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Title: Quantitation of contrast enhancement in dynamic magnetic resonance imaging of the breast
Author: Brookes, Jason A.
ISNI:       0000 0001 3481 8896
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1996
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This thesis explores issues relating to the quantitation of both signal enhancement and contrast agent uptake, along with problems associated with such quantitation, with the aim of improving the specificity of dynamic, contrast enhanced breast MRI. A variable flip angle technique for measuring T1 in vivo was implemented using 2D and 3D FLASH sequences, in order to monitor the differential relaxation rate following injection of contrast agent. Experiments (with phantom objects) investigating sources of error in these techniques found that (i) the rf transmit power calibration automatically performed by the imaging system was 13.5% in error, (ii) significant non-uniformity in the rf transmit field existed over the breast coil volume and (iii) a 2D FLASH sequence developed locally from an editable scout sequence was significantly more accurate at measuring T1 than a commercially supplied 2D FLASH sequence. Since the in vivo measurement of T1 requires complicated imaging protocols and data analysis, two simple indices commonly used to quantitate signal enhancement were evaluated by computer simulation and comparison in a group of patients. The postulate that the index least influenced by pre-contrast tissue T1 (when using a contrast enhanced gradient echo imaging protocol) would be better able to correctly classify an undiagnosed lesion as either benign or malignant was used to evaluate which index was the most appropriate for quantitating signal enhancement in breast MRI. An index which normalised the difference between pre- and post-contrast signal to the fat signal intensity proved to be the better of the two indices. One problem with this index, however, is that it is sensitive to variations in fat signal through the breast. A simple uniformity correction scheme was implemented to reduce this problem and tested on both phantom and patient image data sets.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pattern recognition & image processing