Local versus Whole Breast Volumetric Breast Density Assessments and Implications

Ren, Bin; Smith, Andrew P.; Jing, Zhenxue

doi:10.1007/978-3-642-31271-7_100

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…The AEC of this type of mammography unit adjusts the exposure to account for the densest part of the breast (Ren et al 2012) and is therefore sensitive to breast density. In this study, it was shown that the distribution of AGD for DM is wider than for DBT.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the increase in AGD for DBT is lower for women with dense breasts. The reason for this difference is that the AEC is controlled primarily by breast thickness in DBT mode, while it compensates more for breast density in DM mode (239). For other vendors such as GE, DBT and DM provides a more equal dose level (240), as the radiation dose requirement for DBT and DM is calculated using the same method, and the dose is divided evenly among the DBT projections.…”

Section: Radiation Dose Estimates For Dm and Dbtmentioning

confidence: 99%

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Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

et al. 2018

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The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

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Section: Discussionmentioning

confidence: 99%

Section: Radiation Dose Estimates For Dm and Dbtmentioning

confidence: 99%

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

et al. 2018

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“…For MC cases, lesions were located across different breast quadrants (figure 4(c) for illustration only), and at different breast depths (i.e. the center depth of each quadrant), which resulted in different local background statistics (Pereira et al 2009, Malkov et al 2010, Ren et al 2012. Similarly, four local VOIs at each lesion location were extracted, and combined into a large 2 × 2 VOI, designated as VOI MC (figure 4(f)).…”

Section: Synthetic Breast Lesionsmentioning

confidence: 99%

Model observer for assessing digital breast tomosynthesis for multi-lesion detection in the presence of anatomical noise

et al. 2018

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Model observers are widely used in task-based assessments of medical image quality. The presence of multiple abnormalities in a single set of images, such as in multifocal multicentric breast cancer (MFMC), has an immense clinical impact on treatment planning and survival outcomes. Detecting multiple breast tumors is challenging as MFMC is relatively uncommon, and human observers do not know the number or locations of tumors a priori. Digital breast tomosynthesis (DBT), in which an x-ray beam sweeps over a limited angular range across the breast, has the potential to improve the detection of multiple tumors. However, prior studies of DBT image quality all focus on unifocal breast cancers. In this study, we extended our 2D multi-lesion (ML) channelized Hotelling observer (CHO) into a 3D ML-CHO that detects multiple lesions from volumetric imaging data. Then we employed the 3D ML-CHO to identify optimal DBT acquisition geometries for detection of MFMC. Digital breast phantoms with multiple embedded synthetic lesions were scanned by simulated DBT scanners of different geometries (wide/narrow angular span, different number of projections per scan) to simulate MFMC cases. With new implementations of 3D partial least squares (PLS) and modified Laguerre–Gauss (LG) channels, the 3D ML-CHO made detection decisions based upon the overall information from individual DBT slices and their correlations. Our evaluation results show that: (1) the 3D ML-CHO could achieve good detection performance with a small number of channels, and 3D PLS channels on average outperform the counterpart LG channels; (2) incorporating locally varying anatomical backgrounds and their correlations as in the 3D ML-CHO is essential for multi-lesion detection; (3) the most effective DBT geometry for detection of MFMC may vary when the task of clinical interest changes, and a given DBT geometry may not yield images that are equally informative for detecting MF, MC, and unifocal cancers.

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“…For example, studies (e.g. Pereira et al (2009), Malkov et al (2010 and Ren et al (2012)) have shown that local breast density differs significantly across breast subregions, and the distribution of radiodense breast tissue is spatially autocorrelated. Thus, it is critical for model observers to account for these factors.…”

Section: Introductionmentioning

confidence: 99%

Model observer design for multi-signal detection in the presence of anatomical noise

2017

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As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre-Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.

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Local versus Whole Breast Volumetric Breast Density Assessments and Implications

Cited by 4 publications

References 5 publications

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

Model observer for assessing digital breast tomosynthesis for multi-lesion detection in the presence of anatomical noise

Model observer design for multi-signal detection in the presence of anatomical noise

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