k- Space Trajectory Design for Reduced Scan-time in MRI Systems
In MRI systems, the final scan is obtained as a Fourier Transform of a k-space image. The k-sapce image is obtained by designing gradient waveforms subject to practical constraints. This work proposed a generalized framework based on the projection of infeasible trajectories onto the set of feasible trajectories which provides a bouquet of methods with tunable parameters, that can reduce the scan time by 50% to 67% without perceptible degradation of the quality of the final scan.
The first of these longitudinal studies covers approximately 10,000 people above the age of 45 years from the Taluka of Srinivasapura (about 100 km from Bangalore). These individuals are being assessed in several interdisciplinary dimensions, periodically, as they age, ranging from cognition to imaging to genetics. A parallel study funded by the Tata Trusts is being carried out wherein approximately 1000 urban middle-class subjects living in Bangalore are assessed in a similar manner. These two studies are harmonized and they run in parallel. Our hope is that in the next few years, we will be able to discern the unique risk factors and protective factors that contribute to aging in these two populations.
Electromagnetic field analysis for low-cost low-field Halbach array based portable MRI System
The design of a low-cost portable MRI system is a challenging task. In order to build such a system, understanding of the electromagnetic fields in a Halbach magnet array-based system is crucial. Theoretical expressions modelling the EM fields was carried out in collaboration with TU Delft and Leiden University Medical Center, Netherlands. The modelling helped realize a prototype system that can be useful for obtaining affordable MRI scans for babies in developing countries.
Patrick S. Fuchs, “Electromagnetic Fields in MRI Analytical Methods and Applications,” PhD Thesis, TU Delft. Jointly supervised by Dr. Ir. R. F. Remis, Prof. Dr. Ir. G. Leus, Prof. Dr. K. V. S. Hari
Applications of AI/Machine Learning in Echocardiography and to predict the state of an ICU patient
The ongoing project aims to develop deep learning-based algorithms to estimate the parameters of a human cardiovascular system for Indian subjects. The first step in this endeavour has been the creation of an Indian dataset of echocardiograms in collaboration with Sri Jayadeva Institute of Cardiovascular Sciences, Bangalore. Over the past one year, echocardiograms of 4000 subjects have been collected.
Development of deep learning algorithms to automatically estimate Ejection Fraction and detection of Regional Wall Abnormalities are under progress. Another activity aims to collect data from Intensive Care Units at Christian Medical College, Vellore and develop model based and neural network-based approaches to predict the state of the ICU patient. The collection of the data from bedside monitors and other patient information is ongoing.
Artificial Pancreas for Type-1 Diabetes patients
To develop an effective artificial pancreas system for Type-1 diabetic patients, especially for the Indian population. It involved designing and developing a prototype device which implemented a novel control algorithm based on advanced predictive and feedback control theory using the data collected from Indian subjects.