Evidence map›Paper›PMID 28832336›Full record

ArticlePhysics in medicine and biology2017

Improving image quality for digital breast tomosynthesis: an automated detection and diffusion-based method for metal artifact reduction.

Yao Lu, Heang-Ping Chan, Jun Wei, Lubomir M Hadjiiski, Ravi K Samala

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yao LuDepartment of Radiology, University of Michigan, 1500 E. Medical Center Drive, C477 Med-Inn Bldg, Ann Arbor, MI, United States of America.
Heang-Ping ChanDepartment of Radiology, University of Michigan, 1500 E. Medical Center Drive, C477 Med-Inn Bldg, Ann Arbor, MI, United States of America.
Jun WeiDepartment of Radiology, University of Michigan, 1500 E. Medical Center Drive, C477 Med-Inn Bldg, Ann Arbor, MI, United States of America.
Lubomir M HadjiiskiDepartment of Radiology, University of Michigan, 1500 E. Medical Center Drive, C477 Med-Inn Bldg, Ann Arbor, MI, United States of America.
Ravi K SamalaDepartment of Radiology, University of Michigan, 1500 E. Medical Center Drive, C477 Med-Inn Bldg, Ann Arbor, MI, United States of America.

Funding

Improvement of microcalcification detection in digital breast tomosynthesisR01CA151443 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI CHAN, HEANG-PING · 2011 to 2015
$3.0M
NCI NIH HHS R01 CA151443
6 · The paper itself

Abstract

In digital breast tomosynthesis (DBT), the high-attenuation metallic clips marking a previous biopsy site in the breast cause errors in the estimation of attenuation along the ray paths intersecting the markers during reconstruction, which result in interplane and inplane artifacts obscuring the visibility of subtle lesions. We proposed a new metal artifact reduction (MAR) method to improve image quality. Our method uses automatic detection and segmentation to generate a marker location map for each projection (PV). A voting technique based on the geometric correlation among different PVs is designed to reduce false positives (FPs) and to label the pixels on the PVs and the voxels in the imaged volume that represent the location and shape of the markers. An iterative diffusion method replaces the labeled pixels on the PVs with estimated tissue intensity from the neighboring regions while preserving the original pixel values in the neighboring regions. The inpainted PVs are then used for DBT reconstruction. The markers are repainted on the reconstructed DBT slices for radiologists' information. The MAR method is independent of reconstruction techniques or acquisition geometry. For the training set, the method achieved 100% success rate with one FP in 19 views. For the test set, the success rate by view was 97.2% for core biopsy microclips and 66.7% for clusters of large post-lumpectomy markers with a total of 10 FPs in 58 views. All FPs were large dense benign calcifications that also generated artifacts if they were not corrected by MAR. For the views with successful detection, the metal artifacts were reduced to a level that was not visually apparent in the reconstructed slices. The visibility of breast lesions obscured by the reconstruction artifacts from the metallic markers was restored.

Indexed as

AlgorithmsArtifactsMetalsAutomationBreast NeoplasmsDiffusionFemaleHumansImage Processing, Computer-AssistedMammographyMetals

Identifiers

PMID28832336
PMCPMC5735824

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.