Abstract
The purpose of this work was to implement and evaluate the performance of a 3D Total Variation (TV) minimization filter for Poisson noise and apply it to 3D digital breast tomosynthesis (DBT) data. The value of Lagrange multiplier (λ) to be used in filter equation has a direct relationship with the results obtained. Some preliminary studies about λ values were done.
A Mammographic Accreditation Phantom Model 156 was acquired and its biggest tumor-like mass and cluster of microcalcifications were used for image quality assessment. The proposed methodology was also tested with one clinical DBT data set.
For 3D filter performance analysis: a reduction of 41.08 % and 38.60 % in 3D TV was achieved when a constant or variable λ value is used over slices, respectively. Either for constant or variable λ, the artifact spread function was improved, when compared to the unfiltered data. For the in-plane analysis: when constant λ is used, a reduction of 37.02 % in TV, an increase of 47.72 % in contrast to noise ratio (CNR) and a deterioration of 0.15 % in spatial resolution were obtained. For a variable λ, a reduction of 37.12 % in TV, an increase of 42.66 % in CNR and an improvement of 18.85 % in spatial resolution were achieved. A visual inspection of unfiltered and filtered clinical images demonstrates the quantitative values achieved with the phantom, where areas with higher noise level become smoother while preserving edges and details of the structures (about 43 % of TV reduction).
Both quantitative and qualitative analysis performed in this study confirmed the relevance of this approach in improving image quality in DBT imaging.
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Acknowledgments
This work was supported in part by Fundação para a Ciência e a Tecnologia - Portugal (projects Pest-OE/SAU/IU0645/2013, Pest-OE/SAU/IU0645/2014 and PTDC/BBB-IMG/3310/2012).
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Mota, A.M., Oliveira, N., Almeida, P., Matela, N. (2016). 3D Total Variation Minimization Filter for Breast Tomosynthesis Imaging. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_63
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