Magnetic resonance imaging (MRI) scanners are a crucial diagnostic tool for radiologists. They are able to render two­ and three­dimensional images of the body without exposure to harmful radiation. MRI systems are, however, costly to build and maintain. This adversely impacts access to these scanners in developing regions. In an effort to combat this problem, a low­field MRI scanner is being developed. Conventional MRI scanners utilize a superconducting solenoid to generate the main magnetic field. The low­field scanner, on the other hand, induces the main magnetic field through a Hallbach array of permanent neodymium magnets. While beneficial for production and maintenance costs, as well as portability, the Hallbach array is not able to generate a perfectly homogeneous magnetic field. The inhomogeneities present in the main magnetic field result in distortion of the images when reconstructed using conventional fast Fourier transform (FFT) methods. To counteract this, a reconstruction method that utilizes field information needs to be employed. In this thesis, existing methods to determine and utilize the field information to correct image distortion are explored. From this analysis, it is evident that model­based (MB) methods are most suitable for reconstruction of data from the low­field scanner. Current MB methods are only implemented for two­dimensional reconstruction. The goal of this thesis is to expand these methods to three­dimensional reconstruction. A novel MB method for three­dimensional reconstruction is presented. This new method is able to circumvent memory constraints that arise from reconstruction of large data sets. Though the new method requires several hours to reconstruct a 128 × 128 × 30 data set, visual inspection indicates that an accurate result is achieved.

The founders of Momo Medical envisioned a health care product that would help nurses worldwide with pressure ulcer prevention. Pressure ulcers are a chronic wound that affects the skin of patients who do not regularly change bed posture. As it currently stands, nurses lack the manpower and time to change the postureof patients in a timely manor. Thus, a goal was set to create a product that would reduce this strain on nurses.The goal was to design a non-intrusive product that keeps track of the posture of a patient on a hospital bed, and send an alarm if this has not changed in a set amount of time. A constraint with this was that the product has to lay directly underneath the mattress of the patient. Furthermore, the product has to make use of as few sensors as possible. With these constrains, the project group was divided in three subgroups: the sensor, the algorithm, and the testing subgroup.This report will focus on the design, implementation, and evaluation of the algorithm. The algorithm has to be designed to process the sensor data and return, with 90% accuracy, the posture of the patient. It also has to be able to determine with 99% accuracy whether or not there is a patient on the bed. An optional objective was to design an algorithm to determine the heart and respiration rate of the patient, with 80% accuracy, but this was of lower priority. It was soon decided to give priority to posture detection and not further investigate detection of the heart and respiration rate.In the final product, the information about the patient’s posture will be displayed in a graphical user interface (GUI). This GUI has to be user-friendly and intuitive to use by the nurses. During the prototyping phase, however, a different GUI was used, one that displayed various debugging information. This prototype GUI was necessary to test the various algorithms.Several different mathematical models have been investigated, implemented, and tested. These models range from taking the variance of the sensor data, to deploying a Fourier transform on a polynomial curve based on the sensor data. With preliminary test data, an accuracy of 93% was achieved using a neural network. With the final testing data however, only the neural network reached the required accuracy of 90%, though only just, at 90.8%. With future iterations of the algorithm, more research and data collection is advised, as neural networks work better with larger data sets.