Potential of Dose Reduction After Marker Placement With Full-Field Digital Mammography

Riedl, Christopher C.; Jaromi, Silvia; Floery, D.; Pfarl, Georg; Fuchsjaeger, Michael H.; Helbich, Thomas H.

doi:10.1097/01.rli.0000163743.63063.9f

Cited by 11 publications

(5 citation statements)

References 16 publications

(17 reference statements)

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“…However, we did not evaluate the impact of this combination. In addition, most diagnostic performance experiments involve rating images for the presence of abnormalities, with multiple grades ranging from 4 to 100 being used ( 36 37 ). However, such an approach is limited for many diagnostic tasks when they are performed in a clinical setting.…”

Section: Discussionmentioning

confidence: 99%

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Kim

Kang

et al. 2022

J Korean Soc Radiol

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Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution ( p < 0.001); diagnostic quality for calcifications ( p < 0.001); and for masses, asymmetry, or architectural distortion ( p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality ( p = 0.547), lesion visibility ( p = 0.120), and contrast ( p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.

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Section: Discussionmentioning

confidence: 99%

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Kim

Kang

et al. 2022

J Korean Soc Radiol

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“…Moreover, as an extra view, it is expected to contribute to the early detection of breast cancer [15,16]. Finally, low dose mammography images could be applied in post interventional mammography [17], such as marker or wire verification, or in high frequency surveillance.…”

Section: Discussionmentioning

confidence: 99%

Alternative Exposure Parameters and Post Process Noise Reduction Expect Considerable Dose Reduction in Single Mammography Views—Initial Experience on Mastectomy Specimens

Bluekens¹,

Engen²,

Karssemeijer³

et al. 2013

ABCR

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Objective: To experimentally indicate a lower limit of dose in mammographic imaging yielding adequate image quality for complementary diagnostic views, by evaluation of image series with different exposure parameters and additional image processing on mastectomy specimens with diverse pathology. Methods: Image series were obtained on seven specimens with different target-filter combinations at different exposure values. Three experienced radiologists assessed the lowest acceptable dose level per specimen using a relative grading technique. With the standard image as reference, fibroglandular tissue and pathological structures, including microcalcifications, were evaluated. Subsequently, a series of pixel binning processes was tested and subjectively assessed on the selected images. Results: The lowest dose level at which image quality was acceptable, and achieved with a W/Ag target-filter combination at 32 kV and 4 mAs. These images can be acquired with 10.4% to 22.4% of the average glandular dose in standard images. Post process pixel binning added to the interpretability of such low dose images. Conclusion: This specimen study suggests that dose level of mammography images might be reduced substantially by general application of a W/Ag spectrum, particularly when combined with post process noise reduction. Future studies should focus on the feasibility of this technique in clinical mammography.

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“…Since tomosynthesis is a 3D technique, it is believed that only one view needs to be acquired. 16 In our example, a comparable breast would have been exposed to an average glandular dose (AGD) of about 1.5 mGy using a modern digital mammography system with automatic parameter selection, compared to 2 mGy calculated AGD for tomosynthesis images of our phantom (1.33 times more dose for tomosynthesis compared to a "normal" digital mammography). The especially designed phantom used in this study has a total height of 5 cm (size xy-direction is 10×10 cm) and is composed of six slabs: three slabs of 1-cm-thick breast equivalent material, two slabs of 0.5-cm-thick breast equivalent material containing different objects of interest simulating lesions (slabs A and B) and a 1-cm thick slab of swirl material placed on top of the phantom to simulate breast textured tissue.…”

Section: Tomosynthesis Of the Phantommentioning

confidence: 99%

Thick Slices from Tomosynthesis Data Sets: Phantom Study for the Evaluation of Different Algorithms

et al. 2007

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Tomosynthesis is a 3-dimensional mammography technique that generates thin slices separated one to the other by typically 1 mm from source data sets. The relatively high image noise in these thin slices raises the value of 1-cm thick slices computed from the set of reconstructed slices for image interpretation. In an initial evaluation, we investigated the potential of different algorithms for generating thick slices from tomosynthesis source data (maximum intensity projection-MIP; average algorithm-AV, and image generation by means of a new algorithm, so-called softMip). The three postprocessing techniques were evaluated using a homogeneous phantom with one textured slab with a total thickness of about 5 cm in which two 0.5-cm-thick slabs contained objects to simulate microcalcifications, spiculated masses, and round masses. The phantom was examined by tomosynthesis (GE Healthcare). Microcalcifications were simulated by inclusion of calcium particles of four different sizes. The slabs containing the inclusions were examined in two different configurations: adjacent to each other and close to the detector and with the two slabs separated by two 1-cm thick breast equivalent material slabs. The reconstructed tomosynthesis slices were postprocessed using MIP, AV, and softMip to generate 1-cm thick slices with a lower noise level. The three postprocessing algorithms were assessed by calculating the resulting contrast versus background for the simulated microcalcifications and contrast-to-noise ratios (CNR) for the other objects. The CNRs of the simulated round and spiculated masses were most favorable for the thick slices generated with the average algorithm, followed by softMip and MIP. Contrast of the simulated microcalcifications was best for MIP, followed by softMip and average projections. Our results suggest that the additional generation of thick slices may improve the visualization of objects in tomosynthesis. This improvement differs from the different algorithms for microcalcifications, speculated objects, and round masses. SoftMip is a new approach combining features of MIP and average showing image properties in between MIP and AV.

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Potential of Dose Reduction After Marker Placement With Full-Field Digital Mammography

Abstract: Our results indicate that under evaluation of each individual case, a dose reduction of 50% to 75% can be recommended in postinterventional digital mammograms.

Cited by 11 publications

References 16 publications

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study

Alternative Exposure Parameters and Post Process Noise Reduction Expect Considerable Dose Reduction in Single Mammography Views—Initial Experience on Mastectomy Specimens

Thick Slices from Tomosynthesis Data Sets: Phantom Study for the Evaluation of Different Algorithms

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