The relationship between neuropsychological functioning and FDG-PET hypometabolism in intractable mesial temporal lobe epilepsy.

We examined the relationship between baseline neuropsychological functioning and 18-fluorodeoxyglucose positron emission tomography (FDG-PET) in intractable mesial temporal lobe epilepsy (MTLE). We hypothesized relationships between dominant temporal lobe hypometabolism and verbal memory and between nondominant temporal lobe hypometabolism and nonverbal memory in line with the lateralized material-specific model of memory deficits in MTLE. We also hypothesized an association between performance on frontal lobe neuropsychological tests and prefrontal hypometabolism. Thirty-two patients who had undergone temporal lobectomy for treatment of MTLE and who completed both presurgical FDG-PET and comprehensive neuropsychological investigations with widely used standardized measures were included. Age-adjusted composite measures were calculated for verbal memory, nonverbal memory, relative material-specific memory, IQ, executive function, attention/working memory, and psychomotor speed. Fluorodeoxyglucose positron emission tomography was analyzed with statistical parametric mapping (SPM) to identify hypometabolism relative to healthy controls. Pearson's correlation was used to determine the relationship between regions of hypometabolism and neuropsychological functioning. Dominant temporal lobe hypometabolism was associated with relatively inferior verbal memory, while nondominant temporal lobe hypometabolism was associated with inferior nonverbal memory. No relationship was found between performance on any frontal lobe measures and prefrontal hypometabolism. Statistical parametric mapping-quantified lateralized temporal lobe hypometabolism correlates with material-specific episodic memory impairment in MTLE. In contrast, prefrontal hypometabolism is not associated with performance on frontal lobe measures. We suggest that this is because frontal lobe neuropsychology tests may not be good measures of isolated frontal lobe functioning.

The cognitive profile of occipital lobe epilepsy and the selective association of left temporal lobe hypometabolism with verbal memory impairment.

We investigated the cognitive profile of structural occipital lobe epilepsy (OLE) and whether verbal memory impairment is selectively associated with left temporal lobe hypometabolism on [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET). Nine patients with OLE, ages 8-29 years, completed presurgical neuropsychological assessment. Composite measures were calculated for intelligence quotient (IQ), speed, attention, verbal memory, nonverbal memory, and executive functioning. In addition, the Wisconsin Card Sorting Test (WCST) was used as a specific measure of frontal lobe functioning. Presurgical FDG-PET was analyzed with statistical parametric mapping in 8 patients relative to 16 healthy volunteers. Mild impairments were evident for IQ, speed, attention, and executive functioning. Four patients demonstrated moderate or severe verbal memory impairment. Temporal lobe hypometabolism was found in seven of eight patients. Poorer verbal memory was associated with left temporal lobe hypometabolism (p = 0.002), which was stronger (p = 0.03 and p = 0.005, respectively) than the association of left temporal lobe hypometabolism with executive functioning or with performance on the WCST. OLE is associated with widespread cognitive comorbidity, suggesting cortical dysfunction beyond the occipital lobe. Verbal memory impairment is selectively associated with left temporal lobe hypometabolism in OLE, supporting a link between neuropsychological dysfunction and remote hypometabolism in focal epilepsy.

Relationship between preoperative hypometabolism and surgical outcome in neocortical epilepsy surgery.

PURPOSE: Fluorine-18-fluorodeoxyglucose-positron emission tomography (FDG-PET) hypometabolism has been used to localize the epileptogenic zone. However, glucose hypometabolism remote to the ictal focus is common and its relationship to surgical outcome has not been considered in many studies. We investigated the relationship between surgical outcome and FDG-PET hypometabolism topography in a large cohort of patients with neocortical epilepsy. METHODS: We identified all patients (n = 68) who had interictal FDG-PET between 1994 and 2004 and who underwent resective epilepsy surgery with follow up for more than 2 years. The volumes of significant FDG-PET hypometabolism involving the resected epileptic focus and its surrounding regions (perifocal hypometabolism) and those distant to and not contiguous with the perifocal hypometabolism (remote hypometabolism) were determined statistically using Statistical Parametric Mapping (voxel threshold p = 0.01, extent threshold ≥ 250 voxels, uncorrected cluster-level significance p < 0.05) and were compared with magnetic resonance imaging (MRI) and clinical and demographic variables using a multiple logistic regression model to identify independent predictors of seizure outcome. KEY FINDINGS: Remote hypometabolism was present in 39 patients. Seizure freedom was 49% (19 of 39 patients) in patients with glucose hypometabolism remote from the epileptogenic zone compared to 90% (26 of 29 patients) in patients without remote hypometabolism. In 43 patients with an MRI-identified lesion, seizure freedom was 79% (34 of 43 patients). In patients with normal MRI, cortical dysplasia was the predominant pathologic substrate. Multiple logistic regression analysis identified a larger volume of significant remote hypometabolism (p < 0.005) and absence of a MRI-localized lesion (p = 0.006) as independent predictors of continued seizures after surgery. SIGNIFICANCE: In patients with widespread glucose hypometabolism that is statistically significant when compared to controls, epilepsy surgery may not result in complete seizure freedom despite complete removal of the MRI-identified lesion. The volume of significant glucose hypometabolism remote to the ictal-onset zone may be an independent predictor of the success of epilepsy surgery.

