Amyloid brain-dedicated PET images can diagnose Alzheimer's pathology with Centiloid Scale.

PURPOSE: To evaluate whether the Centiloid Scale may be used to diagnose Alzheimer's Disease (AD) pathology effectively with the only use of amyloid PET imaging modality from a brain-dedicated PET scanner. METHODS: This study included 26 patients with amyloid PET images with 3 different radiotracers. All patients were acquired both on a PET/CT and a brain-dedicated PET scanner (CareMiBrain, CMB), from which 4 different reconstructions were implemented. A new pipeline was proposed and used for the PET image analysis based on the original Centiloid Scale processing pipeline, but with only PET images. The Youden's Index was employed to calculate the optimal cutoffs for diagnosis and evaluated by the AUC, accuracy, precision, and recall metrics. RESULTS: The Centiloid Scale (CL) processing pipeline was validated with and without the use of MR images. The CL cutoffs for AD pathology diagnosis on the PET/CT and the 4 CMB reconstructions were 34.4 ±â€¯2.2, 43.5 ±â€¯3.5, 51.9 ±â€¯12.5, 57.5 ±â€¯6.8 and 41.8 ±â€¯1.2 respectively. Overall, for these cutoffs all metrics obtained the maximum score. CONCLUSION: The Centiloid scale applied to PET images allows for AD pathology diagnosis. The CMB scanner can be used with the Centiloid scale to automatically assist in the diagnosis of AD pathology, relieving the large burden of neurodegenerative diseases on a traditional PET/CT.

Relationship between neuroimaging and emotion recognition in mild cognitive impairment patients.

OBJECTIVE: Dementia is a major public health problem with high needs for early detection, efficient treatment, and prognosis evaluation. Social cognition impairment could be an early dementia indicator and can be assessed with emotion recognition evaluation tests. The purpose of this study is to investigate the link between different brain imaging modalities and cognitive status in Mild Cognitive Impairment (MCI) patients, with the goal of uncovering potential physiopathological mechanisms based on social cognition performance. METHODS: The relationship between the Reading the Mind in the Eyes Test (RMET) and some clinical and biochemical variables ([18 F]FDG PET-CT and anatomical MR parameters, neuropsychological evaluation, and CSF biomarkers) was studied in 166 patients with MCI by using a correlational approach. RESULTS: The RMET correlated with neuropsychological variables, as well as with structural and functional brain parameters obtained from the MR and FDG-PET imaging evaluation. However, significant correlations between the RMET and CSF biomarkers were not found. DISCUSSION: Different neuroimaging parameters were found to be related to an emotion recognition task in MCI. This analysis identified potential minimally-invasive biomarkers providing some knowledge about the physiopathological mechanisms in MCI.

Objective Image Quality Comparison Between Brain-Dedicated PET and PET/CT Scanners.

As part of a clinical validation of a new brain-dedicated PET system (CMB), image quality of this scanner has been compared to that of a whole-body PET/CT scanner. To that goal, Hoffman phantom and patient data were obtined with both devices. Since CMB does not use a CT for attenuation correction (AC) which is crucial for PET images quality, this study includes the evaluation of CMB PET images using emission-based or CT-based attenuation maps. PET images were compared using 34 image quality metrics. Moreover, a neural network was used to evaluate the degree of agreement between both devices on the patients diagnosis prediction. Overall, results showed that CMB images have higher contrast and recovery coefficient but higher noise than PET/CT images. Although SUVr values presented statistically significant differences in many brain regions, relative differences were low. An asymmetry between left and right hemispheres, however, was identified. Even so, the variations between the two devices were minor. Finally, there is a greater similarity between PET/CT and CMB CT-based AC PET images than between PET/CT and the CMB emission-based AC PET images.

Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease.

The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy.