Frontiers in nuclear medicine (Lausanne, Switzerland)最新文献

Sydenham's chorea is an autoimmune reaction against cerebral basal ganglia associated with rheumatic fever, caused by group A beta-hemolytic streptococcus infection. Diagnosis of this condition is difficult because of significant delay between infection onset and symptoms presentation, resulting in few positive biological tests or imaging exams. We report the case of a nine-year-old boy exhibiting hemicorporal abnormal movements with tics for whom [¹⁸F]FDG PET/CT exam allowed to make the diagnosis, associated with anti-DNase B elevation. Other biology, spinal tap, EEG and imaging modality like MRI or scanner, were non-contributory.

Purpose: The aims of this study were to evaluate and compare fibroblast activation protein (FAP) expression and localization in surgically resected cholangiocarcinoma (CCA), primary and metastatic hepatocellular carcinoma (HCC), hepatocellular adenoma (HCA), and focal nodular hyperplasia (FNH), and to identify any association between CCA clinical or pathologic features and FAP expression.

Materials and methods: FAP immunostaining from surgically resected CCA (N = 58), primary intrahepatic and extrahepatic metastatic HCC (N = 148), HCA (N26), and FNH (N = 19) was scored (negative, weak positive, moderate positive or strong positive) from tissue microarrays. FAP expression was compared between groups. CCA FAP expression was compared to clinical and tumor pathology features.

Results: Moderate-strong FAP expression in the tumor stroma was present in 93.1% of CCA, 60.7% of extrahepatic metastatic HCC, 29.6% of primary HCC, 21.1% of FNH, and 11.6% of HCA. Moderate-strong FAP expression in tumor stroma was significantly more prevalent in CCA than HCC (p < 0.001), metastatic HCC (p = 0.005), HCA (p < 0.001) and FNH (p < 0.001). FAP was expressed in the stroma of all but one CCA (1.7%), and FAP expression in CCA tumor stroma was not associated with any clinical or tumor pathology features (p > 0.05, all).

Conclusion: FAP is expressed in the stroma of a high proportion (93%) of primary CCA independent of patient clinical or tumor pathology features. As such, these data provide the tissue basis for systematically evaluating FAP as a theranostic target across a broad range of CCA subtypes.

Introduction: In Positron Emission Tomography (PET) imaging, the use of tracers increases radioactive exposure for longitudinal evaluations and in radiosensitive populations such as pediatrics. However, reducing injected PET activity potentially leads to an unfavorable compromise between radiation exposure and image quality, causing lower signal-to-noise ratios and degraded images. Deep learning-based denoising approaches can be employed to recover low count PET image signals: nonetheless, most of these methods rely on structural or anatomic guidance from magnetic resonance imaging (MRI) and fails to effectively preserve global spatial features in denoised PET images, without impacting signal-to-noise ratios.

Methods: In this study, we developed a novel PET only deep learning framework, the Self-SiMilARiTy-Aware Generative Adversarial Framework (SMART), which leverages Generative Adversarial Networks (GANs) and a self-similarity-aware attention mechanism for denoising [18F]-fluorodeoxyglucose (18F-FDG) PET images. This study employs a combination of prospective and retrospective datasets in its design. In total, 114 subjects were included in the study, comprising 34 patients who underwent 18F-Fluorodeoxyglucose PET (FDG) PET imaging for drug-resistant epilepsy, 10 patients for frontotemporal dementia indications, and 70 healthy volunteers. To effectively denoise PET images without anatomical details from MRI, a self-similarity attention mechanism (SSAB) was devised. which learned the distinctive structural and pathological features. These SSAB-enhanced features were subsequently applied to the SMART GAN algorithm and trained to denoise the low-count PET images using the standard dose PET image acquired from each individual participant as reference. The trained GAN algorithm was evaluated using image quality measures including structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), normalized root mean square (NRMSE), Fréchet inception distance (FID), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

Results: In comparison to the standard-dose, SMART-PET had on average a SSIM of 0.984 ± 0.007, PSNR of 38.126 ± 2.631 dB, NRMSE of 0.091 ± 0.028, FID of 0.455 ± 0.065, SNR of 0.002 ± 0.001, and CNR of 0.011 ± 0.011. Regions of interest measurements obtained with datasets decimated down to 10% of the original counts, showed a deviation of less than 1.4% when compared to the ground-truth values.

