Digital Breast Tomosynthesis: Dose and image quality characterization

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

doi:10.1016/j.ejmp.2016.07.610

Cited by 1 publication

(1 citation statement)

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“…However, the optimal radiation doses for diagnostic DBT scans, and how to efficiently use the limited available dose to ensure the best diagnostic image quality, are research questions that remain to be answered (e.g. Svahn et al (2015), Fezzani et al (2016) and James et al (2017)). Also, concern about reducing radiation dose when designing imaging systems is increasing.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%