Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Lu, Yao; Chan, Heang Ping; Wei, Jun; Hadjiiski, Lubomir M.; Samala, Ravi K.

doi:10.1118/1.4903283

Cited by 24 publications

(39 citation statements)

References 57 publications

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“…The slice spacing in the Z direction was chosen to be 1 mm. The details of our reconstruction method have been described elsewhere (Zhang et al 2006, Lu et al 2010, Lu et al 2015). …”

Section: Methodsmentioning

confidence: 99%

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Chan

Wei

et al. 2017

Phys. Med. Biol.

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In digital breast tomosynthesis (DBT), the high-attenuation metallic clips marking a previous biopsy site in the breast cause errors in the estimation of attenuation along the ray paths intersecting the markers during reconstruction, which result in interplane and inplane artifacts obscuring the visibility of subtle lesions. We proposed a new metal artifact reduction (MAR) method to improve image quality. Our method uses automatic detection and segmentation to generate a marker location map for each projection (PV). A voting technique based on the correlation among different PVs is designed to reduce false positives (FPs) and to label the pixels on the PVs and the voxels in the imaged volume that represent the location and shape of the markers. An iterative diffusion method replaces the labeled pixels on the PVs with estimated tissue intensity from the neighboring regions while preserving the original pixel values in the neighboring regions. The inpainted PVs are then used for DBT reconstruction. The markers are repainted on the reconstructed DBT slices for radiologists’ information. The MAR method is independent of reconstruction techniques or acquisition geometry. For the training set, the method achieved 100% success rate with one FP in 19 views. For the test set, the success rate by view was 97.2% for core biopsy microclips and 66.7% for clusters of large post-lumpectomy markers with a total of 10 FPs in 58 views. All FPs were large dense benign calcifications that also generated artifacts if they were not corrected by MAR. For the views with successful detection, the metal artifacts were reduced to a level that was not visually apparent in the reconstructed slices. The visibility of breast lesions obscured by the reconstruction artifacts from the metallic markers was restored.

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“…The slice spacing in the Z direction was chosen to be 1 mm. The details of our reconstruction method have been described elsewhere (Zhang et al 2006, Lu et al 2010, Lu et al 2015). …”

Section: Methodsmentioning

confidence: 99%

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Chan

Wei

et al. 2017

Phys. Med. Biol.

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“…MSBF regularization is developed to enhance microcalcifications while preserving spiculations and reducing the noise 11 . The input DBT volume is processed using CNR enhancement and multiscale calcification response (MCR) to generate an enhancement-modulated calcification response (EMCR) volume.…”

Section: Cad Dbtmentioning

confidence: 99%

“…An MQSA approved radiologist marked the biopsy-proven cluster location with a 3-dimensional (3D) bounding box. SART with MSBF regularization was used to reconstruct DBT volumes and the corresponding PPJ images 11 . Fig.…”

Section: Data Setmentioning

confidence: 99%

“…For the same CAD DBT method, additional FP reduction of microcalcifications based on a random-forest classifier was designed 10 . To increase the detectability of MCs in the reconstructed DBT slices, we developed a multiscale bilateral filtering (MSBF) regularization method for the simultaneous algebraic reconstruction technique (SART) which can selectively enhance small and high contrast objects such as microcalcifications while suppressing noise without blurring subtle spiculations 11 . During the MSBFregularized reconstruction, a 2D planar projection (PPJ) image 11,12 is generated from the high-frequency components of the multiscale decomposed DBT slices.…”

Section: Introductionmentioning

confidence: 99%

“…To increase the detectability of MCs in the reconstructed DBT slices, we developed a multiscale bilateral filtering (MSBF) regularization method for the simultaneous algebraic reconstruction technique (SART) which can selectively enhance small and high contrast objects such as microcalcifications while suppressing noise without blurring subtle spiculations 11 . During the MSBFregularized reconstruction, a 2D planar projection (PPJ) image 11,12 is generated from the high-frequency components of the multiscale decomposed DBT slices. As input to the CAD system, the PPJ image has several advantages, including free of low-frequency background, enhanced microcalcifications, and having a smaller search space for MCs compared to the DBT volume.…”

Section: Introductionmentioning

confidence: 99%

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Digital breast tomosynthesis: application of 2D digital mammography CAD to detection of microcalcification clusters on planar projection image

Samala

Chan

et al. 2015

SPIE Proceedings

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Computer-aided detection (CAD) has the potential to aid radiologists in detection of microcalcification clusters (MCs). CAD for digital breast tomosynthesis (DBT) can be developed by using the reconstructed volume, the projection views or other derivatives as input. We have developed a novel method of generating a single planar projection (PPJ) image from a regularized DBT volume to emphasize the high contrast objects such as microcalcifications while removing the anatomical background and noise. In this work, we adapted a CAD system developed for digital mammography (CAD DM ) to the PPJ image and compared its performance with our CAD system developed for DBT volumes (CAD DBT ) in the same set of cases. For microcalcification detection in the PPJ image using the CAD DM system, the background removal preprocessing step designed for DM was not needed. The other methods and processing steps in the CAD DM system were kept without modification while the parameters were optimized with a training set. The linear discriminant analysis classifier using cluster based features was retrained to generate a discriminant score to be used as decision variable. For view-based FROC analysis, at 80% sensitivity, an FP rate of 1.95/volume and 1.54/image were achieved, respectively, for CAD DBT and CAD DM in an independent test set. At a threshold of 1.2 FPs per image or per DBT volume, the nonparametric analysis of the area under the FROC curve shows that the optimized CAD DM for PPJ is significantly better than CAD DBT . However, the performance of CAD DM drops at higher sensitivity or FP rate, resulting in similar overall performance between the two CAD systems. The higher sensitivity of the CAD DM in the low FP rate region and vice versa for the CAD DBT indicate that a joint CAD system combining detection in the DBT volume and the PPJ image has the potential to increase the sensitivity and reduce the FP rate.

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Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images

et al. 2023

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BackgroundThe noise in digital breast tomosynthesis (DBT) includes x‐ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs).PurposeWe previously developed a deep‐learning‐based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep‐learning‐based denoising to improve the detection of MCs in DBT.MethodsWe have a modular breast phantom set containing seven 1‐cm‐thick heterogeneous 50% adipose/50% fibroglandular slabs custom‐made by CIRS, Inc. (Norfolk, VA). We made six 5‐cm‐thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125–0.150, 0.150–0.180, 0.180–0.212, 0.212–0.250 mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists’ reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter‐balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs.ResultsThe average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p < 0.005, two‐tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9 ± 4.6, 2.8 ± 3.7, and 2.7 ± 3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p ≤ 0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction.ConclusionsThis observer study using breast phantom images showed that deep‐learning‐based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists’ confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

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Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Cited by 24 publications

References 57 publications

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction

Digital breast tomosynthesis: application of 2D digital mammography CAD to detection of microcalcification clusters on planar projection image

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images

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