Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis
Purpose: Detection of subtle microcalcifications in digital breast tomosynthesis (DBT) is a challenging task because of the large, noisy DBT volume. It is important to enhance the contrast‐to‐noise ratio (CNR) of microcalcifications in DBT reconstruction. Most regularization methods depend on local...
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Published in | Medical physics (Lancaster) Vol. 42; no. 1; pp. 182 - 195 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
American Association of Physicists in Medicine
01.01.2015
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Subjects | |
Online Access | Get full text |
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Summary: | Purpose:
Detection of subtle microcalcifications in digital breast tomosynthesis (DBT) is a challenging task because of the large, noisy DBT volume. It is important to enhance the contrast‐to‐noise ratio (CNR) of microcalcifications in DBT reconstruction. Most regularization methods depend on local gradient and may treat the ill‐defined margins or subtle spiculations of masses and subtle microcalcifications as noise because of their small gradient. The authors developed a new multiscale bilateral filtering (MSBF) regularization method for the simultaneous algebraic reconstruction technique (SART) to improve the CNR of microcalcifications without compromising the quality of masses.
Methods:
The MSBF exploits a multiscale structure of DBT images to suppress noise and selectively enhance high frequency structures. At the end of each SART iteration, every DBT slice is decomposed into several frequency bands via Laplacian pyramid decomposition. No regularization is applied to the low frequency bands so that subtle edges of masses and structured background are preserved. Bilateral filtering is applied to the high frequency bands to enhance microcalcifications while suppressing noise. The regularized DBT images are used for updating in the next SART iteration. The new MSBF method was compared with the nonconvex total p‐variation (TpV) method for noise regularization with SART. A GE GEN2 prototype DBT system was used for acquisition of projections at 21 angles in 3° increments over a ±30° range. The reconstruction image quality with no regularization (NR) and that with the two regularization methods were compared using the DBT scans of a heterogeneous breast phantom and several human subjects with masses and microcalcifications. The CNR and the full width at half maximum (FWHM) of the line profiles of microcalcifications and across the spiculations within their in‐focus DBT slices were used as image quality measures.
Results:
The MSBF method reduced contouring artifacts and enhanced the CNR of microcalcifications compared to the TpV method, thus preserving the image quality of the structured background. The MSBF method achieved the highest CNR of microcalcifications among the three methods. The FWHM of the microcalcifications and mass spiculations resulting from the MSBF method was comparable to that without regularization, and superior to that of the TpV method.
Conclusions:
The SART regularized by the multiscale bilateral filtering method enhanced the CNR of microcalcifications and preserved the sharpness of microcalcifications and spiculated masses. The MSBF method provided better image quality of the structured background and was superior to TpV and NR for enhancing microcalcifications while preserving the appearance of mass margins. |
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Bibliography: | Telephone: (734) 647‐8556; Fax: (734) 615‐5513. Present address: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat‐sen University, Guangdong 510275, China. Author to whom correspondence should be addressed. Electronic mail yaol@umich.edu ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author to whom correspondence should be addressed. Electronic mail: yaol@umich.edu; Telephone: (734) 647-8556; Fax: (734) 615-5513. Present address: Guangdong Province Key Laboratory of Computational Science, School of Mathematics and Computational Science, Sun Yat-sen University, Guangdong 510275, China. |
ISSN: | 0094-2405 2473-4209 2473-4209 |
DOI: | 10.1118/1.4903283 |