Proliferative and Glycolytic Assessment of the Whole-Body Bone Marrow Compartment

Objective: Quantitative assessment of active bone marrow (BM) in vivo is yet to be well-defined. This study aims to compare total body BM volume estimations obtained from use of both18F-FLT PET/CT and 18F-FDG PET/CT in order to consolidate higher cellular proliferation rates with imaging the highly active red BM in pancreatic cancer. Methods: This phase I pilot study includes seven patients with pancreatic cancers who underwent both 18F-FLT and 18F-FDG imaging each acquired within a week’s duration. A CT-based classifier is used for segmenting bone into cortical and trabecular regions. The total BM volume is determined through statistical thresholding on PET activity found within the trabecular bone. Results: Results showed that 18F-FLT measures of red BM volume (RBV) were higher than those obtained from 18F-FDG (∆=89.21 ml). RBV obtained using 18F-FLT in males were found to have high correlation with measured weight (R2=0.61) and BMI (R2=0.70). The red BM fraction obtained from 18F-FLT was significantly different between males and females, with females showing much higher red bone matter within the trabecular bone (p<0.05). In contrast to 18F-FLT, 18F-FDG BM measurements showed that RBV was significantly different between males and females (p<0.05). Results also show that spinal activity SUV threshold for red BM segmentation is significantly different between 18F-FLT PET and 18F-FDG PET (p<0.05). Conclusion: By combining 18F-FLT-PET and 18F-FDG-PET, this study provides useful insights for in vivo BM estimation through its proliferative and glycolytic activitie

A practical EMG-based human-computer interface for users with motor disabilities

In line with the mission of the Assistive Technology Act of 1998 (ATA), this study proposes an integrated assistive real-time system which affirms that technology is a valuable tool that can be used to improve the lives of people with disabilities . An assistive technology device is defined by the ATA as any item, piece of equipment, or product system, whether acquired commercially, modified, or customized, that is used to increase, maintain, or improve the functional capabilities of individuals with disabilities . The purpose of this study is to design and develop an alternate input device that can be used even by individuals with severe motor disabilities . This real-time system design utilizes electromyographic (EMG) biosignals from cranial muscles and electroencephalographic (EEG) biosignals from the cerebrum\u27s occipital lobe, which are transformed into controls for two-dimensional (2-D) cursor movement, the left-click (Enter) command, and an ON/OFF switch for the cursor-control functions . This HCI system classifies biosignals into mouse functions by applying amplitude thresholds and performing power spectral density (PSD) estimations on discrete windows of data. Spectral power summations are aggregated over several frequency bands between 8 and 500 Hz and then compared to produce the correct classification . The result is an affordable DSP-based system that, when combined with an on-screen keyboard, enables the user to fully operate a computer without using any extremities

Integrated electromyogram and eye-gaze tracking cursor control system for computer users with motor disabilities

This research pursued the conceptualization, implementation, and testing of a system that allows for computer cursor control without requiring hand movement. The target user group for this system are individuals who are unable to use their hands because of spinal dysfunction or other afflictions. The system inputs consisted of electromyogram (EMG) signals from muscles in the face and point-of-gaze coordinates produced by an eye-gaze tracking (EGT) system. Each input was processed by an algorithm that produced its own cursor update information. These algorithm outputs were fused to produce an effective and efficient cursor control. Experiments were conducted to compare the performance of EMG/EGT, EGT-only, and mouse cursor controls. The experiments revealed that, although EMG/ EGT control was slower than EGT-only and mouse control, it effectively controlled the cursor without a spatial accuracy limitation and also facilitated a reliable click operation

Adaptive eye-gaze tracking using neural-network-based user profiles to assist people with motor disability

This study developed an adaptive real-time humancomputer interface (HCI) that serves as an assistive technology tool for people with severe motor disability. The proposed HCI design uses eye gaze as the primary computer input device. Controlling the mouse cursor with raw eye coordinates results in sporadic motion of the pointer because of the saccadic nature of the eye. Even though eye movements are subtle and completely imperceptible under normal circumstances, they considerably affect the accuracy of an eye-gaze-based HCI. The proposed HCI system is novel because it adapts to each specific user’s different and potentially changing jitter characteristics through the configuration and training of an artificial neural network (ANN) that is structured to minimize the mouse jitter. This task is based on feeding the ANN a user’s initially recorded eye-gaze behavior through a short training session. The ANN finds the relationship between the gaze coordinates and the mouse cursor position based on the multilayer perceptron model. An embedded graphical interface is used during the training session to generate user profiles that make up these unique ANN configurations. The results with 12 subjects in test 1, which involved following a moving target, showed an average jitter reduction of 35%; the results with 9 subjects in test 2, which involved following the contour of a square object, showed an average jitter reduction of 53%. For both results, the outcomes led to trajectories that were significantly smoother and apt at reaching fixed or moving targets with relative ease and within a 5% error margin or deviation from desired trajectories. The positive effects of such jitter reduction are presented graphically for visual appreciation

