Experimental evaluation of seven quality control phantoms for digital breast tomosynthesis

Sage, Julie; Fezzani, K.; Fitton, Isabelle; Hadid, L.; Moussier, A.; Pierrat, N.; Martineau, Antoine; Dreuil, Serge; Heulers, L.; Étard, C.

doi:10.1016/j.ejmp.2018.12.031

Cited by 11 publications

(13 citation statements)

References 14 publications

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“…As previously described by Bliznakova 10 the production of anatomically realistic breast physical phantom is still nowadays a challenge. A structured and heterogeneous background 4,13 can make some targets more or less affected by artifacts and automatic detection more difficult.…”

Section: Discussionmentioning

confidence: 99%

“…A feasibility study 4 comparing image scoring and z ‐resolution between seven commercially phantoms 12 including the Tomomam ® phantom was conducted by the French Institute for Radiation protection and Nuclear Safety (IRSN), for three different X‐ray systems which are currently commercially available in Europe and equipped with a DBT mode: Siemens Mammomat Inspiration, GE HealthCare SenoClaire, and Hologic Selenia Dimensions ® . The Tomomam ® phantom passed all experimental tests: it was one of the three phantoms considered discriminant for image scoring, and the only one for quantification of z ‐resolution by using the aluminum beads.…”

Section: Methodsmentioning

confidence: 99%

“…Digital breast tomosynthesis (DBT) is a diagnostic modality that improves breast cancer detection either in combination with full-field digital mammography (FFDM) or alone, [1][2][3] and might become a standard of care in breast cancer screening. 4,5 This imaging technique involves volumetric reconstruction of the whole breast from a limited number of low-dose two-dimensional (2D) projections acquired over a fixed angular range specific to the manufacturer.…”

Section: Introductionmentioning

confidence: 99%

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Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis

Fitton

Noël²,

Minassian³

et al. 2022

Medical Physics

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To describe the creation process of a new breast phantom specifically designed to monitor quality control (QC) metrics consistency over several months in digital breast tomosynthesis (DBT). Methods: The semi-anthropomorphic Tomomam ® phantom was designed and evaluated twice monthly on a single Hologic Selenia Dimensions ® unit over 5 months. The phantom is manufactured in a one-piece epoxy resin homogeneous material as the basis for manufacturing, simulating breast tissue as 50% equivalent glandular (GL)/50% equivalent adipose (AD) and compressed thickness of 60 mm. The distribution of test objects on different planes inside the phantom should allow the quantification of 10 image quality metrics: reproducibility, signal difference-to-noise ratio (SDNR), geometric distortions in the plane, missing or added tissue at chest wall, at the top and bottom of images stack and lateral sides, in-plane homogeneity, image scoring, artifact spread function (ASF), geometric distortions in the volume. SDNR was quantified according to GL and AD tissues. Tolerance criteria per parameter were described to analyze results over the study time. Results: Mean scores were equal to 15.4, 15.0, and 11.6 for masses, microcalcifications, and fibers, respectively. A large difference between GL and AD tissues for SDNR metrics was noted over the study time: the best results were obtained from GL tissues. Both geometric distortions and local homogeneity in the plane conformed to expected values. The mean volume value of the triangular prism was 11.3% greater than the expected value due to a reconstruction height equal to 66 mm instead of 60 mm. Conclusions: In this study, we monitored several QC metrics discriminating GL and AD tissues by using a new breast phantom developed by us. The preliminary clinical tests demonstrated that the Tomomam ® phantom could be used to reliably and efficiently track 10 QC metrics with a single acquisition. More data need to be acquired to refine tolerance criteria for some metrics.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis

Fitton

Noël²,

Minassian³

et al. 2022

Medical Physics

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“…This phantom was selected as other works, not yet reported, will use human observers to evaluate image quality of the same images. The CIRS phantom is one of a few specifically designed phantoms for DBT 32 with materials designed to mimic adipose and gland tissues ‘swirled’ together. The CIRS phantom has previously been shown to be a valid phantom for this type of research where it was also been used where signal measurements have been taken 33 .…”

