therapy resulting in durable control of extra-cranial disease (ECD). Contrast-enhanced Magnetic Resonance Imaging (CE-MRI) is the preferred method for imaging IMD, but has limitations particularly in follow-up surveillance scans to optimise patient care. We investigate a new diagnostic approach of hybrid ([¹⁸]F]fluoropivalate (FPIA) Positron Emission Tomography-multiparametric MRI (PET-mpMRI), in 12 treatment-naïve and 10 stereotactic radiosurgery (SRS)-treated patients (± combination therapy within 4–8 weeks). High FPIA uptake was seen in all IMD compared to contralateral white matter, regardless of ECD tumour-of-origin (p = 0.0001) and FPIA-PET volumes extended beyond CE-MRI volumes in treatment-naïve but not SRS-treated tumours. Patients with maximum PET Standardised Uptake Value, (SUVmax) ≥ 2.0 showed particularly short overall-survival (median 4 v 15 months, p = 0.0136), while CE-MRI was uninformative regarding outcome; a PET-mpMRI grade-measure also provided non-invasive prediction of overall-survival, warranting larger studies of PET-mpMRI. Independent metabolomics analyses were consistent with shared adaptation of IMD to utilise or accumulate monocarboxylates and acylcarnitines, respectively, providing a common phenotypic basis to FPIA-PET. Reprogrammed monocarboxylate metabolism-related FPIA-PET provides new insights into annotating IMD, to be expounded in future opportunities for therapy decisions for the growing number of cancer patients with IMD [Trial registration reference: Clinicaltrials.gov NCT04807582; 3rd November 2021, retrospectively registered].

Objective

Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However this extended angle increases the number of multiple scatters and the scatter contribution within oblique planes. As scattering affects both quality and quantification of the reconstructed image, it is crucial to correct this effect with more accurate methods than the state-of-the-art single scatter simulation (SSS) that can reach its limits with such an extended field-of-view (FOV). In this work, which is an extension of our previous assessment of deep learning-based scatter estimation (DLSE) carried out on a conventional PET system, we aim to evaluate the DLSE method performance on LAFOV total-body PET.

Approach

The proposed DLSE method based on an convolutional neural network (CNN) U-Net architecture uses emission and attenuation sinograms to estimate scatter sinogram. The network was trained from Monte-Carlo (MC) simulations of XCAT phantoms [(^{text {18}})F]-FDG PET acquisitions using a Siemens Biograph Vision Quadra scanner model, with multiple morphologies and dose distributions. We firstly evaluated the method performance on simulated data in both sinogram and image domain by comparing it to the MC ground truth and SSS scatter sinograms. We then tested the method on seven [(^{text {18}})F]-FDG and [(^{text {18}})F]-PSMA clinical datasets, and compare it to SSS estimations.

Results

DLSE showed superior accuracy on phantom data, greater robustness to patient size and dose variations compared to SSS, and better lesion contrast recovery. It also yielded promising clinical results, improving lesion contrasts in [(^{text {18}})F]-FDG datasets and performing consistently with [(^{text {18}})F]-PSMA datasets despite no training with [(^{text {18}})F]-PSMA.

Significance

LAFOV PET scatter can be accurately estimated from raw data using the proposed DLSE method.

Purpose

Whole-body PET imaging plays an essential role in cancer diagnosis and treatment but suffers from low image quality. Traditional deep learning-based denoising methods work well for a specific acquisition but are less effective in handling diverse PET protocols. In this study, we proposed and validated a 3D Denoising Diffusion Probabilistic Model (3D DDPM) as a robust and universal solution for whole-body PET image denoising.

Methods

The proposed 3D DDPM gradually injected noise into the images during the forward diffusion phase, allowing the model to learn to reconstruct the clean data during the reverse diffusion process. A 3D convolutional network was trained using high-quality data from the Biograph Vision Quadra PET/CT scanner to generate the score function, enabling the model to capture accurate PET distribution information extracted from the total-body datasets. The trained 3D DDPM was evaluated on datasets from four scanners, four tracer types, and six dose levels representing a broad spectrum of clinical scenarios.

Results

The proposed 3D DDPM consistently outperformed 2D DDPM, 3D UNet, and 3D GAN, demonstrating its superior denoising performance across all tested conditions. Additionally, the model’s uncertainty maps exhibited lower variance, reflecting its higher confidence in its outputs.

Conclusions

The proposed 3D DDPM can effectively handle various clinical settings, including variations in dose levels, scanners, and tracers, establishing it as a promising foundational model for PET image denoising. The trained 3D DDPM model of this work can be utilized off the shelf by researchers as a whole-body PET image denoising solution. The code and model are available at https://github.com/Miche11eU/PET-Image-Denoising-Using-3D-Diffusion-Model.

