EEG–fMRI of idiopathic and secondarily generalized epilepsies

We used simultaneous EEG and functional MRI (EEG–fMRI) to study generalized spike wave activity (GSW) in idiopathic and secondary generalized epilepsy (SGE). Recent studies have demonstrated thalamic and cortical fMRI signal changes in association with GSW in idiopathic generalized epilepsy (IGE). We report on a large cohort of patients that included both IGE and SGE, and give a functional interpretation of our findings. Forty-six patients with GSW were studied with EEG–fMRI; 30 with IGE and 16 with SGE. GSW-related BOLD signal changes were seen in 25 of 36 individual patients who had GSW during EEG–fMRI. This was seen in thalamus (60%) and symmetrically in frontal cortex (92%), parietal cortex (76%), and posterior cingulate cortex/precuneus (80%). Thalamic BOLD changes were predominantly positive and cortical changes predominantly negative. Group analysis showed a negative BOLD response in the cortex in the IGE group and to a lesser extent a positive response in thalamus. Thalamic activation was consistent with its known role in GSW, and its detection in individual cases with EEG–fMRI may in part be related to the number and duration of GSW epochs recorded. The spatial distribution of the cortical fMRI response to GSW in both IGE and SGE involved areas of association cortex that are most active during conscious rest. Reduction of activity in these regions during GSW is consistent with the clinical manifestation of absence seizures

Recent Developments in Positron Emission Tomography Tracers for Proteinopathies Imaging in Dementia

An early detection and intervention for dementia represent tremendous unmet clinical needs and priorities in society. A shared feature of neurodegenerative diseases causing dementia is the abnormal accumulation and spreading of pathological protein aggregates, which affect the selective vulnerable circuit in a disease-specific pattern. The advancement in positron emission tomography (PET) biomarkers has accelerated the understanding of the disease mechanism and development of therapeutics for Alzheimer's disease and Parkinson's disease. The clinical utility of amyloid-β PET and the clinical validity of tau PET as diagnostic biomarker for Alzheimer's disease continuum have been demonstrated. The inclusion of biomarkers in the diagnostic criteria has introduced a paradigm shift that facilitated the early and differential disease diagnosis and impacted on the clinical management. Application of disease-modifying therapy likely requires screening of patients with molecular evidence of pathological accumulation and monitoring of treatment effect assisted with biomarkers. There is currently still a gap in specific 4-repeat tau imaging probes for 4-repeat tauopathies and α-synuclein imaging probes for Parkinson's disease and dementia with Lewy body. In this review, we focused on recent development in molecular imaging biomarkers for assisting the early diagnosis of proteinopathies (i.e., amyloid-β, tau, and α-synuclein) in dementia and discussed future perspectives

Towards AI-Assisted Disease Diagnosis: Learning Deep Feature Representations for Medical Image Analysis

Artificial Intelligence (AI) has impacted our lives in many meaningful ways. For our research, we focus on improving disease diagnosis systems by analyzing medical images using AI, specifically deep learning technologies. The recent advances in deep learning technologies are leading to enhanced performance for medical image analysis and computer-aided disease diagnosis. In this dissertation, we explore a major research area in medical image analysis - Image classification. Image classification is the process to assign an image a label from a fixed set of categories. For our research, we focus on the problem of Alzheimer\u27s Disease (AD) diagnosis from 3D structural Magnetic Resonance Imaging (sMRI) and Positron Emission Tomography (PET) brain scans. Alzheimer\u27s Disease is a severe neurological disorder. In this dissertation, we address challenges related to Alzheimer\u27s Disease diagnosis and propose several models for improved diagnosis. We focus on analyzing the 3D Structural MRI (sMRI) and Positron Emission Tomography (PET) brain scans to identify the current stage of Alzheimer\u27s Disease: Normal Controls (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). This dissertation demonstrates ways to improve the performance of a Convolutional Neural Network (CNN) for Alzheimer\u27s Disease diagnosis. Besides, we present approaches to solve the class-imbalance problem and improving classification performance with limited training data for medical image analysis. To understand the decision of the CNN, we present methods to visualize the behavior of a CNN model for disease diagnosis. As a case study, we analyzed brain PET scans of AD and CN patients to see how CNN discriminates among data samples of different classes. Additionally, this dissertation proposes a novel approach to generate synthetic medical images using Generative Adversarial Networks (GANs). Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. Our proposed model can solve such issue and generate brain MRI and PET images for three different stages of Alzheimer\u27s Disease - Normal Control (CN), Mild Cognitive Impairment (MCI), and Alzheimer\u27s Disease (AD). Our proposed approach can be generalized to create synthetic data for other medical image analysis problems and help to develop better disease diagnosis model

Alzheimer’s Dementia : The Emerging Role of Positron Emission Tomography

Open Access via Sage AgreementPeer reviewedPublisher PD

Molecular Imaging of Brain Tumours

This chapter is a review of the most common radiotracers currently used in clinical brain tumour imaging, and an update of future potentially useful radiotracers for imaging brain tumours with positron emission tomography (PET). It will focus mainly on glioma—the most common type of primary brain tumour—and intracranial metastases, as the cause of the majority of morbidity and mortality in neurooncology. Emerging data support the use of somatostatin analogue PET in the treatment planning and surveillance of meningiomas. There is currently a limited role of PET in other non-glial brain neoplasms including neuronal tumours, pineal and pituitary tumours, germ cell tumours and embryonal tumours (PNET, neuroblastoma). Finally, the newest hybrid imaging modality of PET/MRI and the promise it holds for obtaining state-of-the-art structural and functional imaging data simultaneously, are concisely reviewed

