Vishnu Anilkumar

Vishnu Anilkumar M.Sc.(CS) Machine Intelligence

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  • Research Focus : Reducing Inhomogeneity in MRI Images Using Machine Learning

Medical image acquisition devices provide a vast amount of anatomical and functional information, which facilitate and improve diagnosis and patient treatment, especially when supported by modern quantitative image analysis methods. However, modality specific image artifacts, such as the phenomena of intensity inhomogeneity in magnetic resonance images (MRI),are still prominent and can adversely affect quantitative image analysis. Inhomogeneities are considered to be multiplicative low-frequency variations of intensities that are due to the anomalies of the magnetic fields of the scanners.

Radio Frequency (RF) birdcage coils are widely used in Magnetic Resonance Imaging (MRI). Before the actual construction of the coil, not only calculating the capacitance value, which is necessary for the coil to resonate at the desired frequency, but also modeling the birdcage coil in a 3D simulation environment and making electromagnetic analysis in the volume of interest have importance in terms of observing the resonance behavior of the coil, electromagnetic field distributions inside the coil, or specific absorption rate (SAR) distribution for any object. In this project thesis, for obtain the design, a study is carried out firstly using the computer program Birdcage Builder and HFSS (high frequency structural simulator) is used for design and simulate the birdcage coil to obtain desired frequency for 3T Secondly, we optimize the B1 field that reduce the inhomogeneities.