Purpose

In recent years, copper-61 has attracted considerable attention from both physicists and radiochemists due to its favorable physical decay properties for PET imaging and its ease of production at any cyclotron center producing [¹⁸F]FDG. The aim of this study was to evaluate the potential of ⁶¹Cu-based radiopharmaceuticals for PET imaging of NETs, as an alternative to the commonly used gallium-68.

Methods

Copper-61 was produced by irradiation of natural zinc liquid targets, followed by post-processing. In vitro evaluation of ⁶¹Cu- and ⁶⁸Ga-labeled SST analogues was performed in SSTR positive AR42J tumor cells. PET/MRI was carried out in mice bearing AR42J subcutaneous tumors.

Results

High molar activity [⁶¹Cu]Cu-DOTA-TATE and [⁶¹Cu]Cu-NOTA-TATE were successfully prepared with a radiochemical purity of over 95% and were shown to be stable for at least 6 h after the EOS. Both ⁶¹Cu- and ⁶⁸Ga-labeled SST analogues exhibited high cellular uptake, with residual uptake when blocked with an excessive amount of peptide precursor. [⁶¹Cu]Cu-NOTA-TATE showed the highest tumor uptake at 1 h p.i. (13.25 ± 1.86%ID/g) and the tumor-to-non-tumor ratio increased from 1 h to 4 h p.i. At the later time point, tumor visualization improved compared to 1 h p.i. Moreover, preclinical PET/MR images demonstrated that [⁶¹Cu]Cu-NOTA-TATE has a more favorable biodistribution and imaging properties than [⁶¹Cu]Cu-DOTA-TATE, with the extended PET imaging window providing a clear advantage of [⁶¹Cu]Cu-NOTA-TATE over its gallium-68 analogues.

Conclusion

[⁶¹Cu]Cu-NOTA-TATE showed similar biodistribution and pharmacokinetics to [⁶⁸Ga]Ga-DOTA-TATE at 1 h p.i., while demonstrating superior imaging characteristics for late PET imaging. These findings demonstrate that [⁶¹Cu]Cu-NOTA-TATE holds promising characteristics for improving the detection of NETs with increased translational potential.

Purpose

To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve precise MR-CT matching for accurate MRAC evaluation.

Methods

A validation dataset of 21 patients underwent whole-body [¹⁸F]FDG PET/CT followed by [¹⁸F]FDG PET/MR. A flatbed insert ensured consistent positioning, allowing direct comparison of four MRAC methods—four-tissue and five-tissue models with discrete and continuous μ-maps—against CT-based attenuation correction (CTAC). A deep learning-based framework, trained on a dataset of 300 patients, was used to generate synthesized-CTs from MR images, forming the basis for all MRAC methods. Quantitative analyses were conducted at the whole-body, region of interest, and lesion levels, with lesion-distance analysis evaluating the impact of bone proximity on standardized uptake value (SUV) quantification.

Results

Distinct differences were observed among MRAC methods in spine and femur regions. Joint histogram analysis showed MRAC-4 (continuous μ-map) closely aligned with CTAC. Lesion-distance analysis revealed MRAC-4 minimized bone-induced SUV interference (r = 0.01, p = 0.8643). However, tissues prone to bone segmentation interference, such as the spine and liver, exhibited greater SUV variability and lower reproducibility in MRAC-4 compared to MRAC-2 (2D bone segmentation, discrete μ-map) and MRAC-3 (3D bone segmentation, discrete μ-map).

Conclusion

Using a flatbed insert, this study validated MRAC with high precision. Continuous μ-value MRAC method (MRAC-4) demonstrated superior accuracy and minimized bone-related SUV errors but faced challenges in reproducibility, particularly in bone-rich regions.

Purpose

The relevance of repetitive [^99mTc]Tc-DPD scintigraphy in wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) remains unclear. We investigated the impact of tafamidis on cardiac [^99mTc]Tc-DPD uptake, clinical, and laboratory markers at 6 and 12 months, and correlated 12 months [^99mTc]Tc-DPD uptake regression with survival.

Methods

This single-center study enrolled 39 ATTRwt-CM patients. Upon treatment initiation with tafamidis, patients underwent follow-up [^99mTc]Tc-DPD scintigraphy, and clinical and laboratory evaluations at 6 months (n = 6) and 12 months (n = 13), or both (n = 20).

Results

Tafamidis resulted in a significant decline in Perugini score (6 months p = 0.008, 12 months p < 0.001), and (semi-)quantitative [^99mTc]Tc-DPD uptake (total cardiac uptake: baseline 816 [522–933] cps, vs. 6 months 634 [502–734] cps, p = 0.003, vs. 12 months 523 [108–754] cps, p = 0.001). Clinical and laboratory improvements were observed (NYHA: 6 months p = 0.007, 12 months p = 0.033; NT-proBNP: baseline 2586 [1271–5561] ng/L, vs. 6 months 2526 [1109–4786] ng/L, p = 0.016, vs. 12 months 2340 [1411–4749] ng/L, p = 0.012). In Kaplan–Meier analysis, a decrease in right ventricular [^99mTc]Tc-DPD tracer uptake equal to or greater than the median value at 12 months (-30%) was associated with improved survival (log-rank p = 0.021).