Discussion: In general, SMART-PET shows promise in reducing noise in PET images and can synthesize diagnostic quality images with a 90% reduction in standard of care injected activity. These results make it a potential candidate for clinical applications in radiosensitive populations and for longitudinal neurological studies.

Purpose: ⁶⁸Ga-Trivehexin is a PET tracer targeting αvβ6-integrin, a transmembrane receptor that is frequently expressed by pancreatic cancer cells. This study aimed to determine the biokinetics, image contrast, and acquisition parameters for ⁶⁸Ga-Trivehexin PET imaging in pancreatic cancers.

Methods: 44 patients with pancreatic cancer underwent Trivehexin PET/CT between June 2021 and November 2022 (EK-242052023). Biokinetics and -distribution were extracted. Previous imaging follow-up imaging, and histological findings were used as reference standards. A one-way ANOVA test, followed by Tukey HSD post-hoc test was conducted. T-tests for subgroups ± chemotherapy prior to PET were performed. Based on dynamic PET data (n = 11) recorded over 45 min, time-activity curves were generated.

Results: ⁶⁸Ga-Trivehexin PET/CT detected 40 pancreatic cancers, SUVmax 12.6; range [5.1-30.8]; 39 liver metastases, SUVmax 7.9 [2.7-16.3]; 21 lymph node metastases, SUVmax 8.6 [2.5-15.0]; 17 peritoneal metastases, SUVmax 9.5 [4.0-16.9] and 14 other metastases, SUVmax 7.2 [2.9-13.1]. Tukey post-hoc analysis revealed significant differences for SUVmax in pancreatic cancer compared to SUVmax in liver metastases [4.74, 95%-CI (1.74, 7.75)], for SUVmax in pancreatic cancer to SUVmax in lymph node metastasis [4.07, 95%-CI (0.47, 7. 67)], for tumor-to-liver ratio (TLR) of liver metastasis to TLR of pancreatic cancer [1.82, 95%-CI (0.83, 2.80)], for TLR of pancreatic cancer to TLR of peritoneal carcinomatoses [-1.88, 95%-CI (-3.15, -0.61)], and TLR of pancreatic cancer to TLR of pleural carcinomatosis [-2.79, 95%-CI (-5.42, -0.18)]. When comparing subgroups ± chemotherapy prior to PET, TLR of pancreatic cancers and TLR of peritoneal carcinomatoses were significantly different. At 45 min p.i., the highest tumor-to-backround (TBR) was observed.

Conclusion: ⁶⁸Ga-Trivehexin is suitable for imaging of αvβ6-integrin expression in pancreatic cancer due to its ability to distinguish primary carcinoma and metastases from background tissue.

Introduction: Neural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermore, the limited availability of samples in neuroscience research can impede the quick adoption of deep learning methods, presenting a challenge for their broader application in neural fingerprinting.

Methods: This study addresses these challenges by using contrastive learning to eliminate the need for retraining with new subjects and developing a data augmentation methodology to enhance model robustness in limited sample size conditions. We utilized the LEMON dataset, comprising 3 Tesla MRI and resting-state fMRI scans from 138 subjects, to compute functional connectivity as a baseline for fingerprinting performance based on correlation metrics. We adapted a recent deep learning model by incorporating data augmentation with short random temporal segments for training and reformulated the fingerprinting task as a contrastive problem, comparing the efficacy of contrastive triplet loss against conventional cross-entropy loss.

Results: The results of this study confirm that deep learning methods can significantly improve fingerprinting performance over correlation-based methods, achieving an accuracy of about 98% in identifying a single subject out of 138 subjects utilizing 39 different functional connectivity profiles.

Discussion: The contrastive method showed added value in the "leave subject out" scenario, demonstrating flexibility comparable to correlation-based methods and robustness across different data sizes. These findings suggest that contrastive learning and data augmentation offer a scalable solution for neural fingerprinting, even with limited sample sizes.