Section: Methodsmentioning

confidence: 99%

Variation in digital breast tomosynthesis image quality at differing heights above the detector

Davidson

Khalifah

Zhou

2021

J of Medical Radiation Sci

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Introduction The aim of this preliminary work was to determine if image quality in digital breast tomosynthesis (DBT) changes when tomosynthesis image slices were obtained at differing heights above the detector and in differing breast thicknesses. Methods A CIRS Model 020 BR3D breast imaging phantom was used to obtain the DBT images. The images were also acquired at different tube voltages, and each exposure was determined by the automatic exposure control system. Contrast‐to‐noise ratio (CNR) and figure‐of‐merit (FOM) values were obtained and compared. Results At a phantom thickness of 5 cm or greater, there was a significant reduction (P ≤ 0.05) of image CNR values obtained from the images near the top of the phantom to those obtained near the bottom of the phantom. When the phantom thickness was 4 cm, there was no significant difference in CNR values between DBT images acquired at any height in the phantom. FOM values generally showed no difference when images were obtained at differing heights above the detector. Conclusion Image quality, as measured by the CNR, was reduced when tomosynthesis slice image heights were at the top of the phantom and when the thickness of the phantom was more than 4 cm. From this preliminary work, clinicians need to be aware that DBT images obtained near the top of the breast, when breast thickness is greater than 4 cm, may have reduced image quality. Further work is needed to fully assess any DBT image quality changes when images are obtained near the top of the breast.

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“…With the aim of improving its diagnostic performance and hence also helping to reduce the rate of recalls, in the last decades research efforts have been directed towards new pseudo-3D or 3D X-ray breast imaging technologies such as digital breast tomosynthesis (DBT) [3][4][5] and breast computed tomography [6], respectively. These techniques allow to overcomein part or totally, respectivelythe overlap of normal and pathological tissues in the direction of the incident beam, which can decrease the visibility of malignant abnormalities or simulate the appearance of a lesion [7][8][9]. DBT acquires many two-dimensional projections from various angular positions of the X-ray tube around the compressed breast, at a comparable level of radiation dose with respect to DM [5].…”

Section: Introductionmentioning

confidence: 99%

A deep learning classifier for digital breast tomosynthesis

et al. 2021

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To develop a computerized detection system for the automatic classification of the presence/absence of mass lesions in digital breast tomosynthesis (DBT) annotated exams, based on a deep convolutional neural network (DCNN). Materials and Methods: Three DCNN architectures working at image-level (DBT slice) were compared: two stateof-the-art pre-trained DCNN architectures (AlexNet and VGG19) customized through transfer learning, and one developed from scratch (DBT-DCNN). To evaluate these DCNN-based architectures we analysed their classification performance on two different datasets provided by two hospital radiology departments. DBT slice images were processed following normalization, background correction and data augmentation procedures. The accuracy, sensitivity, and area-under-the-curve (AUC) values were evaluated on both datasets, using receiver operating characteristic curves. A Grad-CAM technique was also implemented providing an indication of the lesion position in the DBT slice. Results: Accuracy, sensitivity and AUC for the investigated DCNN are in-line with the best performance reported in the field. The DBT-DCNN network developed in this work showed an accuracy and a sensitivity of (90% ± 4%) and (96% ± 3%), respectively, with an AUC as good as 0.89 ± 0.04. A k-fold cross validation test (with k = 4) showed an accuracy of 94.0% ± 0.2%, and a F1-score test provided a value as good as 0.93 ± 0.03. Grad-CAM maps show high activation in correspondence of pixels within the tumour regions. Conclusions: We developed a deep learning-based framework (DBT-DCNN) to classify DBT images from clinical exams. We investigated also a possible application of the Grad-CAM technique to identify the lesion position.

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Experimental evaluation of seven quality control phantoms for digital breast tomosynthesis

Cited by 11 publications

References 14 publications

Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis

Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis

Variation in digital breast tomosynthesis image quality at differing heights above the detector

A deep learning classifier for digital breast tomosynthesis

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