Molecular imaging of dementia

A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients

In today's world, 46.8 million people suffer from brain related diseases. Dementia is most prevalent of all. In general scenario, a dementia patient lacks proper guidance in searching out the way to return back at his/her home. Thus, increasing the risk of getting damaged at individual-health level. Therefore, it is important to track their movement in more sophisticated manner as possible. With emergence of wearables, GPS sensors and Internet of Things (IoT), such devices have become available in public domain. Smartphone apps support caregiver to locate the dementia patients in real-time. RF, GSM, 3G, Wi-Fi and 4G technology fill the communication gap between patient and caregiver to bring them closer. In this paper, we incorporated 7 most popular wearables for investigation to seek appropriateness for dementia tracking in recent times in systematic manners. We performed an in-depth review of these wearables as per the cost, technology wise and application wise characteristics. A case novel study i.e. IoT-based Force Sensor Resistance enabled System-FSRIoT, has been proposed and implemented to validate the effectiveness of IoT in the domain of smarter dementia patient tracking in wearable form factor. The results show promising aspect of a whole new notion to leverage efficient assistive physio-medical healthcare to the dementia patients and the affected family members to reduce life risks and achieve a better social life.

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

Reliability of semiquantitative ¹⁸F-FDG PET parameters derived from simultaneous brain PET/MRI: a feasibility study

PURPOSE

Simultaneous brain PET/MRI faces an important issue of validation of accurate MRI based attenuation correction (AC) method for precise quantitation of brain PET data unlike in PET/CT systems where the use of standard, validated CT based AC is routinely available. The aim of this study was to investigate the feasibility of evaluation of semiquantitative (18)F-FDG PET parameters derived from simultaneous brain PET/MRI using ultrashort echo time (UTE) sequences for AC and to assess their agreement with those obtained from PET/CT examination.

METHODS

Sixteen patients (age range 18-73 years; mean age 49.43 (19.3) years; 13 men 3 women) underwent simultaneous brain PET/MRI followed immediately by PET/CT. Quantitative analysis of brain PET images obtained from both studies was undertaken using Scenium v.1 brain analysis software package. Twenty ROIs for various brain regions were system generated and 6 semiquantitative parameters including maximum standardized uptake value (SUV max), SUV mean, minimum SUV (SUV min), minimum standard deviation (SD min), maximum SD (SD max) and SD from mean were calculated for both sets of PET data for each patient. Intra-class correlation coefficients (ICCs) were determined to assess agreement between the various semiquantitative parameters for the two PET data sets.

RESULTS

Intra-class co-relation between the two PET data sets for SUV max, SUV mean and SD max was highly significant (p<0.00) for all the 20 predefined brain regions with ICC>0.9. SD from mean was also found to be statistically significant for all the predefined brain regions with ICC>0.8. However, SUV max and SUV mean values obtained from PET/MRI were significantly lower compared to those of PET/CT for all the predefined brain regions.

CONCLUSION

PET quantitation accuracy using the MRI based UTE sequences for AC in simultaneous brain PET/MRI is reliable in a clinical setting, being similar to that obtained using PET/CT.