Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems Article Swipe
Zirong Li
,
Yanyang Wang
,
Jianjia Zhang
,
Weiwen Wu
,
Hengyong Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.08511
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.08511
Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models have proven to be effective in addressing different inverse problems encountered in CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models primarily focus on reconstructing two dimensional (2D) data distribution, leading to inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, which reduces the complexity of training compared to directly working on 3D volumes. However, in the reconstruction phase, the TOSM updates the data distribution in 3D space, utilizing complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method demonstrates remarkable advancements and attains state-of-the-art results in solving 3D ill-posed inverse problems. Notably, the proposed TOSM effectively addresses the inter-slice inconsistency issue, resulting in high-quality 3D volumetric reconstruction.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.08511
- https://arxiv.org/pdf/2308.08511
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4385966059Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.08511Digital Object Identifier
- Title
-
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse ProblemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-08-16Full publication date if available
- Authors
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Zirong Li, Yanyang Wang, Jianjia Zhang, Weiwen Wu, Hengyong YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.08511Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2308.08511Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2308.08511Direct OA link when available
- Concepts
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Computer science, Inverse problem, Iterative reconstruction, Artificial intelligence, Field (mathematics), Reliability (semiconductor), Algorithm, Computer vision, Mathematics, Physics, Mathematical analysis, Quantum mechanics, Power (physics), Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effectively | 193 |
| abstract_inverted_index.encountered | 29 |
| abstract_inverted_index.inter-slice | 196 |
| abstract_inverted_index.large-scale | 167 |
| abstract_inverted_index.limitation, | 83 |
| abstract_inverted_index.principles, | 157 |
| abstract_inverted_index.reliability | 160 |
| abstract_inverted_index.score-based | 57, 90 |
| abstract_inverted_index.sparse-view | 36, 168 |
| abstract_inverted_index.theoretical | 156 |
| abstract_inverted_index.transaxial) | 141 |
| abstract_inverted_index.advancements | 178 |
| abstract_inverted_index.demonstrates | 176 |
| abstract_inverted_index.distribution | 128 |
| abstract_inverted_index.high-quality | 201 |
| abstract_inverted_index.technologies | 10 |
| abstract_inverted_index.complementary | 133 |
| abstract_inverted_index.distribution, | 67 |
| abstract_inverted_index.distributions | 101 |
| abstract_inverted_index.inconsistency | 197 |
| abstract_inverted_index.reconstructed | 76 |
| abstract_inverted_index.reconstructing | 62 |
| abstract_inverted_index.reconstruction | 121 |
| abstract_inverted_index.two-and-a-half | 88 |
| abstract_inverted_index.experimentation | 165 |
| abstract_inverted_index.inconsistencies | 70 |
| abstract_inverted_index.reconstruction. | 41, 54, 147, 204 |
| abstract_inverted_index.state-of-the-art | 181 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.28338694 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |