Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning Article Swipe
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· 2021
· Open Access
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· DOI: https://doi.org/10.1109/tmrb.2021.3093434
Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem of accumulative error in iterative frame stream processing, we deploy an overall rotation estimation between reference orientation and actual OCT image orientation. We train the network with synthetic OCT videos by intentionally adding rotational distortion into real OCT images. As part of this article we discuss the proposed method in two different scanning modes: the first is a conventional pullback mode where the optical components move along the protection sheath, and the second is a self-designed scanning mode where the catheter is globally translated by using an external actuator. The efficiency and robustness of the proposed method are evaluated with synthetic scans as well as real scans under two scanning modes.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmrb.2021.3093434
- OA Status
- green
- Cited By
- 6
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3175608616
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3175608616Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tmrb.2021.3093434Digital Object Identifier
- Title
-
Data Stream Stabilization for Optical Coherence Tomography Volumetric ScanningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-29Full publication date if available
- Authors
-
Guiqiu Liao, Oscar Caravaca Mora, Benoît Rosa, Philippe Zanne, Alexandre Asch, Diego Dall’Alba, Paolo Fiorini, Michel de Mathelin, Florent Nageotte, Michalina J. GoraList of authors in order
- Landing page
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https://doi.org/10.1109/tmrb.2021.3093434Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2112.01134Direct OA link when available
- Concepts
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Optical coherence tomography, Artificial intelligence, Computer science, Computer vision, Distortion (music), Robustness (evolution), Rotation (mathematics), Convolutional neural network, Orientation (vector space), Optics, Physics, Mathematics, Telecommunications, Bandwidth (computing), Geometry, Chemistry, Amplifier, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 3, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
71Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.be | 41 |
| abstract_inverted_index.by | 73, 130, 184 |
| abstract_inverted_index.in | 21, 29, 50, 103, 149 |
| abstract_inverted_index.is | 4, 59, 87, 156, 173, 181 |
| abstract_inverted_index.of | 18, 100, 141, 193 |
| abstract_inverted_index.to | 93 |
| abstract_inverted_index.we | 108, 144 |
| abstract_inverted_index.OCT | 23, 30, 52, 119, 128, 137 |
| abstract_inverted_index.The | 14, 61, 189 |
| abstract_inverted_index.and | 89, 117, 170, 191 |
| abstract_inverted_index.are | 197 |
| abstract_inverted_index.can | 40 |
| abstract_inverted_index.for | 10, 70 |
| abstract_inverted_index.map | 82 |
| abstract_inverted_index.the | 22, 35, 47, 51, 65, 94, 98, 124, 146, 154, 162, 167, 171, 179, 194 |
| abstract_inverted_index.tip | 25 |
| abstract_inverted_index.two | 84, 150, 208 |
| abstract_inverted_index.CNN. | 95 |
| abstract_inverted_index.data | 56 |
| abstract_inverted_index.each | 71 |
| abstract_inverted_index.into | 135 |
| abstract_inverted_index.mode | 160, 177 |
| abstract_inverted_index.move | 165 |
| abstract_inverted_index.part | 140 |
| abstract_inverted_index.real | 136, 205 |
| abstract_inverted_index.this | 38, 142 |
| abstract_inverted_index.well | 203 |
| abstract_inverted_index.with | 126, 199 |
| abstract_inverted_index.(OCT) | 3 |
| abstract_inverted_index.along | 166 |
| abstract_inverted_index.error | 102 |
| abstract_inverted_index.first | 63, 155 |
| abstract_inverted_index.frame | 105 |
| abstract_inverted_index.image | 120 |
| abstract_inverted_index.input | 92 |
| abstract_inverted_index.organ | 12 |
| abstract_inverted_index.scans | 201, 206 |
