2024-01-23-quillent24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction	Digital Breast Tomosynthesis (DBT) is an X-ray imaging modality enabling the reconstruction of 3D volumes of breasts. DBT is mainly used for cancer screening, and is intended to replace conventional mammography in the coming years. However, DBT reconstructions are impeded by several types of artefacts induced by the geometry of the device itself, degrading the image quality and limiting its resolution along the thickness of the compressed breast. In this study, we propose a deep-learning-based pipeline to address the DBT reconstruction problem, focusing on the removal of sparse-view and limited-angle artefacts. Specifically, this procedure is composed of two steps: a classic reconstruction algorithm is first applied on normalised projections, then a deep neural network is tasked with erasing the artefacts present in the obtained volumes. A major difficulty to solve our problem is the lack of real conditions artefact-free data. To overcome this complication, we resort to a new dataset comprised of synthetic breast texture phantoms. We then show that our training method and database strategy are promising to tackle the problem as they improve the informational value of planes orthogonal to the detector, which are not currently used by radiologists due to their poor quality. Eventually, we assess the impact of removing the bias components from the network and using stacks of slices as inputs, with regard to the generalisation ability of our approach on both synthetic and clinical data.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	quillent24a	0	A deep learning method trained on synthetic data for digital breast tomosynthesis reconstruction	1813	1825	1813-1825	1813	false	Quillent, Arnaud and Bismuth, Vincent Jonas and Bloch, Isabelle and Kervazo, Christophe and Ladjal, Said	given family Arnaud Quillent given family Vincent Jonas Bismuth given family Isabelle Bloch given family Christophe Kervazo given family Said Ladjal	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/quillent24a/quillent24a.pdf