A previously published paper in the official journal of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) concluded that the artificial intelligence chatbot ChatGPT may offer an adequate substitute for nuclear medicine staff informational counseling to patients in an investigated setting of ¹⁸F-FDG PET/CT. To ensure consistency with the previous paper, the author and a team of experts followed a similar methodology and evaluated whether ChatGPT could adequately offer a substitute for nuclear medicine staff informational counseling to patients regarding radiopharmaceutical extravasations. We asked ChatGPT fifteen questions regarding radiopharmaceutical extravasations. Each question or prompt was queried three times. Using the same evaluation criteria as the previously published paper, the ChatGPT responses were evaluated by two nuclear medicine trained physicians and one nuclear medicine physicist for appropriateness and helpfulness. These evaluators found ChatGPT responses to be either highly appropriate or quite appropriate in 100% of questions and very helpful or quite helpful in 93% of questions. The interobserver agreement among the evaluators, assessed using the Intraclass Correlation Coefficient (ICC), was found to be 0.72, indicating good overall agreement. The evaluators also rated the inconsistency across the three ChatGPT responses for each question and found irrelevant or minor inconsistencies in 87% of questions and some differences relevant to main content in the other 13% of the questions. One physician evaluated the quality of the references listed by ChatGPT as the source material it used in generating its responses. The reference check revealed no AI hallucinations. The evaluator concluded that ChatGPT used fully validated references (appropriate, identifiable, and accessible) to generate responses for eleven of the fifteen questions and used generally available medical and ethical guidelines to generate responses for four questions. Based on these results we concluded that ChatGPT may be a reliable resource for patients interested in radiopharmaceutical extravasations. However, these validated and verified ChatGPT responses differed significantly from official positions and public comments regarding radiopharmaceutical extravasations made by the SNMMI and nuclear medicine staff. Since patients are increasingly relying on the internet for information about their medical procedures, the differences need to be addressed.

Objectives: Optimal imaging of ischemic or inflammed myocardium via ¹⁸F-FDG PET imaging requires suppression of background carbohydrate metabolism in normal myocardium. Sole administration of intravenous lipid emulsion has not previously been used to rapidly prepare unfasted patients, such as in emergent clinical situations. In this proof-of-concept pilot, we posited that intravenous fat emulsion suppresses physiological metabolic uptake of in non-ischemic, non-inflammatory myocardium in unprepared and unfasted setting for enhanced cardiac positron emission tomography (PET) imaging.

Methods: We conducted an ethics-approved, single-blind, prospective randomized crossover trial of 10 healthy volunteers from January 2020 to June 2021. Participants were unfasted and rendered hyperglycemic before being administered either high dose intravenous lipid emulsion-1.5 ml kg of 20% lipid emulsion, followed by 15 ml/kg/hr for 30mins-or saline prior to ¹⁸F-FDG injection and subsequent cardiac PET imaging. Assessors undertook image analysis for maximum standard uptake value (SUVmax), minimum standard uptake value (SUVmin) and qualitative assessment, and groups were compared using univariate analysis.

Results: The study population age was 44.5 years [IQR 32.5-56.5], with 50% male and a median BMI of 22.75 [IQR 25.0-28.5] kg/m². The study was feasible and there were no adverse side effects from the interventions. In these participants with normal myocardium, ¹⁸F-FDG uptake was reduced by intravenous lipid emulsion as assessed by SUVmax and qualitative assessment (p = 0.042, r = 0.454 and p = 0.009, r = -0.581, respectively).

Conclusions: Intravenous lipid emulsion suppresses background metabolic uptake of ¹⁸F-FDG even in unprepared and unfasted patients. Our findings prove and expand the possible applications for cardiac ¹⁸F-FDG PET in various settings, including in emergent settings as a means of rapid preparation in place of current more time-consuming standard protocols, allowing time-critical management to be effected.

Hibernomas are rare brown fat tumors that garnered attention in the literature with the increasing use of [¹⁸F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography ([¹⁸F] FDG PET/CT) for the staging workup and follow-up of solid malignancies. Despite being benign tumors, they exhibit high metabolic activity due to their thermogenic nature, leading to significant radiotracer uptake on functional imaging. This can pose a challenge in differentiating them from the malignant lesions, especially the fat-containing malignancies such as liposarcoma. Hibernomas are typically found in the thigh, shoulder, back, and neck. Here, we present a unique case of Hibernoma in a patient undergoing PET/CT for melanoma follow-up in an unusual perihepatic location. To the best of the authors' knowledge, this represents the first reported case of a perihepatic hibernoma in the literature. The report also offers a literature review on hibernomas, including the influence of ambient temperature on their metabolism, diagnostic challenges, management strategies, and reports of hibernomas detected on functional imaging with a range of radiotracers. These observations could serve as a valuable clue in identifying hibernomas, potentially aiding in avoiding unnecessary biopsies or resections.