16th International Workshop on Breast Imaging (IWBI2022) 2022
DOI: 10.1117/12.2625748
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Assessment of video frame interpolation network to generate digital breast tomosynthesis projections

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Cited by 2 publications
(3 citation statements)
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“…In our preliminary study, we have used multiple tests to overcome the increase in radiation dose with interpolated images, but there is still work to do to improve the quality of interpolation and its limitations regarding alignment of structures. In previous work, 12 the approach was tested in DBT projections with different acquisition characteristics from the images used in this current work, and in that case, the results showed greater visual quality with the original reconstruction. Further investigation is needed to verify the implications of the image characteristics in the interpolation process and assess different acquisition geometries.…”
Section: Discussionmentioning
confidence: 71%
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“…In our preliminary study, we have used multiple tests to overcome the increase in radiation dose with interpolated images, but there is still work to do to improve the quality of interpolation and its limitations regarding alignment of structures. In previous work, 12 the approach was tested in DBT projections with different acquisition characteristics from the images used in this current work, and in that case, the results showed greater visual quality with the original reconstruction. Further investigation is needed to verify the implications of the image characteristics in the interpolation process and assess different acquisition geometries.…”
Section: Discussionmentioning
confidence: 71%
“…10,11 In our previous work, a pilot study evaluated how the addition and replacement of some DBT projections by VFI network interpolated images would interfere in reconstructed slices. 12 In the present work, we adapt and trained a residual refinement interpolation neural network (RRIN), 13 commonly used in computer vision applications of video frame interpolation, to generate synthetic DBTMI projections from pairs of original projections. The objective is to compensate for the increase in exposure due to the use of the MI sensor, replacing some of the original projections with synthetic images.…”
Section: Introductionmentioning
confidence: 99%
“…In addition to this reassuring observation, our lab has been investigating options to reduce the radiation dose and compensate for a potential increase in AGD. In our ongoing project, in collaboration with the University of São Paulo, São Carlos Campus, Brazil, Costa et al 17 has trained a convolutional neural network to synthesize DBT projections by interpolation from neighboring projection, which enables to reduce the radiation dose without compromising image quality. An alternative option would be to explore MI sensors without metallic elements, which would not additionally attenuate x-rays during simultaneous acquisition.…”
Section: Discussionmentioning
confidence: 99%