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.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

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.

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.

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.

Spinocerebellar Ataxia 36 is a Frequent Cause of Hereditary Ataxia in Eastern Spain.

Background: Autosomal dominant spinocerebellar ataxia 36 (SCA36) is caused by hexanucleotide repeat expansion in the NOP56 gene. Objectives: To assess frequency, clinical and genetic features of SCA36 in Eastern Spain. Methods: NOP56 expansion was tested in a cohort of undiagnosed cerebellar ataxia families (n = 84). Clinical characterization and haplotype studies were performed. Results: SCA36 was identified in 37 individuals from 16 unrelated families. It represented 5.4% of hereditary ataxia patients. The majority were originally from the same region and displayed a shared haplotype. Mean age at onset was 52.5 years. Non-ataxic features included: hypoacusis (67.9%), pyramidal signs (46.4%), lingual fasciculations/atrophy (25%), dystonia (17.8%), and parkinsonism with evidence of dopaminergic denervation (10.7%). Conclusions: SCA36 is a frequent cause of hereditary ataxia in Eastern Spain, and is associated with a strong founder effect. SCA36 analysis should be considered prior to other studies, especially in AD presentations. Parkinsonism reported here broadens SCA36 clinical spectrum.

The Role of Imaging Studies in Favor of Possible Cerebellar Multiple System Atrophy: A Case Study.

ABSTRACT: We report a 64-year-old woman whose history started with urinary incontinence and neurological symptoms (cognitive impairment, dysarthria, and gait difficulties). The 18F-FDG PET/CT showed hypometabolism of the whole cerebellum. Then 6 months later, she developed tremor, postural instability, and ataxia, so she was hospitalized to complete study. Blood tests (antibodies, vitamin B12, copper, genetic test of spinocerebellar ataxia) did not have alterations, but imaging studies, along with clinical symptoms, provide the diagnosis of possible multiple system atrophy.

Pan-Costochondritis Caused by Aspergillus Diagnosed by 18F-FDG PET/CT.

Thoracic pain is an entity that can be difficult to diagnose etiologically. Once the cardiac origin has been ruled out, the rheumatologic, neoplastic, and infectious causes have to be taken into account. We present the case of a patient with atypical chest pain after triple-bypass surgery in whom F-FDG PET/CT scan showed an important uptake of the radiopharmaceutical in costal cartilages, in relation to pan-costochondritis due to Aspergillus.

Intestinal uptake of (99m)Tc-MDP: a case report of protein-losing enteropathy correlated with pathology findings from autopsy.

We present a case of a 60-year-old man with a history of severe hypoproteinemia and constitutional syndrome, suspected to have protein-losing enteropathy (PLE). Bone scintigraphy ((99m)Tc-MDP) performed to rule out the presence of bone metastases incidentally showed abnormal uptake in abdominal soft tissue. The patient unexpectedly died of heart failure, and autopsy revealed microscopic alterations consistent with PLE exclusively in the right colon, corresponding to the area of abnormal uptake. Few similar cases have been published, but none of them reported correlative pathological findings affecting the area of abnormal tracer uptake. In this case of PLE, (99m)Tc-MDP scintigraphy was a useful imaging method for localizing the site of protein loss, showing a focal area of alteration in the right colon. This finding could also have been of great help in case that surgery had been finally performed to control the protein loss.

Effect of transport temperature on the Scantibodies total intact parathyroid hormone immunoradiometric assay using the RIAMAT-280.

BACKGROUND: Inadequate temperature affects the stability of intact parathyroid hormone (i-PTH) kits. Room temperature during transport modifies i-PTH results. METHOD: Percent bound (%B/Bmax) and concentrations (pmol/L) of standards, controls (C1, C2) and pool from eight standard curves were divided into: group I (three curves from kits kept at room temperature for more than 48 h) and group II (five curves from kits kept at 2-8 degrees C) during transport. i-PTH was measured using Scantibodies total i-PTH assay with RIAMAT-280. RESULTS: %B/Bmax for standards, C1 and C2 were significantly higher in group I versus II (P = 0.04). %B/Bmax for the pool were significantly lower in group I (P = 0.001). i-PTH pool concentration in group I was 51% lower (95% confidence interval, 47-53%, P = 0.001); differences were not significant for C1 (P = 0.25) and C2 (P = 0.57) in both groups. CONCLUSION: Room temperature on i-PTH kit during transport alters the standard curve, resulting in a decrease in i-PTH. Using a pool as internal quality control allows the detection of these changes not detected by kit controls.