PSMA PET-CT in the Diagnosis and Staging of Prostate Cancer.

Prostate cancer is the most common cancer and the second leading cause of cancer death in men. The imaging assessment and treatment of prostate cancer has vastly improved over the past decade. The introduction of PSMA PET-CT has improved the detection of loco-regional and metastatic disease. PSMA PET-CT also has a role in the primary diagnosis and staging, in detecting biochemical recurrence after curative treatment and in metastasis-directed therapy. In this paper we review the role of PSMA PET-CT in prostate cancer.

The topography and significance of extratemporal hypometabolism in refractory mesial temporal lobe epilepsy examined by FDG-PET.

PURPOSE: This study aims to map the temporal and extratemporal 18-fluorodeoxyglucose positron emission tomography (FDG-PET)-defined hypometabolism in mesial temporal lobe epilepsy (MTLE). We hypothesize that quantitative analysis will reveal extensive extratemporal glucose hypometabolism (EH), that the EH is related to seizure propagation beyond the temporal lobe, hypometabolism restricted to one temporal lobe predicts a good outcome following surgery, and EH predicts a poor outcome. METHODS: Sixty-four patients were studied who had undergone temporal lobectomy for intractable MTLE and had at least 2 years of postoperative follow-up. Spatial preprocessing and statistical analysis on preoperative interictal FDG-PET using statistical parametric mapping (SPM 2) identified significant regions of hypometabolism compared to normal controls. The predictors of outcome were determined by univariable and multiple logistic regression analyses. RESULTS: EH was common and widespread, occurring most frequently in the ipsilateral insula and frontal lobe. The extent of EH was not significantly associated with age of onset or the duration of epilepsy. Presence of secondarily generalized tonic--clonic seizures (SGTCS) was associated with a larger extent of remote hypometabolism (RH, p < 0.005). Multiple logistic regression analysis identified the extent of RH and the age at surgery as independent predictors of seizure outcome. DISCUSSION: Our results indicate that RH in MTLE is associated with a poorer surgical outcome, especially if seen in the contralateral hemisphere. The extent of RH relates to SGTCS but not to duration of epilepsy.

Enhanced parameter estimation methods for noisy SPECT data.

Functional imaging with PET and SPECT is capable of visualizing subtle changes in physiological function in vivo, which aids in the early diagnosis of disease. Quantitative functional parameters are usually derived by curve fitting the dynamic data of a functional imaging study. However, the intrinsic high level of noise and low signal to noise ratio can lead to instability in the parameter estimation and give rise to non-physiological parameter estimates. Clustering techniques have been applied to improve signal to noise ratio and the reliability of parametric image generation, but these may enhance partial volume effects (PVE) and result in biased estimates for small structures. Therefore, a systematic study was performed using computer simulations of SPECT data and the generalized linear least square algorithm (GLLS) to evaluate the ability of three proposed enhanced methods and a clustering-aided method to improve the reliability of parametric image generation. The results demonstrate that clustering with sufficient cluster numbers ameliorated PVE and provided noise-insensitive parameter estimates. The enhanced GLLS method with a prior volume of distribution and bootstrap Monte Carlo resampling improved the reliability of the curve fitting, and is thus suitable for application to noisy SPECT data.

Fast and reliable estimation of multiple parametric images using an integrated method for dynamic SPECT.

