European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.

Methods

We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain (^{18})F-Fluorodeoxyglucose ((^{18})F-FDG) PET scans. The center difference is learned by applying IRMA on PCA-based feature vectors of healthy controls (HC), resulting in a subspace (varvec{V}), representing information not comparable between centers, and the remaining subspace (varvec{U}), where no center differences are present. In this proof-of-concept study, we demonstrate the properties of the method using data from four centers. After center-harmonization, a Generalized Matrix Learning Vector Quantization (GMLVQ) model was trained to discriminate between Parkinson’s disease, Alzheimer’s disease and Dementia with Lewy Bodies.

Results

At the initial IRMA iteration, the system was able to determine the center origin of the four HC cohorts almost perfectly. The method required six iterations, corresponding to a six-dimensional subspace (varvec{V}), to determine the entire center difference. An uncorrected disease classification model was highly biased to center-specific effects, creating a falsely inflated performance when applying internal (cross-) validation. The cross-validation performance of the center-harmonized model remained high, while it generalized significantly better to unseen test cohorts. Furthermore, the framework is highly transparent, providing analytic reconstructions of the correction and visualizations of the data in voxel space.

Conclusion

IRMA can be used to learn and disregard center-specific information in features extracted from brain (^{18})F-FDG PET scans, while retaining disease-specific information.

Purpose

Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MR_SYN) and performing automated quantitative regional analysis using MR_SYN-derived segmentation.

Methods

In this retrospective study, 139 subjects who underwent brain [¹⁸F]FBB PET/CT and T1-weighted MRI were included. A U-Net-like model was trained to translate CT images to MR_SYN; subsequently, a separate model was trained to segment MR_SYN into 95 regions. Regional and composite standardised uptake value ratio (SUVr) was calculated in [¹⁸F]FBB PET images using the acquired ROIs. For evaluation of MR_SYN, quantitative measurements including structural similarity index measure (SSIM) were employed, while for MR_SYN-based segmentation evaluation, Dice similarity coefficient (DSC) was calculated. Wilcoxon signed-rank test was performed for SUVrs computed using MR_SYN and ground-truth MR (MR_GT).

Results

Compared to MR_GT, the mean SSIM of MR_SYN was 0.974 ± 0.005. The MR_SYN-based segmentation achieved a mean DSC of 0.733 across 95 regions. No statistical significance (P > 0.05) was found for SUVr between the ROIs from MR_SYN and those from MR_GT, excluding the precuneus.

Conclusion

We demonstrated a deep learning framework for automated regional brain analysis in PET/CT with MR_SYN. Our proposed framework can benefit patients who have difficulties in performing an MRI scan.

Aim

To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images.

Methods

A 3D residual U-NET model was trained using 8 different tracers (FDG: 75% and non-FDG: 25%) from 11 sites from US, Europe and Asia. A total of 309 training and 33 validation datasets scanned on GE Discovery MI (DMI) ToF scanners were used for development of DLToF models of three strengths: low (L), medium (M) and high (H). The training and validation pairs consisted of target ToF and input non-ToF BSREM reconstructions using site-preferred regularisation parameters (beta values). The contrast and noise properties of each model were defined by adjusting the beta value of target ToF images. A total of 60 DMI datasets, consisting of a set of 4 tracers (¹⁸F-FDG, ¹⁸F-PSMA, ⁶⁸Ga-PSMA, ⁶⁸Ga-DOTATATE) and 15 exams each, were collected for testing and quantitative analysis of the models based on standardized uptake value (SUV) in regions of interest (ROI) placed in lesions, lungs and liver. Each dataset includes 5 image series: ToF and non-ToF BSREM and three DLToF images. The image series (300 in total) were blind scored on a 5-point Likert score by 4 readers based on lesion detectability, diagnostic confidence, and image noise/quality.

Results

In lesion SUV_max quantification with respect to ToF BSREM, DLToF-H achieved the best results among the three models by reducing the non-ToF BSREM errors from -39% to -6% for ¹⁸F-FDG (38 lesions); from -42% to -7% for ¹⁸F-PSMA (35 lesions); from -34% to -4% for ⁶⁸Ga-PSMA (23 lesions) and from -34% to -12% for ⁶⁸Ga-DOTATATE (32 lesions). Quantification results in liver and lung also showed ToF-like performance of DLToF models. Clinical reader resulted showed that DLToF-H results in an improved lesion detectability on average for all four radiotracers whereas DLToF-L achieved the highest scores for image quality (noise level). The results of DLToF-M however showed that this model results in the best trade-off between lesion detection and noise level and hence achieved the highest score for diagnostic confidence on average for all radiotracers.

Conclusion

This study demonstrated that the DLToF models are suitable for both FDG and non-FDG tracers and could be utilized for digital BGO PET/CT scanners to provide an image quality and lesion detectability comparable and close to ToF.

Purpose

The aim of this study was to investigate the radiological and pathological characteristics of false-positive lesions on [⁶⁸Ga]Ga-PSMA-11 PET/CT during the primary staging of prostate cancer.

