The study is the first to use ultrasound to improve the machine learning performance of photoacoustic tomography reconstruction for cancer diagnosis.
The photoacoustic imaging uses light and sound to detect cancer in the ovaries.
“Existing modalities are mainly based on the size and shape of the ovarian lesions, which do not provide an accurate diagnosis for earlier ovarian cancer and for risk assessment of large adnexal/ovarian lesions,” said Quing Zhu. “Photoacoustic imaging adds more functional information about vascular contrast from hemoglobin concentration and blood oxygen saturation.
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