18F-FLT Positron Emission Tomography/Computed Tomography Imaging in Pancreatic Cancer: Determination of Tumor Proliferative Activity and Comparison with Glycolytic Activity as Measured by 18F-FDG Positron Emission Tomography/Computed Tomography Imaging

OBJECTIVE: This phase-I imaging study examined the imaging characteristic of 3’-deoxy-3’-((18)F)-fluorothymidine ((18)F-FLT) positron emission tomography (PET) in patients with pancreatic cancer and comparisons were made with ((18)F)-fluorodeoxyglucose ((18)F-FDG). The ultimate aim was to develop a molecular imaging tool that could better define the biologic characteristics of pancreas cancer, and to identify the patients who could potentially benefit from surgical resection who were deemed inoperable by conventional means of staging. METHODS: Six patients with newly diagnosed pancreatic cancer underwent a combined FLT and FDG computed tomography (CT) PET/CT imaging protocol. The FLT PET/CT scan was performed within 1 week of FDG PET/CT imaging. Tumor uptake of a tracer was determined and compared using various techniques; statistical thresholding (z score=2.5), and fixed standardized uptake value (SUV) thresholds of 1.4 and 2.5, and applying a threshold of 40% of maximum SUV (SUV(max)) and mean SUV (SUV(mean)). The correlation of functional tumor volumes (FTV) between (18)F-FDG and (18)F-FLT was assessed using linear regression analysis. RESULTS: It was found that there is a correlation in FTV due to metabolic and proliferation activity when using a threshold of SUV 2.5 for FDG and 1.4 for FLT (r=0.698, p=ns), but a better correlation was obtained when using SUV of 2.5 for both tracers (r=0.698, p=ns). The z score thresholding (z=2.5) method showed lower correlation between the FTVs (r=0.698, p=ns) of FDG and FLT PET. CONCLUSION: Different tumor segmentation techniques yielded varying degrees of correlation in FTV between FLT and FDG-PET images. FLT imaging may have a different meaning in determining tumor biology and prognosis

Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer’s disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that theMini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume ormean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing AD

A practical guideline for intracranial volume estimation in patients with Alzheimer’s disease

Background Intracranial volume (ICV) is an important normalization measure used in morphometric analyses to correct for head size in studies of Alzheimer Disease (AD). Inaccurate ICV estimation could introduce bias in the outcome. The current study provides a decision aid in defining protocols for ICV estimation in patients with Alzheimer disease in terms of sampling frequencies that can be optimally used on the volumetric MRI data, and the type of software most suitable for use in estimating the ICV measure. Methods Two groups of 22 subjects are considered, including adult controls (AC) and patients with Alzheimer Disease (AD). Reference measurements were calculated for each subject by manually tracing intracranial cavity by the means of visual inspection. The reliability of reference measurements were assured through intra- and inter- variation analyses. Three publicly well-known software packages (Freesurfer, FSL, and SPM) were examined in their ability to automatically estimate ICV across the groups. Results Analysis of the results supported the significant effect of estimation method, gender, cognitive condition of the subject and the interaction among method and cognitive condition factors in the measured ICV. Results on sub-sampling studies with a 95% confidence showed that in order to keep the accuracy of the interleaved slice sampling protocol above 99%, the sampling period cannot exceed 20 millimeters for AC and 15 millimeters for AD. Freesurfer showed promising estimates for both adult groups. However SPM showed more consistency in its ICV estimation over the different phases of the study. Conclusions This study emphasized the importance in selecting the appropriate protocol, the choice of the sampling period in the manual estimation of ICV and selection of suitable software for the automated estimation of ICV. The current study serves as an initial framework for establishing an appropriate protocol in both manual and automatic ICV estimations with different subject populations

A probabilistic approach for pediatric epilepsy diagnosis using brain functional connectivity networks

Background The lives of half a million children in the United States are severely affected due to the alterations in their functional and mental abilities which epilepsy causes. This study aims to introduce a novel decision support system for the diagnosis of pediatric epilepsy based on scalp EEG data in a clinical environment. Methods A new time varying approach for constructing functional connectivity networks (FCNs) of 18 subjects (7 subjects from pediatric control (PC) group and 11 subjects from pediatric epilepsy (PE) group) is implemented by moving a window with overlap to split the EEG signals into a total of 445 multi-channel EEG segments (91 for PC and 354 for PE) and finding the hypothetical functional connectivity strengths among EEG channels. FCNs are then mapped into the form of undirected graphs and subjected to extraction of graph theory based features. An unsupervised labeling technique based on Gaussian mixtures model (GMM) is then used to delineate the pediatric epilepsy group from the control group. Results The study results show the existence of a statistically significant difference (p \u3c 0.0001) between the mean FCNs of PC and PE groups. The system was able to diagnose pediatric epilepsy subjects with the accuracy of 88.8% with 81.8% sensitivity and 100% specificity purely based on exploration of associations among brain cortical regions and without a priori knowledge of diagnosis. Conclusions The current study created the potential of diagnosing epilepsy without need for long EEG recording session and time-consuming visual inspection as conventionally employed