Novel Perlin-based phantoms using 3D models of compressed breast shapes and fractal noise
(2022) Medical Imaging 2022: Physics of Medical Imaging In Progress in Biomedical Optics and Imaging - Proceedings of SPIE 12031.- Abstract
Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval,... (More)
Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval, respectively. DBT projections and reconstructions of the phantoms were simulated using two acquisition geometries of our DBT prototype. The performance of both acquisition geometries was compared using breast volume segmentations of the Perlin phantoms. Results show that breast volume estimates are improved with the introduction of posterior-anterior motion of the x-ray source in DBT acquisitions. The breast volume is overestimated in DBT, varying substantially with the acquisition geometry; segmentation errors are more evident for thicker and larger breasts. These results provide additional evidence and suggest that custom acquisition geometries can improve the performance and accuracy in DBT. Perlin phantoms help to identify limitations in acquisition geometries and to optimize the performance of the DBT prototypes.
(Less)
- author
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- digital breast tomosynthesis, Perlin noise, ray-tracing, virtual clinical trial
- host publication
- Medical Imaging 2022 : Physics of Medical Imaging - Physics of Medical Imaging
- series title
- Progress in Biomedical Optics and Imaging - Proceedings of SPIE
- editor
- Zhao, Wei and Yu, Lifeng
- volume
- 12031
- article number
- 120313S
- publisher
- SPIE
- conference name
- Medical Imaging 2022: Physics of Medical Imaging
- conference location
- Virtual, Online
- conference dates
- 2022-03-21 - 2022-03-27
- external identifiers
-
- scopus:85131211009
- ISSN
- 1605-7422
- ISBN
- 9781510649378
- DOI
- 10.1117/12.2612565
- language
- English
- LU publication?
- yes
- id
- 79b745bf-5e9d-4bfc-a972-023d53ee7a82
- date added to LUP
- 2022-08-22 12:20:10
- date last changed
- 2022-08-22 12:20:10
@inproceedings{79b745bf-5e9d-4bfc-a972-023d53ee7a82,
abstract = {{<p>Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval, respectively. DBT projections and reconstructions of the phantoms were simulated using two acquisition geometries of our DBT prototype. The performance of both acquisition geometries was compared using breast volume segmentations of the Perlin phantoms. Results show that breast volume estimates are improved with the introduction of posterior-anterior motion of the x-ray source in DBT acquisitions. The breast volume is overestimated in DBT, varying substantially with the acquisition geometry; segmentation errors are more evident for thicker and larger breasts. These results provide additional evidence and suggest that custom acquisition geometries can improve the performance and accuracy in DBT. Perlin phantoms help to identify limitations in acquisition geometries and to optimize the performance of the DBT prototypes.</p>}},
author = {{Teixeira, Joao P.V. and Silva Filho, Telmo M. and Do Rego, Thais G. and Malheiros, Yuri B. and Dustler, Magnus and Bakic, Predrag R. and Vent, Trevor L. and Acciavatti, Raymond J. and Krishnamoorthy, Srilalan and Surti, Suleman and Maidment, Andrew D.A. and Barufaldi, Bruno}},
booktitle = {{Medical Imaging 2022 : Physics of Medical Imaging}},
editor = {{Zhao, Wei and Yu, Lifeng}},
isbn = {{9781510649378}},
issn = {{1605-7422}},
keywords = {{digital breast tomosynthesis; Perlin noise; ray-tracing; virtual clinical trial}},
language = {{eng}},
publisher = {{SPIE}},
series = {{Progress in Biomedical Optics and Imaging - Proceedings of SPIE}},
title = {{Novel Perlin-based phantoms using 3D models of compressed breast shapes and fractal noise}},
url = {{http://dx.doi.org/10.1117/12.2612565}},
doi = {{10.1117/12.2612565}},
volume = {{12031}},
year = {{2022}},
}