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“Deep learning poised to improve breast cancer imaging”

Researchers have developed a new image reconstruction approach that could improve breast cancer detection.

The deep learning algorithm, known as Z-Net, overcomes a major hurdle in multi-modality imaging by allowing images to be recovered in real time.

It works with an imaging platform that combines optical spectral information with contrast-free magnetic resonance imaging (MRI) to improve detection of breast cancer.

Keith Paulsen, who led the research team from Dartmouth College, said: “The near infrared spectral tomography (NIRST) and MRI imaging platform we developed has shown promise, but the time and effort involved in image reconstruction has prevented it from being translated into the day-to-day clinical workflow. Thus, we designed a deep-learning algorithm that incorporates anatomical image data from MRI to guide NIRST image formation without requiring complex modelling of light propagation in tissue.” 

Paulsen and colleagues from the Beijing University of Technology and the University of Birmingham report that their new algorithm can distinguish between malignant and benign tumours using MRI-guided NIRST imaging data from patient breast exams.

“Z-Net could allow NIRST to become an efficient and effective add-on to non-contrast MRI for breast cancer screening and diagnosis because it allows MRI-guided NIRST images to be recovered in nearly real time,” said Paulsen.

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Image credit | iStock

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