Dynamic single photon emission computed tomography (SPECT) has demonstrated the potential to quantitatively estimate physiological parameters in the brain and the heart. The generalized linear least square (GLLS) method is a well-established method for solving linear compartment models with fast computational speed. However, the high level of noise intrinsic in the SPECT data leads to reliability and instability problems of GLLS for generating parametric images. An integrated method is proposed to restrict the noise in both the temporal and spatial domains to estimate multiple parametric images for dynamic SPECT. This method comprises three steps which are optimum image sampling schedule in the projection space, cluster analysis applied postreconstruction and parametric image generation with GLLS. The simulation and experimental studies for the neuronal nicotine acetylcholine receptor tracer of 5-[123I]-iodo-A-85380 were employed to evaluate the performance of the proposed method. The results of influx rate of K1 and volume of distribution of Vd demonstrated that the integrated method was successful in generating low noise parametric images for high noise SPECT data without enhancing the partial volume effect. Furthermore, the integrated method is computationally efficient for potential clinical applications.

Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies.

[18F]-Fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) scans of lymphoma patients usually show disease involvement as foci of increased radiotracer uptake. Existing methods for detecting abnormalities, model the characteristics of these foci; this is challenging due to the inconsistent shape and localization information about the lesions. Thresholding the degree of FDG uptake is the standard method to separate different sites of involvement. But may fragment sites into smaller regions, and may also incorrectly identify sites of normal physiological FDG uptake and normal FDG excretion (sFEPU) such as the kidneys, bladder, brain and heart. These sFEPU can obscure sites of abnormal uptake, which can make image interpretation problematic. Identifying sFEPU is therefore important for improving the sensitivity of lesion detection and image interpretation. Existing methods to identify sFEPU are inaccurate because they fail to account for the low inter-class differences between sFEPU fragments and their inconsistent localization information. In this study, we address this issue by using a multi-scale superpixel-based encoding (MSE) to group the individual sFEPU fragments into larger regions, thereby, enabling the extraction of highly discriminative image features via domain transferred convolutional neural networks. We then classify there regions into one of the sFEPU classes using a class-driven feature selection and classification model (CFSC) method that avoids overfitting to the most frequently occurring classes. Our experiments on 40 whole-body lymphoma PET-CT studies show that our method achieved better accuracy (an average F-score of 91.73%) compared to existing methods in the classification of sFEPU.

A graph-based approach for the retrieval of multi-modality medical images.

In this paper, we address the retrieval of multi-modality medical volumes, which consist of two different imaging modalities, acquired sequentially, from the same scanner. One such example, positron emission tomography and computed tomography (PET-CT), provides physicians with complementary functional and anatomical features as well as spatial relationships and has led to improved cancer diagnosis, localisation, and staging. The challenge of multi-modality volume retrieval for cancer patients lies in representing the complementary geometric and topologic attributes between tumours and organs. These attributes and relationships, which are used for tumour staging and classification, can be formulated as a graph. It has been demonstrated that graph-based methods have high accuracy for retrieval by spatial similarity. However, naïvely representing all relationships on a complete graph obscures the structure of the tumour-anatomy relationships. We propose a new graph structure derived from complete graphs that structurally constrains the edges connected to tumour vertices based upon the spatial proximity of tumours and organs. This enables retrieval on the basis of tumour localisation. We also present a similarity matching algorithm that accounts for different feature sets for graph elements from different imaging modalities. Our method emphasises the relationships between a tumour and related organs, while still modelling patient-specific anatomical variations. Constraining tumours to related anatomical structures improves the discrimination potential of graphs, making it easier to retrieve similar images based on tumour location. We evaluated our retrieval methodology on a dataset of clinical PET-CT volumes. Our results showed that our method enabled the retrieval of multi-modality images using spatial features. Our graph-based retrieval algorithm achieved a higher precision than several other retrieval techniques: gray-level histograms as well as state-of-the-art methods such as visual words using the scale- invariant feature transform (SIFT) and relational matrices representing the spatial arrangements of objects.

Metabolic changes in occipital lobe epilepsy with automatisms.

PURPOSE: Some studies suggest that the pattern of glucose hypometabolism relates not only to the ictal-onset zone but also reflects seizure propagation. We investigated metabolic changes in patients with occipital lobe epilepsy (OLE) that may reflect propagation of ictal discharge during seizures with automatisms. METHODS: Fifteen patients who had undergone epilepsy surgery for intractable OLE and had undergone interictal Fluorine-18-fluorodeoxyglucose positron-emission tomography ((18)F-FDG-PET) between 1994 and 2004 were divided into two groups (with and without automatisms during seizure). Significant regions of hypometabolism were identified by comparing (18)F-FDG-PET results from each group with 16 healthy controls by using statistical parametric mapping. KEY FINDINGS: Significant hypometabolism was confined largely to the epileptogenic occipital lobe in the patient group without automatisms. In patients with automatisms, glucose hypometabolism extended from the epileptogenic occipital lobe into the ipsilateral temporal lobe. SIGNIFICANCE: We identified a distinctive hypometabolic pattern that was specific for OLE patients with automatisms during a seizure. This finding supports the postulate that seizure propagation is a cause of glucose hypometabolism beyond the region of seizure onset.