Radiolabelled Molecules for Brain Imaging with PET and SPECT

Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are in vivo molecular imaging methods which are widely used in nuclear medicine for diagnosis and treatment follow-up of many major diseases. These methods use target-specific molecules as probes, which are labeled with radionuclides of short half-lives that are synthesized prior to the imaging studies. These probes are called radiopharmaceuticals. The use of PET and SPECT for brain imaging is of special significance since the brain controls all the body’s functions by processing information from the whole body and the outside world. It is the source of thoughts, intelligence, memory, speech, creativity, emotion, sensory functions, motion control, and other important body functions. Protected by the skull and the blood–brain barrier, the brain is somehow a privileged organ with regard to nutrient supply, immune response, and accessibility for diagnostic and therapeutic measures. Invasive procedures are rather limited for the latter purposes. Therefore, noninvasive imaging with PET and SPECT has gained high importance for a great variety of brain diseases, including neurodegenerative diseases, motor dysfunctions, stroke, epilepsy, psychiatric diseases, and brain tumors. This Special Issue focuses on radiolabeled molecules that are used for these purposes, with special emphasis on neurodegenerative diseases and brain tumors

Reconstruction Algorithms for Novel Joint Imaging Techniques in PET

Positron emission tomography (PET) is an important functional in vivo imaging modality with many clinical applications. Its enormously wide range of applications has made both research and industry combine it with other imaging modalities such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI). The general purpose of this work is to study two cases in PET where the goal is to perform image reconstruction jointly on two data types. The first case is the Beta-Gamma image reconstruction. Positron emitting isotopes, such as 11C, 13N, and 18F, can be used to label molecules, and tracers, such as 11CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to low positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (Simultaneous Beta-Gamma Imager). The imager can simultaneously detect positrons (β+) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs the beta and gamma images independently to estimate the thickness component of the beta forward model, and then jointly estimates the radioactivity distribution in the object. We have validated the physics model and the reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the Structural Similarity (SSIM) index for comparing the leaf images from the Simultaneous Beta-Gamma Imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively. The second case is the virtual-pinhole PET technology, which has shown that higher resolution and contrast recovery can be gained by adding a high resolution PET insert with smaller crystals to a conventional PET scanner. Such enhancements are obtained when the insert is placed in proximity of the region of interest (ROI) and in coincidence with the conventional PET scanner. Intuitively, the insert may be positioned within the scanner\u27s axial field-of-view (FOV) and radially closer to the ROI than the scanner\u27s ring. One of the complicating factors of this design is the insert\u27s blocking the scanner\u27s lines-of-response (LORs). Such data may be compensated through attenuation and scatter correction in image reconstruction. However, a potential solution is to place the insert outside of the scanner\u27s axial FOV and to move the body to be in proximity of the insert. We call this imaging strategy the surveillance mode. As the main focus of this work, we have developed an image reconstruction framework for the surveillance mode imaging. The preliminary results show improvement in spatial resolution and contrast recovery. Any improvement in contrast recovery should result in enhancement in tumor detectability, which will be of high clinical significance

Reconstruction Algorithms for Novel Joint Imaging Techniques in PET

Positron emission tomography (PET) is an important functional in vivo imaging modality with many clinical applications. Its enormously wide range of applications has made both research and industry combine it with other imaging modalities such as X-ray computed tomography (CT) or magnetic resonance imaging (MRI). The general purpose of this work is to study two cases in PET where the goal is to perform image reconstruction jointly on two data types. The first case is the Beta-Gamma image reconstruction. Positron emitting isotopes, such as 11C, 13N, and 18F, can be used to label molecules, and tracers, such as 11CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to low positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (Simultaneous Beta-Gamma Imager). The imager can simultaneously detect positrons (β+) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs the beta and gamma images independently to estimate the thickness component of the beta forward model, and then jointly estimates the radioactivity distribution in the object. We have validated the physics model and the reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the Structural Similarity (SSIM) index for comparing the leaf images from the Simultaneous Beta-Gamma Imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively. The second case is the virtual-pinhole PET technology, which has shown that higher resolution and contrast recovery can be gained by adding a high resolution PET insert with smaller crystals to a conventional PET scanner. Such enhancements are obtained when the insert is placed in proximity of the region of interest (ROI) and in coincidence with the conventional PET scanner. Intuitively, the insert may be positioned within the scanner\u27s axial field-of-view (FOV) and radially closer to the ROI than the scanner\u27s ring. One of the complicating factors of this design is the insert\u27s blocking the scanner\u27s lines-of-response (LORs). Such data may be compensated through attenuation and scatter correction in image reconstruction. However, a potential solution is to place the insert outside of the scanner\u27s axial FOV and to move the body to be in proximity of the insert. We call this imaging strategy the surveillance mode. As the main focus of this work, we have developed an image reconstruction framework for the surveillance mode imaging. The preliminary results show improvement in spatial resolution and contrast recovery. Any improvement in contrast recovery should result in enhancement in tumor detectability, which will be of high clinical significance

Computed-Tomography (CT) Scan

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (cross-sectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction