Robust Myocardial Perfusion MRI Quantification With DeepFermi Article Swipe
Sherine Brahma
,
Andreas Kofler
,
Felix Zimmermann
,
Tobias Schaeffter
,
Amedeo Chiribiri
,
Christoph Kolbitsch
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tbme.2024.3485233
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tbme.2024.3485233
Stress perfusion cardiac magnetic resonance is an important technique for examining and assessing the blood supply of the myocardium. Currently, the majority of clinical perfusion scans are evaluated based on visual assessment by experienced clinicians. This makes the process subjective, and to this end, quantitative methods have been proposed to offer a more user-independent assessment of perfusion. These methods, however, rely on time-consuming deconvolution analysis and are susceptible to data outliers caused by artifacts due to cardiac or respiratory motion. In our work, we introduce a novel deep-learning method that integrates the commonly used Fermi function with a neural network architecture for fast, accurate, and robust myocardial perfusion quantification. This approach employs the Fermi model to ensure that the perfusion maps are consistent with measured data, while also utilizing a prior based on a 3D convolutional neural network to generalize spatio-temporal information across different patient data. Our network is trained within a self-supervised learning framework, which circumvents the need for ground-truth perfusion labels that are challenging to obtain. Furthermore, we extended this training methodology by adopting a technique that ensures estimations are resistant to data outliers, thereby improving robustness against motion artifacts. Our simulation experiments demonstrated an overall improvement in the accuracy and robustness of perfusion parameter estimation, consistently outperforming traditional deconvolution analysis algorithms across varying Signal-to-Noise Ratio scenarios in the presence of data outliers. For the in vivo studies, our method generated robust perfusion estimates that aligned with clinical diagnoses, while being approximately five times faster than conventional algorithms.
Related Topics
Concepts
Robustness (evolution)
Deconvolution
Outlier
Computer science
Artificial intelligence
Pattern recognition (psychology)
Convolutional neural network
Perfusion scanning
Ground truth
Perfusion
Deep learning
Artificial neural network
Machine learning
Computer vision
Algorithm
Radiology
Medicine
Gene
Chemistry
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tbme.2024.3485233
- OA Status
- hybrid
- Cited By
- 1
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403676981
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403676981Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tbme.2024.3485233Digital Object Identifier
- Title
-
Robust Myocardial Perfusion MRI Quantification With DeepFermiWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-10-23Full publication date if available
- Authors
-
Sherine Brahma, Andreas Kofler, Felix Zimmermann, Tobias Schaeffter, Amedeo Chiribiri, Christoph KolbitschList of authors in order
- Landing page
-
https://doi.org/10.1109/tbme.2024.3485233Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/tbme.2024.3485233Direct OA link when available
- Concepts
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Robustness (evolution), Deconvolution, Outlier, Computer science, Artificial intelligence, Pattern recognition (psychology), Convolutional neural network, Perfusion scanning, Ground truth, Perfusion, Deep learning, Artificial neural network, Machine learning, Computer vision, Algorithm, Radiology, Medicine, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.presence | 221 |
| abstract_inverted_index.proposed | 48 |
| abstract_inverted_index.studies, | 229 |
| abstract_inverted_index.training | 172 |
| abstract_inverted_index.accurate, | 103 |
| abstract_inverted_index.artifacts | 73 |
| abstract_inverted_index.assessing | 12 |
| abstract_inverted_index.different | 143 |
| abstract_inverted_index.estimates | 235 |
| abstract_inverted_index.evaluated | 27 |
| abstract_inverted_index.examining | 10 |
| abstract_inverted_index.generated | 232 |
| abstract_inverted_index.important | 7 |
| abstract_inverted_index.improving | 187 |
| abstract_inverted_index.introduce | 84 |
| abstract_inverted_index.outliers, | 185 |
| abstract_inverted_index.outliers. | 224 |
| abstract_inverted_index.parameter | 206 |
| abstract_inverted_index.perfusion | 1, 24, 107, 119, 161, 205, 234 |
| abstract_inverted_index.resistant | 182 |
| abstract_inverted_index.resonance | 4 |
| abstract_inverted_index.scenarios | 218 |
| abstract_inverted_index.technique | 8, 177 |
| abstract_inverted_index.utilizing | 128 |
| abstract_inverted_index.Currently, | 19 |
| abstract_inverted_index.algorithms | 213 |
| abstract_inverted_index.artifacts. | 191 |
| abstract_inverted_index.assessment | 31, 54 |
| abstract_inverted_index.consistent | 122 |
| abstract_inverted_index.diagnoses, | 240 |
| abstract_inverted_index.framework, | 154 |
| abstract_inverted_index.generalize | 139 |
| abstract_inverted_index.integrates | 90 |
| abstract_inverted_index.myocardial | 106 |
| abstract_inverted_index.perfusion. | 56 |
| abstract_inverted_index.robustness | 188, 203 |
| abstract_inverted_index.simulation | 193 |
| abstract_inverted_index.algorithms. | 249 |
| abstract_inverted_index.challenging | 165 |
| abstract_inverted_index.circumvents | 156 |
| abstract_inverted_index.clinicians. | 34 |
| abstract_inverted_index.estimation, | 207 |
| abstract_inverted_index.estimations | 180 |
| abstract_inverted_index.experienced | 33 |
| abstract_inverted_index.experiments | 194 |
| abstract_inverted_index.improvement | 198 |
| abstract_inverted_index.information | 141 |
| abstract_inverted_index.methodology | 173 |
| abstract_inverted_index.myocardium. | 18 |
| abstract_inverted_index.respiratory | 78 |
| abstract_inverted_index.subjective, | 39 |
| abstract_inverted_index.susceptible | 67 |
| abstract_inverted_index.traditional | 210 |
| abstract_inverted_index.Furthermore, | 168 |
| abstract_inverted_index.architecture | 100 |
| abstract_inverted_index.consistently | 208 |
| abstract_inverted_index.conventional | 248 |
| abstract_inverted_index.demonstrated | 195 |
| abstract_inverted_index.ground-truth | 160 |
| abstract_inverted_index.quantitative | 44 |
| abstract_inverted_index.approximately | 243 |
| abstract_inverted_index.convolutional | 135 |
| abstract_inverted_index.deconvolution | 63, 211 |
| abstract_inverted_index.deep-learning | 87 |
| abstract_inverted_index.outperforming | 209 |
| abstract_inverted_index.time-consuming | 62 |
| abstract_inverted_index.Signal-to-Noise | 216 |
| abstract_inverted_index.quantification. | 108 |
| abstract_inverted_index.self-supervised | 152 |
| abstract_inverted_index.spatio-temporal | 140 |
| abstract_inverted_index.user-independent | 53 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.70909517 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |