2022
DOI: 10.1002/mp.15595
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INSIDEnet: Interpretable NonexpanSIve Data‐Efficient network for denoising in grating interferometry breast CT

Abstract: Purpose Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft‐tissue contrast. Grating interferometry breast computed tomography (GI‐BCT) is a promising X‐ray phase contrast modality that could overcome these limitations by offering high soft‐tissue contrast and excellent three‐dimensional resolution. To enable the transition of this tech… Show more

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Cited by 5 publications
(3 citation statements)
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“…We used in-silico breast phantoms as described in [ 39 ]. DPC sinograms have then been simulated by forward projecting clean phantom data, adding realistic Poisson noise and finally retrieving the differential phase contrast sinogram with Fourier analysis [ 39 ].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…We used in-silico breast phantoms as described in [ 39 ]. DPC sinograms have then been simulated by forward projecting clean phantom data, adding realistic Poisson noise and finally retrieving the differential phase contrast sinogram with Fourier analysis [ 39 ].…”
Section: Methodsmentioning
confidence: 99%
“…We used in-silico breast phantoms as described in [ 39 ]. DPC sinograms have then been simulated by forward projecting clean phantom data, adding realistic Poisson noise and finally retrieving the differential phase contrast sinogram with Fourier analysis [ 39 ]. We simulated 120000 photons leaving the source to get as close as possible to the best data quality we can achieve on our prototype.…”
Section: Methodsmentioning
confidence: 99%
“…We simulated noisy and clean sinograms using the in-silico absorption and phase breast phantoms introduced in [27]. For the dark-field channel, we simulated simple dots representing microcalcifications.…”
Section: Data Generation Pipelinementioning
confidence: 99%