Comparison of microcalcification detectability in FFDM and DBT using a virtual clinical trial

Li, Zhijin; Desolneux, Agnès; Muller, Serge; Carvalho, Pablo Milioni de; Carton, Ann-Katherine

doi:10.1117/12.2293619

Cited by 8 publications

(16 citation statements)

References 2 publications

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“…For the GEHC Pristina, d tr_c was significantly larger for SM than for DM and DBT, while the DBT and DM scores were comparable. According to the CDMAM results, DM outperformed DBT, yet the L1 data do not show this and are more consistent with a virtual clinical study by Li et al 47 in which DBT tended to outperform DM for microcalcification detection. Both phantoms gave poorer results for SM, compared to DM, consistent with Ikejimba et al 26 and contrary to the results of Baldelli et al 17 The T tr results for the Hologic 3Dimensions show reduced performance for SM, compared to DM and DBT, in agreement with Nelson et al 48 for the earlier Hologic Dimensions unit evaluated using the ACR phantom.…”

Section: Small-detail Detectionsupporting

confidence: 75%

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 Detectionsupporting

confidence: 75%

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 covariance matrix Σ is learnt from a database of 400 images, with size equal to the ROIs ones, half of them containing lesion shapes while the others represent healthy tissues. These images are synthetic ones generated by using a software available at GE Healthcare [17]. Various types of background models are used, namely uniform background for the phantom data, and textured background for the clinical data.…”

Section: Resultsmentioning

confidence: 99%

A New Approach For Microcalcification Enhancement In Digital Breast Tomosynthesis Reconstruction

Sghaier

Chouzenoux

Palma³

et al. 2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

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We propose a novel approach aimed to improve the detectability of microcalcifications in Digital Breast Tomosynthesis (DBT) volumes. Hence, our contribution is twofold. First, we formulate the clinical task through a detectability function based on an approach inspired from mathematical model observers. Second, we integrate this new developed clinical-task term in a cost function which is minimized for 3D reconstruction of DBT volumes. Experimental results carried out on both phantom and real clinical data show that the proposed clinical term allows the visibility of microcalcifications to be significantly improved, while preserving an overall high quality of the fully reconstructed volume.

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“…We now describe the learning process that we use to set the covariance matrix Σ. We consider a database of 400 ROIs generated by using a simulation software developed by GE Healthcare Li et al (2018b). The ROIs contain a predefined type of background (e.g., uniform, textured).…”

Section: Methodsmentioning

confidence: 99%

“…More precisely, we selected the optimal combination maximizing the area under ROC curve, by following the CHO framework Platiša et al (2011). Values of (a u , c) equal to (0.8, 30) and (0.6, 30) are used, for uniform and textured background respectively, following previous research works Li et al (2018b). The parameter ζ in ( 10) is chosen equal to 3 2 ∥∆d 0 ∥ 1,2 , leading to satisfying visual results where d 0 is taken as the FBP solution.…”

Section: Methodsmentioning

confidence: 99%

A Novel Task-Based reconstruction approach for digital breast tomosynthesis

Sghaier

Chouzenoux

Pesquet

et al. 2022

Medical Image Analysis

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The reconstruction of a volumetric image from Digital Breast Tomosynthesis (DBT) measurements is an ill-posed inverse problem, for which existing iterative regularized approaches can provide a good solution. However, the clinical task is somehow omitted in the derivation of those techniques, although it plays a primary role in the radiologist diagnosis. In this work, we address this issue by introducing a novel variational formulation for DBT reconstruction, tailored for a specific clinical task, namely the detection of microcalcifications. Our method aims at simultaneously enhancing the detectability performance and enabling a high-quality restoration of the background breast tissues. Our contribution is threefold. First, we introduce an original task-based reconstruction framework through the proposition of a detectability function inspired from mathematical model observers. Second, we propose a novel total-variation regularizer where the gradient field accounts for the different morphological contents of the imaged breast. Third, we integrate the two developed measures into a cost function, minimized thanks to a new form of the Majorize Minimize Memory Gradient (3MG) algorithm. We conduct a numerical comparison of the convergence speed of the proposed method with those of standard convex optimization algorithms. Experimental results show the interest of our DBT reconstruction approach, qualitatively and quantitatively.

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Comparison of microcalcification detectability in FFDM and DBT using a virtual clinical trial

Cited by 8 publications

References 2 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

A New Approach For Microcalcification Enhancement In Digital Breast Tomosynthesis Reconstruction

A Novel Task-Based reconstruction approach for digital breast tomosynthesis

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