| abstract_inverted_index.solve | 97 |
| abstract_inverted_index.speed | 17 |
| abstract_inverted_index.train | 123 |
| abstract_inverted_index.under | 207 |
| abstract_inverted_index.using | 74, 185 |
| abstract_inverted_index.where | 161, 178 |
| abstract_inverted_index.(CNN). | 79 |
| abstract_inverted_index.(NURD) | 69 |
| abstract_inverted_index.B-scan | 72 |
| abstract_inverted_index.Neural | 77 |
| abstract_inverted_index.actual | 118 |
| abstract_inverted_index.adding | 132 |
| abstract_inverted_index.causes | 26 |
| abstract_inverted_index.deploy | 109 |
| abstract_inverted_index.image, | 53 |
| abstract_inverted_index.method | 148, 196 |
| abstract_inverted_index.modes. | 210 |
| abstract_inverted_index.modes: | 153 |
| abstract_inverted_index.second | 172 |
| abstract_inverted_index.stream | 106 |
| abstract_inverted_index.videos | 129 |
| abstract_inverted_index.B-scans | 86 |
| abstract_inverted_index.Network | 78 |
| abstract_inverted_index.Optical | 0 |
| abstract_inverted_index.article | 143 |
| abstract_inverted_index.between | 83, 114 |
| abstract_inverted_index.correct | 46 |
| abstract_inverted_index.discuss | 145 |
| abstract_inverted_index.further | 45 |
| abstract_inverted_index.images. | 138 |
| abstract_inverted_index.imaging | 8 |
| abstract_inverted_index.luminal | 11 |
| abstract_inverted_index.medical | 7 |
| abstract_inverted_index.network | 125 |
| abstract_inverted_index.optical | 19, 163 |
| abstract_inverted_index.overall | 111 |
| abstract_inverted_index.problem | 99 |
| abstract_inverted_index.sheath, | 169 |
| abstract_inverted_index.catheter | 24, 180 |
| abstract_inverted_index.computed | 88 |
| abstract_inverted_index.emerging | 6 |
| abstract_inverted_index.external | 187 |
| abstract_inverted_index.globally | 182 |
| abstract_inverted_index.modality | 9 |
| abstract_inverted_index.process, | 37 |
| abstract_inverted_index.proposed | 147, 195 |
| abstract_inverted_index.provided | 90 |
| abstract_inverted_index.pullback | 159 |
| abstract_inverted_index.reduced. | 43 |
| abstract_inverted_index.rotation | 16, 112 |
| abstract_inverted_index.scanning | 36, 152, 176, 209 |
| abstract_inverted_index.Coherence | 1 |
| abstract_inverted_index.actuator. | 188 |
| abstract_inverted_index.algorithm | 58, 62 |
| abstract_inverted_index.different | 151 |
| abstract_inverted_index.estimates | 64 |
| abstract_inverted_index.evaluated | 198 |
| abstract_inverted_index.improving | 34 |
| abstract_inverted_index.iterative | 104 |
| abstract_inverted_index.partially | 42 |
| abstract_inverted_index.proposed. | 60 |
| abstract_inverted_index.reference | 115 |
| abstract_inverted_index.scanning. | 32 |
| abstract_inverted_index.synthetic | 127, 200 |
| abstract_inverted_index.Distortion | 68 |
| abstract_inverted_index.Rotational | 67 |
| abstract_inverted_index.Tomography | 2 |
| abstract_inverted_index.components | 20, 164 |
| abstract_inverted_index.diagnosis. | 13 |
| abstract_inverted_index.distortion | 28, 49, 134 |
| abstract_inverted_index.efficiency | 190 |
| abstract_inverted_index.estimation | 113 |
| abstract_inverted_index.protection | 168 |
| abstract_inverted_index.robustness | 192 |
| abstract_inverted_index.rotational | 27, 48, 133 |
| abstract_inverted_index.successive | 85 |
| abstract_inverted_index.translated | 183 |
| abstract_inverted_index.volumetric | 31, 55 |
| abstract_inverted_index.Non-Uniform | 66 |
| abstract_inverted_index.correlation | 81 |
| abstract_inverted_index.instability | 39 |
| abstract_inverted_index.orientation | 116 |
| abstract_inverted_index.processing, | 107 |
| abstract_inverted_index.accumulative | 101 |
| abstract_inverted_index.conventional | 158 |
| abstract_inverted_index.non-constant | 15 |
| abstract_inverted_index.orientation. | 121 |
| abstract_inverted_index.Convolutional | 76 |
| abstract_inverted_index.intentionally | 131 |
| abstract_inverted_index.self-designed | 175 |
| abstract_inverted_index.stabilization | 57 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.60516063 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |