DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916213288861696 |
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| author | Hu, Yuyang Kothapalli, Satya V. V. N. Gan, Weijie Sukstanskii, Alexander L. Wu, Gregory F. Goyal, Manu Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. |
| author_facet | Hu, Yuyang Kothapalli, Satya V. V. N. Gan, Weijie Sukstanskii, Alexander L. Wu, Gregory F. Goyal, Manu Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. |
| contents | We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery (FLAIR) and Magnetization Prepared-Rapid Gradient Echo (MPRAGE) images, without acquiring corresponding measurements, by leveraging multi-Gradient-Recalled Echo (mGRE) MRI signals as conditional inputs. DiffGEPCI operates in a two-step fashion: it initially estimates a 3D volume slice-by-slice using the axial plane and subsequently applies a refinement algorithm (referred to as 2.5D) to enhance the quality of the coronal and sagittal planes. Experimental validation on real mGRE data shows that DiffGEPCI achieves excellent performance, surpassing generative adversarial networks (GANs) and traditional diffusion models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18073 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model Hu, Yuyang Kothapalli, Satya V. V. N. Gan, Weijie Sukstanskii, Alexander L. Wu, Gregory F. Goyal, Manu Yablonskiy, Dmitriy A. Kamilov, Ulugbek S. Image and Video Processing We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery (FLAIR) and Magnetization Prepared-Rapid Gradient Echo (MPRAGE) images, without acquiring corresponding measurements, by leveraging multi-Gradient-Recalled Echo (mGRE) MRI signals as conditional inputs. DiffGEPCI operates in a two-step fashion: it initially estimates a 3D volume slice-by-slice using the axial plane and subsequently applies a refinement algorithm (referred to as 2.5D) to enhance the quality of the coronal and sagittal planes. Experimental validation on real mGRE data shows that DiffGEPCI achieves excellent performance, surpassing generative adversarial networks (GANs) and traditional diffusion models. |
| title | DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2311.18073 |