Research Supervisor Connect

Phase Contrast Imaging of the Breast


A novel method for imaging breast cancer


Professor Patrick Brennan.

Research location

Medical Imaging and Radiation Sciences Research Group

Program type



X-ray breast imaging is the mainstay for regular screening of women over the age of 40, but current methods present with three significant limitations:

• less than optimal sensitivity and specificity (currently at 60-70 and 80% respectively);
• involvement of excessive radiation dose to the radiosensitive breast;
• painful compression of the breast (to maximise imaging contrast).

These limitations lead to reduced detection of early cancers, excessive recall of disease-free women, higher mortality, unnecessary irradiation of healthy tissue, and poor engagement of women with breast cancer screening.

In collaboration with world experts in Health Sciences, Medicine, Science, and Engineering and Information Technologies, we will explore a completely innovative solution to address these limitations: in-line phase-contrast computed tomography (PCT) of the breast. Since Roentgen's discovery of X-rays in 1895, clinical systems have relied on recording the intensity (number) of X-rays transmitted through the body. Instead our system exploits changes to the X-ray phase (wave-form) occurring within the body, which contains critically important biologic information. Our early pilot data show a 20-fold improvement in the signal-to-noise ratio (thus improved sensitivity and specificity) at the same X-ray dose and a 400-fold reduction in radiation dose without image quality loss, compared to conventional CT breast imaging. With such improvements, painful compression may not be required. The proposed work will involve irradiation of excised breast tissues and a novel patient experiment with images being assessed by the leading expert radiologists in Australia,USA, Europe and SE Asia using our $4m BREAST platform.

Additional information

There are opportunities here for PhD students interested in Diagnostic Imaging, Computer Science and Electrical Engineering.

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Opportunity ID

The opportunity ID for this research opportunity is 2194

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