Automated identification of dementia using FDG-PET imaging.

Parametric FDG-PET images offer the potential for automated identification of the different dementia syndromes. However, various existing image features and classifiers have their limitations in characterizing and differentiating the patterns of this disease. We reported a hybrid feature extraction, selection, and classification approach, namely, the GA-MKL algorithm, for separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. In this approach, we extracted three groups of features to describe the average level, spatial variation, and asymmetry of glucose metabolic rates in 116 cortical volumes. An optimal combination of features, that is, capable of classifying dementia cases was identified by a genetic algorithm- (GA-) based method. The condition of each FDG-PET study was predicted by applying the selected features to a multikernel learning (MKL) machine, in which the weighting parameter of each kernel function can be automatically estimated. We compared our approach to two state-of-the-art dementia identification algorithms on a set of 129 clinical cases and improved the performance in separating the dementia types, achieving accuracy of 94.62%. There is a very good agreement between the proposed automated technique and the diagnosis made by clinicians.

Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization.

Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.

Localized Sparse Code Gradient in Alzheimer's disease staging.

The accurate diagnosis of Alzheimer's disease (AD) at different stages is essential to identify patients at high risk of dementia and plan prevention or treatment measures accordingly. In this study, we proposed a new AD staging method for the entire spectrum of AD including the AD, Mild Cognitive Impairment with and without AD conversions, and Cognitive Normal groups. Our method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features. It also updated the testing data based on the Localized Sparse Code Gradients (LSCG) to further enhance the classification. The LSCG algorithm, validated using Magnetic Resonance Imaging data from the ADNI baseline cohort, achieved significant improvements on all diagnosis groups compared to using the original sparse coding method.

Cellular automata and anisotropic diffusion filter based interactive tumor segmentation for positron emission tomography.

Tumor segmentation in positron emission tomography (PET) aids clinical diagnosis and in assessing treatment response. However, the low resolution and signal-to-noise inherent in PET images, makes accurate tumor segmentation challenging. Manual delineation is time-consuming and subjective, whereas fully automated algorithms are often limited to particular tumor types, and have difficulties in segmenting small and low-contrast tumors. Interactive segmentation may reduce the inter-observer variability and minimize the user input. In this study, we present a new interactive PET tumor segmentation method based on cellular automata (CA) and a nonlinear anisotropic diffusion filter (ADF). CA is tolerant of noise and image pattern complexity while ADF reduces noise while preserving edges. By coupling CA with ADF, our proposed approach was robust and accurate in detecting and segmenting noisy tumors. We evaluated our method with computer simulation and clinical data and it outperformed other common interactive PET segmentation algorithms.

A patient-centric distribution architecture for medical image sharing.

Over the past decade, rapid development of imaging technologies has resulted in the introduction of improved imaging devices, such as multi-modality scanners that produce combined positron emission tomography-computed tomography (PET-CT) images. The adoption of picture archiving and communication systems (PACS) in hospitals have dramatically improved the ability to digitally share medical image studies via portable storage, mobile devices and the Internet. This has in turn led to increased productivity, greater flexibility, and improved communication between hospital staff, referring physicians, and outpatients. However, many of these sharing and viewing capabilities are limited to proprietary vendor-specific applications. Furthermore, there are still interoperability and deployment issues which reduce the rate of adoption of such technologies, thus leaving many stakeholders, particularly outpatients and referring physicians, with access to only traditional still images with no ability to view or interpret the data in full. In this paper, we present a distribution architecture for medical image display across numerous devices and media, which uses a preprocessor and an in-built networking framework to improve compatibility and promote greater accessibility of medical data. Our INVOLVE2 system consists of three main software modules: 1) a preprocessor, which collates and converts imaging studies into a compressed and distributable format; 2) a PACS-compatible workflow for self-managing distribution of medical data, e.g. via CD USB, network etc; 3) support for potential mobile and web-based data access. The focus of this study was on cultivating patient-centric care, by allowing outpatient users to comfortably access and interpret their own data. As such, the image viewing software included on our cross-platform CDs was designed with a simple and intuitive user-interface (UI) for use by outpatients and referring physicians. Furthermore, digital image access via mobile devices or web-based access enables users to engage with their data in a convenient and user-friendly way. We evaluated the INVOLVE2 system using a pilot deployment in a hospital environment.

