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In this work, we present the development of phantoms made out by anatomical models printed with a 3D printer and adequate them to fit an Ultra-Low Field Magnetic Resonance Imaging System, in order to obtain 2D images. It was used a machine learning algorithm to segment anatomical images from CT and MRI databases from three different organs, and therefore a 3D printer was used to build structures that can fit system's bore and acquire images using basic acquisition sequences to obtain 2D images. The images obtained with the Ultra-Low Field MRI system, show the borders of the phantoms manufactured. This can help us to ensure that the contours from our phantoms can be appreciated with the imaging system, which can lead us to improve system's hardware and imaging processing techniques for academic and research purposes. [ABSTRACT FROM AUTHOR]

A temperature recording and storage system was designed and built, consisting of an array of five thermistor-type sensors, coupled to a synthetic model of biological tissue (MRI phantom) for Ultra-Low Field Magnetic Resonance Imaging System (ULF-MRI), using the Atmega328P microcontroller, like data logging and also used to determine the change in temperature associated with the absorption of energy within the phantom due to the interaction between the radio frequency (RF) field with a sample in different regions within it in MRI studies. The changes in temperature during the MRI study was registered for the entered MRI sequence. All the experiments were carried out in a ULF-MRI system Terranova that uses the Earth's magnetic field to obtain the resonance signal from the magnetization of the protons present in a sample of interest, and thus be able to perform primary imaging and spectroscopy studies [1]. [ABSTRACT FROM AUTHOR]