Conclusions

Tafamidis in ATTRwt-CM resulted in significant reductions of cardiac [^99mTc]Tc-DPD uptake, NYHA class, and cardiac biomarkers at 6 and 12 months. Regression of right ventricular [^99mTc]Tc-DPD uptake at 12 months was associated with improved survival.

Purpose

The translocator protein 18 kDa (TSPO) is a widely used marker for imaging neuroinflammation via Positron Emission Tomography (PET). However, the vast majority of reported TSPO PET tracers display low binding affinity to a common isoform of human TSPO (rs6971; A147T), making them unsuitable for universal use in the general population. In this study, we have developed and preclinically validated two novel tracers designed to image TSPO in patients of all genotypes.

Methods

Novel analogues of known TSPO ligands were synthesised, evaluated for TSPO binding affinity in vitro (membranes prepared from transfected HEK-293T cells expressing wild-type (WT) or A147T TSPO) and radiolabelled with carbon-11 or fluorine-18. They were evaluated in situ (autoradiography on genotyped human brain tissue) and in vivo (rat, both WT and clinically relevant experimental autoimmune encephalomyelitis (EAE) neuroinflammation model) as potential polymorphism-insensitive TSPO PET tracers.

Results

Two new TSPO ligands, DPA-813 and DPA-814, displayed equivalent single-digit nanomolar binding affinities in vitro towards both human TSPO isoforms. [¹¹C]DPA-813 and [¹⁸F]DPA-814 were synthesised in moderate radiochemical yields, high radiochemical purity, and high molar activity. Autoradiography on human MS tissues showed high specific binding for both tracers, irrespective of the TSPO isoform. The tracers demonstrated high plasma stability after 45 min and no brain metabolism with > 99% intact tracer. Biodistribution in WT animals indicated good brain uptake for both tracers (0.28 and 0.41%ID/g for [¹⁸F]DPA-814 and [¹¹C]DPA-813, respectively). PET imaging in the clinically relevant EAE neuroinflammation model in rats showed significantly higher uptake of [¹¹C]DPA-813 and [¹⁸F]DPA-814 in the spinal cord of the EAE rats compared to the controls.

Conclusion

We have developed two novel PET tracers that display indiscriminately high binding affinity to both common isoforms of human TSPO, show favourable metabolic stability and brain penetration in rats, and significantly higher uptake in the spinal cord of a neuroinflammatory rat model of multiple sclerosis. Going forward, first-in-human clinical validation will mark a critical juncture in the development of these tracers, which could offer substantial improvements over existing imaging tools for detecting neuroinflammation, irrespective of genetic variations.

Introduction

Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosis. This study aims to automatically detect and score ATTR-CM in total body scintigraphy images using deep learning on multi-tracer, multi-scanner, and multi-center datasets.

Methods

In the current study, we employed six datasets (from 12 cameras) for various tasks and purposes. Dataset #1 (93 patients, ^99mTc-MDP) was used to develop the 2D-planar segmentation and localization models. Dataset #2 (216 patients, ^99mTc-DPD) was used for the detection (grade 0 vs. grades 1, 2, and 3) and scoring (0 and 1 vs. grades 2 and 3) of ATTR-CM. Datasets #3 (41 patients, ^99mTc-HDP), #4 (53 patients, ^99mTc-PYP), and #5 (129 patients, ^99mTc-DPD) were used as external centers. ATTR-CM detection and scouring were performed by two physicians in each center. Moreover, Dataset #6 consisting of 3215 patients without labels, was employed for retrospective model performance evaluation. Different regions of interest were cropped and fed into the classification model for the detection and scoring of ATTR-CM. Ensembling was performed on the outputs of different models to improve their performance. Model performance was measured by classification accuracy, sensitivity, specificity, and AUC. Grad-CAM and saliency maps were generated to explain the models’ decision-making process.

Results

In the internal test set, all models for detection and scoring achieved an AUC of more than 0.95 and an F1 score of more than 0.90. For detection in the external dataset, AUCs of 0.93, 0.95, and 1 were achieved for datasets 3, 4, and 5, respectively. For the scoring task, AUCs of 0.95, 0.83, and 0.96 were achieved for these datasets, respectively. In dataset #6, we found ten cases flagged as ATTR-CM by the network. Out of these, four cases were confirmed by a nuclear medicine specialist as possibly having ATTR-CM. GradCam and saliency maps showed that the deep-learning models focused on clinically relevant cardiac areas.

Conclusion

In the current study, we developed and evaluated a fully automated pipeline to detect and score ATTR-CM using large multi-tracer, multi-scanner, and multi-center datasets, achieving high performance on total body images. This fully automated pipeline could lead to more timely and accurate diagnoses, ultimately improving patient outcomes.