Evidence map›Paper›PMID 25341407›Full record

ArticleThe international journal of cardiovascular imaging2015

Dual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imaging.

Giovanni J Ughi, Johan Verjans, Ali M Fard, Hao Wang, Eric Osborn, Tetsuya Hara, Adam Mauskapf, Farouc A Jaffer, Guillermo J Tearney

Open access · greenAbstract readValidation Study
In one paragraph

Article in The international journal of cardiovascular imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
7.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 56 citations in OpenAlex.

  1. Ultrathin-strut versus thin-strut stent healing and outcomes in preclinical and clinical subjects.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2024
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  7. Intravascular Fluorescence Molecular Imaging of Atherosclerosis.Methods in molecular biology (Clifton, N.J.) · 2022
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  11. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 2 countries.

Giovanni J UghiWellman Center for Photomedicine, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA, giovanni.ughi@mgh.harvard.edu.
Johan Verjans
Ali M Fard
Hao Wang
Eric Osborn
Tetsuya Hara
Adam Mauskapf
Farouc A Jaffer
Guillermo J Tearney
Harvard University · USMassachusetts General Hospital · US

Funding

Harvard Clinical and Translational Science CenterUL1TR001102 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2013 to 2017
$106.2M
Chemical analysis of coronary atherosclerosis in patientsR01HL093717 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI TEARNEY, GUILLERMO J · 2008 to 2011
$3.3M
Intravascular Fluorescence Molecular Imaging of Inflammation in AtherosclerosisR01HL108229 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI JAFFER, FAROUC AMIN · 2010 to 2014
$2.1M
NIRF-OFDI of Inflammation in Atheroma Progression and Stent ComplicationsR01HL122388 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI JAFFER, FAROUC AMIN · 2015 to 2018
$2.1M
NCATS NIH HHS 1UL1 TR001102-01NCATS NIH HHS UL1 TR001102NHLBI NIH HHS R01 HL093717NHLBI NIH HHS R01HL093717NHLBI NIH HHS R01 HL108229NHLBI NIH HHS R01HL108229NHLBI NIH HHS R01 HL122388
6 · The paper itself

Abstract

Intravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT-NIRF systems acquire data at a very high frame-rate (>100 frames/s), a high number of images per pullback need to be analyzed, making manual processing of OCT-NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT-NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from six New Zealand White rabbit atherosclerotic after indocyanine-green injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 µm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from eight different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT-NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.

Indexed as

AlgorithmsAnimalsAutomationCoronary Artery DiseaseCoronary VesselsDisease Models, AnimalFluorescent DyesHumansImage Interpretation, Computer-AssistedIndocyanine GreenMolecular ImagingOptical ImagingPredictive Value of TestsRabbitsReproducibility of ResultsSpectroscopy, Near-InfraredFluorescent DyesIndocyanine Green

Identifiers

PMID25341407
PMCPMC4344893
OpenAlexW1978406326

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.