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Title: The application of a novel detector for X-ray diffraction study of breast cancer
Author: Zheng, Y.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Current imaging methods - mammography, digital breast tomosynthesis, ultrasound and magnetic resonance images (MRI), fail to provide an accurate breast tumour size measurement. X-ray diffraction (XRD) can provide better contrast than mammography and digital breast tomosynthesis, better spatial resolution than ultrasound and is more cost effective than MRI. However, its use is limited by high radiation dose. Previous breast XRD research often fails to investigate samples with a realistic thickness or have a large data set. A system combining mammography and XRD was designed and verified in this thesis. By using mammography as a scout image to locate the tumour, XRD can be limited in the area that is suspicious as breast tumour thus a lower dose than whole breast XRD. One of the challenges to make this procedure possible was finding a suitable X-ray detector. A novel CMOS APS X-ray detector (DynAMITe) was characterised using a new mean-variance method with X-ray radiation in this thesis. This detector was later proven to be suitable for mammography guided XRD, with a 12.8 cm × 13.1 cm active area, 134 ± 40 e- read noise and 91.3 dB dynamic range. Forty-one breast tissue samples were used to evaluate the system feasibility. Further investigation showed that the designed system can detect cancerous breast tissues that form as little as 5.60~6.65% of a 39 mm thick test object. This result was better than the literature result of MRI of real patients regarding the smallest amount of cancer detectable. At the same time, MRI was the most accurate among the four current imaging methods for detecting breast tumour. This novel method combing mammography and XRD is proven to have superior performance compared to current imaging methods. However, the study should be verified with pathological results.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available