Methods

This study retrospectively analyzed 216 prostate cancer patients who had [⁶⁸Ga]Ga-PSMA-11 PET/CT before radical prostatectomy. False-positive lesion was defined as suspicious lesion with PRIMARY score ≥ 3 on PET/CT but benign on whole-mount pathology. To analyze the radiological and pathological features of false-positive lesions, no-uptake areas on PSMA PET/CT with benign pathology on the whole-mount specimen were randomly delineated and defined as true-negative lesions. The pathological features of false-positive and true-negative lesions were compared using Fisher’s exact test. The differences of SUVmax and SUVmean between false-positive and true-positive lesions were determined and compared using the Wilcoxon matched-pairs signed-ranks test. In addition, PSMA expression in false-positive lesions was assessed by immunohistochemistry.

Results

A total of 36 false-positive lesions were identified on [⁶⁸Ga]Ga-PSMA-11 PET/CT: 25 (69.44%) were simple atrophy with cyst formation, 7 (19.44%) were prostatic nodular hyperplasia, 3 (8.33%) were inflammation and 1 (2.78%) was normal glands. A comparable number of 36 true-negative lesions were delineated: 21 (58.33%) were normal glands, 8 (22.22%) were simple atrophy with cyst formation, 6 (16.67%) were prostatic nodular hyperplasia, and 1 (2.78%) were inflammation. The Fisher’s exact test revealed a statistically significant difference in the prevalence of simple atrophy with cyst formation between false-positive and true-negative lesions (69.44% vs. 22.22%; P < 0.001). Differences in SUVmax and SUVmean between false-positive and true-positive lesions were also statistically significant (both P < 0.001). PSMA expression in false-positive lesions was confirmed via immunohistochemistry.

Conclusion

This study determined that simple atrophy with cyst formation is a distinctive pathological feature of false-positive lesions on [⁶⁸Ga]Ga-PSMA-11 PET/CT. This observation is likely attributable to the elevated PSMA expression in simple atrophy with cyst formation, as confirmed by histological analysis. Additionally, false-positive lesions were found to have significantly lower SUV compared to true-positive lesions.

Purpose: While many studies have explored the link between biomarkers and cognitive decline in Parkinson's disease (PD), a more comprehensive approach is needed, combining striatal dopamine depletion, cerebral glucose metabolism, and cognitive assessments. In this study, we investigated the relationships between striatal dopamine transporter (DAT) uptake, cerebral glucose hypometabolism, and cognition, as well as the potential progression pattern of these changes in PD.

Methods: We enrolled 62 patients with PD and 33 healthy controls. The subjects underwent N-(3-[¹⁸F]fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl)nortropane (FP-CIT) PET/CT, [¹⁸F] fluorodeoxyglucose (FDG) PET/CT, and detailed neuropsychological testing. The mean standard uptake value ratio (SUVR) value of the regions showing significantly lower metabolism in PD patients was defined as SUVR_[hypo]. The relationship between striatal DAT uptake and SUVR_[hypo] was assessed using general linear models, while their impact on cognitive function was evaluated with multivariate linear regression. Additionally, the pattern of their changes was assessed using an event-based model.

Results: Compared to the control group, PD patients exhibited glucose hypometabolism in specific cortical regions. DAT uptake in the anterior and posterior putamen was positively correlated with SUVR_[hypo]. Decreased DAT uptake in the anterior putamen and caudate nucleus was associated with lower z-score in visuospatial function. Decreased DAT uptake in the posterior and anterior putamen occurred first, followed by PD-related cerebral hypometabolism, and visuospatial function.

Conclusion: This study highlights the interconnectedness of dopaminergic depletion, cerebral glucose hypometabolism, and visuospatial dysfunction, proposing that striatal DAT uptake may serve as an early biomarker for cerebral hypometabolism and cognitive impairment in PD.

Purpose

Carotid artery atherosclerosis, a significant manifestation of cardiovascular disease (CVD) and leading cause of stroke, develops through a gradual process of arterial inflammation and calcification. This study explores the relationship between arterial inflammation (18 F-FDG PET/CT) and vascular calcification (18 F-NaF PET/CT) in the left and right common carotid arteries (LCC/RCC) and their association with CVD and thromboembolic risk in patients with subclinical atherosclerosis.

Methods

A cohort of 115 subjects (73 healthy volunteers, 42 at-risk for CVD) underwent 18 F-NaF and 18 F-FDG PET/CT imaging. Radiotracer uptake was quantitatively assessed by measuring the average blood-pool-corrected mean standardized uptake value (aSUVmean).

Results

Relative to healthy volunteers, at-risk subjects had greater uptake of NaF and FDG (10–22% and 16–27% higher, respectively, in both arteries, p < 0.05). On multivariate regression, NaF aSUVmean correlated with age and BMI (p < 0.01), and FDG aSUVmean correlated with BMI (p ≤ 0.01), fibrinogen (p < 0.01 in LCC only), and total cholesterol (p = 0.02 in RCC only). NaF aSUVmean increased with elevated 10-year CVD risk (p = 0.003 in LCC only), while no significant trend was seen for FDG. NaF and FDG aSUVmean increased with elevated thromboembolic risk in both arteries (p < 0.05). No correlations between NaF and FDG aSUVmean were observed (p > 0.05).

Conclusion

18 F-NaF PET/CT may serve as a prognostic tool for carotid microcalcification and subclinical atherosclerosis, while the utility of 18 F-FDG PET/CT remains uncertain.

Clinical trial registration

“Cardiovascular Molecular Calcification Assessed by 18F-NaF PET CT (CAMONA)”, NCT01724749, https://clinicaltrials.gov/study/NCT01724749.