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- Torlegård, B., et al.
(författare)
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Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial
- 2020
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Ingår i: 15th International Workshop on Breast Imaging, IWBI 2020. - : SPIE. - 0277-786X .- 1996-756X. - 9781510638310 ; 11513
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Konferensbidrag (refereegranskat)abstract
- Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated.
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| 2. |
- Teixeira, Joao P.V., et al.
(författare)
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Novel Perlin-based phantoms using 3D models of compressed breast shapes and fractal noise
- 2022
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Ingår i: Medical Imaging 2022 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE. - 1605-7422. - 9781510649378 ; 12031
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Konferensbidrag (refereegranskat)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, 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.
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