Numerical Body Model Inference for Individualized RF exposure Prediction in Neuroimaging at 7T MRI

Abstract

Magnetic Resonance Imaging is a popular modality for brain imaging in present times. The quality of the images depends on the strength of the magnetic field. An MRI scanner with a magnetic field strength of 3 Tesla(T) is pre-dominantly used for clinical purposes. However, with the advancement in technology, and the need to image finer image finer structures, we are gradually shifting to higher magnetic field strengths like 7T and above. One of the major bottlenecks in these systems is the bias induced in the images due to field inhomogeneities of higher field strengths. Yet another drawback of these systems is the increase in power dissipation in the tissues. This is measured by a quantity called Specific Absorption Rate(SAR). The goal of this thesis is to accurately predict SAR values for various volunteers as they may differ from subject to subject. Many homogeneous models have been created earlier to estimate the value of SAR, however, these estimates are often over-conservative and safe which compromises with image quality. A personalised numerical body model is created for all volunteers using relevant information derived from simulations performed on generalized models. Various tissues have to be segmented to create these 3D numerical body models. However, there has to be a trade-off between ease of segmentation and SAR accuracy. Keeping that in mind, it was found that the optimum number of tissues to get reliable SAR estimates is six. A deep learning method was then used for segmentation. A numerical body model was derived for all the volunteers using the deep learning segmentation. An adapted ForkNet, which is similar to U-net in architecture is used to segment these images. The SAR values derived from the predicted numerical body model and the original body model are similar, hence speeding up the process for SAR prediction. However, there are certain limitations of the thesis that can be addressed in the future. Inadequate data remains a major bottleneck for the project, increasing the data should result in improved segmentation, this can be addressed by acquiring more data. Another major drawback of the thesis is the segmentation accuracy, the ground truth segmentation is performed to the best of our knowledge, however, some errors are still present in the ground truth segmentation. The next steps for this project would be to acquire more data, train the data on multiple input sequences, use a 3D network and using localizer images that are acquired at the start of the MRI scan. Nonetheless, the principles established in this thesis confirm that a deep learning approach can be used to create numerical body models for SAR estimates. It also establishes the fact that these SAR estimates are comparable to the SAR estimates generated from the ground truth numerical body models.