Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography

Petropoulos, Andreas E.; Skiadopoulos, Spyros; Karahaliou, Anna; Messaris, Gerasimos A. T.; Arikidis, Nikolaos; Costaridou, Lena

doi:10.1007/s11517-019-02072-0

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…The contrast of larger microcalcifications can be enhanced by the SM algorithm, while smaller calcifications can disappear on the SM (see Figure 6f for the Siemens Revelation). Petropoulos et al 28 imaged the Leeds TORMAM test object (Leeds Test Objects) on a Hologic Dimensions system to study how microcalcification simulating specks were represented in DM, DBT, and SM images. There was no difference in the signal difference-to-noise ratio (SDNR) in DBT and DM images for smaller calcifications (0.106-0.177 mm) but SDNR was lower for SM images.…”

Section: Small-detail Detectionmentioning

confidence: 99%

“…This is especially important for SM mode, where there are rapid developments in SM algorithms. There have been developments in physical phantom design and implementation, yet there are few studies using phantoms in comparative studies of DM, DBT, and SM imaging modes, 17,[26][27][28] especially across a range of vendors. There is a need for more performance testing for SM in direct comparison with DM and DBT, and this can be done more efficiently and quickly with phantoms than with clinical trials.…”

Section: Introductionmentioning

confidence: 99%

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The impact on lesion detection via a multi‐vendor study: A phantom‐based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography

et al. 2021

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The aim of this study is to perform a test object-based comparison of the imaging performance of digital mammography (DM), digital breast tomosynthesis (DBT), and synthetic mammography (SM). Methods: Two test objects were used, the CDMAM and the L1-structured phantom. Small-detail detectability was assessed using CDMAM and the microcalcification simulating specks in the L1-structured background. Detection of spiculated and non-spiculated mass-like objects was assessed using the L1 phantom. Six different systems were included: Amulet Innovality (Fujifilm), Senographe Pristina (GEHC), 3Dimensions (Hologic), Giotto Class (IMS), Clarity 2D/3D (Planmed), and Mammomat Revelation (Siemens). Images were acquired under automatic exposure control (AEC) and at adjusted levels of AEC/2 and 2 × AEC level. Threshold gold thickness (T tr ) was established for the 0.13-mm-diameter CDMAM discs. Threshold diameters for the calcifications (d tr_c ), the spiculated masses (d tr_sm ), and for the non-spiculated masses (d tr_nsm ) were established. The threshold condition was defined as the thickness or diameter for a 62.5% correct score. Results: T tr for DM was generally superior to DBT, which in turn was superior to SM, but for most systems, these differences between modes were not significant. For L1, no significant differences in d tr_c were found between DM and DBT. The increase in d tr_c from DM to SM at AEC dose was 1%, 19%, 11%, 14%, 46%, and 27% for the Fujifilm, GEHC, Hologic, IMS, Planmed, and Siemens, respectively, indicating significantly poorer performance for all vendors except for Fujifilm, Hologic, and IMS. For both mass types, DBT performed better than SM, while SM showed no significant difference with DM (except for Fujifilm spiculated masses). The dose had an impact on small-detail detectability for both phantoms but did not influence the detection of either mass type. Conclusions: Both phantoms indicated potentially reduced small-detail detectability for SM versus DM and DBT and should therefore not be used in stand-alone mode. The L1 phantom demonstrated no significant difference in microcalcification detection between DM and DBT and also demonstrated the superiority of DBT, compared to DM for mass detection, for all six systems.

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Section: Small-detail Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact on lesion detection via a multi‐vendor study: A phantom‐based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography

et al. 2021

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“…DBT can evaluate the dense breast tissues in detail by describing the breast tissues in three dimensions to overcome the limitation that standard mammography only displays a two-dimensional image. In addition, the reconstructed DBT slice images can partially reduce the often called "anatomical" or "structure" noise caused by tissue superimposition in conventional mammography [5]. It is crucial to segment the breast mass regions in DBT slice images to provide accurate radiologists' qualification.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach

Yang

Wang

et al. 2022

Computational Intelligence and Neuroscience

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To overcome the limitations of conventional breast screening methods based on digital mammography, a quasi-3D imaging technique, digital breast tomosynthesis (DBT) has been developed in the field of breast cancer screening in recent years. In this work, a computer-aided architecture for mass regions segmentation in DBT images using a dilated deep convolutional neural network (DCNN) is developed. First, to improve the low contrast of breast tumour candidate regions and depress the background tissue noise in the DBT image effectively, the constraint matrix is established after top-hat transformation and multiplied with the DBT image. Second, input image patches are generated, and the data augmentation technique is performed to create the training data set for training a dilated DCNN architecture. Then the mass regions in DBT images are preliminarily segmented; each pixel is divided into two different kinds of labels. Finally, the postprocessing procedure removes all false-positives regions with less than 50 voxels. The final segmentation results are obtained by smoothing the boundaries of the mass regions with a median filter. The testing accuracy (ACC), sensitivity (SEN), and the area under the receiver operating curve (AUC) are adopted as the evaluation metrics, and the ACC, SEN, as well as AUC are 86.3%, 85.6%, and 0.852 for segmenting the mass regions in DBT images on the entire data set, respectively. The experimental results indicate that our proposed approach achieves promising results compared with other classical CAD-based frameworks.

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“…However, as DM, synthetic mammography still has the disadvantages of a 2D visualization. For example, in the case of a dense parenchyma pattern or microcalcification clusters, its clinical value is limited due to tissue overlapping (it is used in combination with DBT slice-by-slice) [23]. On the other hand, the construction of slabs has demonstrated good results in terms of reducing time and false positives but originates a lower sensitivity [22].…”

Section: Introductionmentioning

confidence: 99%

Impact of total variation minimization in volume rendering visualization of breast tomosynthesis data

Mota

Clarkson

Almeida

et al. 2020

Computer Methods and Programs in Biomedicine

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Total Variation (TV) minimization algorithms are one compressed sensing technique that has achieved great attention due to the virtue of decrease noise while preserve edges transitions. The purpose of this work is to solve the same TV minimization problem in DBT data, by studying two 3D filters. The obtained results were analyzed at 0º and 90º with a 3D visualization through volume rendering. The filters differ in their application. One considers a slice-by-slice optimization, sequentially traversing all slices of the data. The other considers the intensity values of adjacent slices to make this optimization on each voxel. The performance of each filter was also tested with a clinical case. The results obtained were very encouraging with a significantly increased contrast to noise ratio at 0º and 90º and a small reduction in blur at 90º (slight reduction of the out-of-plane artifact).

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Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography

Cited by 7 publications

References 36 publications

The impact on lesion detection via a multi‐vendor study: A phantom‐based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography

The impact on lesion detection via a multi‐vendor study: A phantom‐based comparison of digital mammography, digital breast tomosynthesis, and synthetic mammography

Automated Segmentation of Mass Regions in DBT Images Using a Dilated DCNN Approach

Impact of total variation minimization in volume rendering visualization of breast tomosynthesis data

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