Use this URL to cite or link to this record in EThOS:
Title: Advanced methods for mapping the radiofrequency magnetic fields in MRI
Author: Padormo, Francesco
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Full text unavailable from EThOS. Please try the link below.
Access from Institution:
As MRI systems have increased in static magnetic field strength, the radiofrequency (RF) fields that are used for magnetisation excitation and signal reception have become significantly less uniform. This can lead to image artifacts and errors when performing quantitative MRI. A further complication arises if the RF fields vary substantially in time. In the first part of this investigation temporal variations caused by respiration were explored on a 3T scanner. It was found that fractional changes in transmit field amplitude between inhalation and expiration ranged from 1% to 14% in the region of the liver in a small group of normal subjects. This observation motivated the development of a pulse sequence and reconstruction method to allow dynamic observation of the transmit field throughout the respiratory cycle. However, the proposed method was unsuccessful due to the inherently time-consuming nature of transmit field mapping sequences. This prompted the development of a novel data reconstruction method to allow the acceleration of transmit field mapping sequences. The proposed technique posed the RF field reconstruction as a nonlinear least-squares optimisation problem, exploiting the fact that the fields vary smoothly. It was shown that this approach was superior to standard reconstruction approaches. The final component of this thesis presents a unified approach to RF field calibration. The proposed method uses all measured data to estimate both transmit and receive sensitivities, whilst simultaneously insisting that they are smooth functions of space. The resulting maps are robust to both noise and imperfections in regions of low signal.
Supervisor: Hajnal, Jo Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available