Recent software developments and applications in functional imaging.

Functional imaging allows the quantification of biochemical or biophysiological changes in-vivo through the visualization of the spatial distribution and temporal changes of administrated radiopharmaceuticals. Instrumentation advances such as PET-CT (positron emission tomography - computed tomography) and PET-MR (positron emission tomography - magnetic resonance), improvements in image processing and reconstruction, the development of target and disease-specific radiotracers and improved kinetic modelling techniques, have substantially enhanced our ability to measure functional changes in normal and diseased states. Various combinations of these advances and refinements are now used in routine clinical practice for patient care. In this paper we review recent literature on software developments and applications in image restoration, motion correction, kinetic analysis, and image processing in the field of functional imaging.

Automated and robust PERCIST-based thresholding framework for whole body PET-CT studies.

Positron emission tomography (PET) is unique for quantitatively assessing treatment response before marked morphological changes are detectable by Computed Tomography (CT). PET response criterion (PERCIST) is a widely accepted approach of assessing metabolic response of malignant lesions by using Standardized uptake value (SUV) normalized by lean body mass (LBM) with a volume of interest (VOI) reference defined in the right lobe of liver. However, the operator-dependent delineation of VOI reference is a time consuming and subjective task. Although the VOI reference can be estimated from the co-aligned CT, the low-dose CT data in PET-CT poses challenge in liver segmentation. In this study, we propose a fully automatic framework to calculate the PERCIST-based thresholding for whole-body PET-CT studies. The framework consists of multi-atlas registration and voxel classification for CT data to segment liver structure and delineate the VOI reference, which is then mapped to the PET data to derive the value of SUVLBM thresholding for PET to select regions of high metabolism. We evaluated our framework with 28 clinical studies diagnosed with lung cancer or lymphoma, and demonstrated both reliability and efficiency in depicting lesions using PERCIST thresholding.

A graph-based approach to the retrieval of volumetric PET-CT lung images.

Combined positron emission tomography and computed tomography (PET-CT) scans have become a critical tool for the diagnosis, localisation, and staging of most cancers. This has led to a rapid expansion in the volume of PET-CT data that is archived in clinical environments. The ability to search these vast imaging collections has potential clinical applications in evidence-based diagnosis, physician training, and biomedical research that may lead to the discovery of new knowledge. Content-based image retrieval (CBIR) is an image search technique that complements conventional text-based retrieval by the use of image features as search criteria. Graph-based CBIR approaches have been found to be exemplary methods for medical CBIR as they provide the ability to consider disease localisation during the similarity measurement. However, the majority of graph-based CBIR studies have been based on 2D key slice approaches and did not exploit the rich volumetric data that is inherent to modern medical images, such as multi-modal PET-CT. In this paper, we present a graph-based CBIR method that exploits 3D spatial features extracted from volumetric regions of interest (ROIs). We index these features as attributes of a graph representation and use a graph-edit distance to measure the similarity of PET-CT images based on the spatial arrangement of tumours and organs in a 3D space. Our study aims to explore the capability of these graphs in 3D PET-CT CBIR. We show that our method achieves promising precision when retrieving clinical PET-CT images of patients with lung tumours.

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images.

A study of non-invasive Patlak quantification for whole-body dynamic FDG-PET studies of mice.

Physiological changes in dynamic PET images can be quantitatively estimated by kinetic modeling technique. The process of PET quantification usually requires an input function in the form of a plasma-time activity curve (PTAC), which is generally obtained by invasive arterial blood sampling. However, invasive arterial blood sampling poses many challenges especially for small animal studies, due to the subjects' limited blood volume and small blood vessels. A simple non-invasive quantification method based on Patlak graphical analysis (PGA) has been recently proposed to use a reference region to derive the relative influx rate for a target region without invasive blood sampling, and evaluated by using the simulation data of human brain FDG-PET studies. In this study, the non-invasive Patlak (nPGA) method was extended to whole-body dynamic small animal FDG-PET studies. The performance of nPGA was systematically investigated by using experimental mouse studies and computer simulations. The mouse studies showed high linearity of relative influx rates between the nPGA and PGA for most pairs of reference and target regions, when an appropriate underlying kinetic model was used. The simulation results demonstrated that the accuracy of the nPGA method was comparable to that of the PGA method, with a higher reliability for most pairs of reference and target regions. The results proved that the nPGA method could provide a non-invasive and indirect way for quantifying the FDG kinetics of tumor in small animal studies.