Evidence map›Paper›PMID 25370638›Full record

ArticleMedical physics2014

Real-time out-of-plane artifact subtraction tomosynthesis imaging using prior CT for scanning beam digital x-ray system.

Meng Wu, Rebecca Fahrig

Abstract read
In one paragraph

Article in Medical physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Meng WuDepartment of Electrical Engineering, Stanford University, Stanford, California 94305.
Rebecca FahrigDepartment of Radiology, Stanford University, Stanford, California 94305.

Funding

Ultrafast Tomosynthesis for Transbronchial Biopsy GuidanceR21HL098683 · NHLBI · STANFORD UNIVERSITY · PI FAHRIG, REBECCA · 2010 to 2011
$410k
NHLBI NIH HHS R21 HL098683
6 · The paper itself

Abstract

purposeThe scanning beam digital x-ray system (SBDX) is an inverse geometry fluoroscopic system with high dose efficiency and the ability to perform continuous real-time tomosynthesis in multiple planes. This system could be used for image guidance during lung nodule biopsy. However, the reconstructed images suffer from strong out-of-plane artifact due to the small tomographic angle of the system.

methodsThe authors propose an out-of-plane artifact subtraction tomosynthesis (OPAST) algorithm that utilizes a prior CT volume to augment the run-time image processing. A blur-and-add (BAA) analytical model, derived from the project-to-backproject physical model, permits the generation of tomosynthesis images that are a good approximation to the shift-and-add (SAA) reconstructed image. A computationally practical algorithm is proposed to simulate images and out-of-plane artifacts from patient-specific prior CT volumes using the BAA model. A 3D image registration algorithm to align the simulated and reconstructed images is described. The accuracy of the BAA analytical model and the OPAST algorithm was evaluated using three lung cancer patients' CT data. The OPAST and image registration algorithms were also tested with added nonrigid respiratory motions.

resultsImage similarity measurements, including the correlation coefficient, mean squared error, and structural similarity index, indicated that the BAA model is very accurate in simulating the SAA images from the prior CT for the SBDX system. The shift-variant effect of the BAA model can be ignored when the shifts between SBDX images and CT volumes are within ±10 mm in the x and y directions. The nodule visibility and depth resolution are improved by subtracting simulated artifacts from the reconstructions. The image registration and OPAST are robust in the presence of added respiratory motions. The dominant artifacts in the subtraction images are caused by the mismatches between the real object and the prior CT volume.

conclusionsTheir proposed prior CT-augmented OPAST reconstruction algorithm improves lung nodule visibility and depth resolution for the SBDX system.

Indexed as

ArtifactsSubtraction TechniqueAlgorithmsHumansLung NeoplasmsModels, TheoreticalMovementRadiographic Image EnhancementRespirationTime FactorsTomography, X-Ray Computed

Identifiers

PMID25370638
PMCPMC4218694

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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.