Deep learning-based methods for image reconstruction in cardiac CT and cardiac cine MRI Article Swipe
Objective: Non-invasive medical imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) are nowadays essential tools for the assessment of cardiac diseases, e.g. coronary artery disease or cardiac dysfunction. The image reconstruction problems in these imaging modalities can be ill-posed for different reasons. For example, in low-dose CT, the measured data is noisy, while in accelerated cardiac MRI, undersampling in k-space leads to incomplete data. Thus, regularization methods must be applied to obtain images suitable for diagnostic purposes. In this thesis, we develop, investigate and evaluate different Neural Networks (NNs)-based methods for image reconstruction in cardiac CT and cardiac cine MRI. Methods: We addressed the reconstruction of low-dose CT and accelerated MR-images using different NNs-based methods. We first performed an ablation study using iterative networks. Then, based on the obtained results and observations, we opted to develop a NNs-based approach, named XT,YT-approach, tailored to the reduction of undersampling artefacts for 2D radial cardiac cine MRI. The approach is based on a NN which is trained on the xt-and yt-spatio-temporal slices which can be extracted from the cine MR images. The XT,YT-approach was then applied to a generalized iterative image reconstruction framework using NN-image priors which we evaluated for 2D radial cine MRI and 3D low-dose CT. Results: The presented XT,YT-method achieved competitive or better results compared to other NNs-based methods and outperformed several other iterative reconstruction methods. Training the NN in spatio-temporal domain has several advantages. First, it is suitable for training on limited datasets. Second, it offers the possibility to highly reduce the number of trainable parameters and therefore prevent the NN from overfitting. Third, the NN is naturally stable with respect to rotation in the xy-plane. Fourth, spatio-temporal correlation is efficiently exploited even by only using 2D convolutional layers. The proposed generalized reconstruction scheme using NN-priors was shown to outperform two other iterative reconstruction methods based on total variation-minimization and learned dictionaries for 3D low-dose CT and 2D radial cardiac cine MRI. Conclusion: Although iterative neural network methods constitute the state-of-the-art for image reconstruction problems, their applicability is currently still limited to relatively small problems. Iterative reconstruction methods using NN-based image priors empirically outperform standard ones and have the potential to reduce the radiation dose exposure in CT and to accelerate the measurements process in MRI.
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Metadata
- Type
- dissertation
- Language
- en
- Landing Page
- https://refubium.fu-berlin.de/handle/fub188/31174
- https://refubium.fu-berlin.de/handle/fub188/31174
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3199614906
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3199614906Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17169/refubium-30910Digital Object Identifier
- Title
-
Deep learning-based methods for image reconstruction in cardiac CT and cardiac cine MRIWork title
- Type
-
dissertationOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Andreas KoflerList of authors in order
- Landing page
-
https://refubium.fu-berlin.de/handle/fub188/31174Publisher landing page
- PDF URL
-
https://refubium.fu-berlin.de/handle/fub188/31174Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://refubium.fu-berlin.de/handle/fub188/31174Direct OA link when available
- Concepts
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Deep learning, Artificial intelligence, Medicine, Radiology, Computer vision, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2022: 1Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.reduction | 148 |
| abstract_inverted_index.resonance | 12 |
| abstract_inverted_index.therefore | 262 |
| abstract_inverted_index.trainable | 259 |
| abstract_inverted_index.xy-plane. | 280 |
| abstract_inverted_index.Objective: | 0 |
| abstract_inverted_index.accelerate | 375 |
| abstract_inverted_index.assessment | 21 |
| abstract_inverted_index.constitute | 333 |
| abstract_inverted_index.diagnostic | 79 |
| abstract_inverted_index.incomplete | 66 |
| abstract_inverted_index.modalities | 39 |
| abstract_inverted_index.outperform | 304, 358 |
| abstract_inverted_index.parameters | 260 |
| abstract_inverted_index.relatively | 347 |
| abstract_inverted_index.techniques | 4 |
| abstract_inverted_index.tomography | 8 |
| abstract_inverted_index.(NNs)-based | 92 |
| abstract_inverted_index.Conclusion: | 327 |
| abstract_inverted_index.accelerated | 58, 113 |
| abstract_inverted_index.advantages. | 238 |
| abstract_inverted_index.competitive | 214 |
| abstract_inverted_index.correlation | 283 |
| abstract_inverted_index.efficiently | 285 |
| abstract_inverted_index.empirically | 357 |
| abstract_inverted_index.generalized | 189, 296 |
| abstract_inverted_index.investigate | 86 |
| abstract_inverted_index.possibility | 252 |
| abstract_inverted_index.Non-invasive | 1 |
| abstract_inverted_index.XT,YT-method | 212 |
| abstract_inverted_index.dictionaries | 316 |
| abstract_inverted_index.dysfunction. | 31 |
| abstract_inverted_index.measurements | 377 |
| abstract_inverted_index.outperformed | 224 |
| abstract_inverted_index.overfitting. | 267 |
| abstract_inverted_index.applicability | 341 |
| abstract_inverted_index.convolutional | 292 |
| abstract_inverted_index.observations, | 135 |
| abstract_inverted_index.undersampling | 61, 150 |
| abstract_inverted_index.XT,YT-approach | 183 |
| abstract_inverted_index.reconstruction | 34, 96, 108, 192, 228, 297, 308, 338, 351 |
| abstract_inverted_index.regularization | 69 |
| abstract_inverted_index.XT,YT-approach, | 144 |
| abstract_inverted_index.spatio-temporal | 234, 282 |
| abstract_inverted_index.state-of-the-art | 335 |
| abstract_inverted_index.yt-spatio-temporal | 171 |
| abstract_inverted_index.variation-minimization | 313 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5010913753 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.39244402 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |