ATM Reporting System

Razimi, Umi Najiah Ahmad; Ahmed, Falah Y. H.; Mustapa, Nordiana Rhahayu

doi:10.1109/icsengt.2019.8906494

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Furthermore, despite women with dense breast tissue being more likely to be diagnosed with breast cancer than those with less dense breasts, the sensitivity of mammograms decreases with breast-tissue density, as lesions are more likely to be obscured [ 5 , 6 ]. This has led to a significantly high rate of false negatives in younger women and women with dense breast tissue [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging

Robins

Camacho

Agudo

et al. 2021

Sensors

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Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

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Section: Introductionmentioning

confidence: 99%