FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis | Paper Citation Record

Observation bcfb943a-719c-4245-9c7f-ea0f4123e66c · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats),.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis The multimodal brain tumor image segmentation benchmark (brats),

Reference 1

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This paper cites Joint segmentation of anatomical and functional images: Applications in quantification of lesions from pet, pet-ct, mri- pet, and mri-pet-ct images,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Joint segmentation of anatomical and functional images: Applications in quantification of lesions from pet, pet-ct, mri- pet, and mri-pet-ct images,

Reference 2

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Observation 54386531-a751-439a-9515-4a8111a995b4 · outbound

This paper cites Edge-aware multi-task network for integrating quantification segmentation and uncertainty prediction of liver tumor on multi-modality non-contrast mri,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Edge-aware multi-task network for integrating quantification segmentation and uncertainty prediction of liver tumor on multi-modality non-contrast mri,

Reference 3

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This paper cites Optimal acquisition sequence for ai-assisted brain tumor segmentation under the constraint of largest information gain per additional mri sequence,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Optimal acquisition sequence for ai-assisted brain tumor segmentation under the constraint of largest information gain per additional mri sequence,

Reference 4

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This paper cites Multi- modal mr synthesis via modality-invariant latent representation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Multi- modal mr synthesis via modality-invariant latent representation,

Reference 5

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This paper cites Pimms: permutation invariant multi-modal segmentation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Pimms: permutation invariant multi-modal segmentation,

Reference 6

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This paper cites an unresolved cited work.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Unresolved cited work

Reference 7

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Observation 45f7a1b8-b27c-4e37-bb60-f7533d6ba06f · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Image-to-image translation with conditional adversarial networks,

Reference 8

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This paper cites Tripartite-gan: Synthesizing liver contrast-enhanced mri to improve tumor detection,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Tripartite-gan: Synthesizing liver contrast-enhanced mri to improve tumor detection,

Reference 9

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This paper cites Coca-gan: common-feature-learning-based context-aware generative adversarial network for glioma grading,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Coca-gan: common-feature-learning-based context-aware generative adversarial network for glioma grading,

Reference 10

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This paper cites Ea-gans: edge-aware generative adversarial networks for cross-modality mr image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Ea-gans: edge-aware generative adversarial networks for cross-modality mr image synthesis,

Reference 11

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This paper cites Unified generative adversarial networks for multimodal segmentation from unpaired 3d medical images,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Unified generative adversarial networks for multimodal segmentation from unpaired 3d medical images,

Reference 12

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Observation e827b7b5-e8ad-4a70-a841-0783911973c1 · outbound

This paper cites Auto-gan: self- supervised collaborative learning for medical image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Auto-gan: self- supervised collaborative learning for medical image synthesis,

Reference 13

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This paper cites Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation

Reference 14

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Observation ffa14711-eb74-4819-8fd9-ff2d934c8e02 · outbound

This paper cites mustgan: multi-stream generative adversarial networks for mr image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis mustgan: multi-stream generative adversarial networks for mr image synthesis,

Reference 15

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This paper cites Disentangled representation learning for controllable person image generation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Disentangled representation learning for controllable person image generation,

Reference 16

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This paper cites Missing mri pulse sequence synthesis using multi-modal generative adversarial network,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Missing mri pulse sequence synthesis using multi-modal generative adversarial network,

Reference 17

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This paper cites Resvit: residual vision transformers for multimodal medical image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Resvit: residual vision transformers for multimodal medical image synthesis,

Reference 18

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This paper cites Unified Multi-Modal Image Synthesis for Missing Modality Imputation.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Unified Multi-Modal Image Synthesis for Missing Modality Imputation

Reference 19

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This paper cites Seeing what a gan cannot generate,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Seeing what a gan cannot generate,

Reference 20

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Observation fbbf9583-12ad-40e8-8daa-bd9f6422419a · outbound

This paper cites Diffusion models beat gans on image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Diffusion models beat gans on image synthesis,

Reference 21

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This paper cites Denoising diffusion probabilistic models,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Denoising diffusion probabilistic models,

Reference 22

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This paper cites Center-to-edge denoising diffusion probabilistic mod- els with cross-domain attention for undersampled mri reconstruction,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Center-to-edge denoising diffusion probabilistic mod- els with cross-domain attention for undersampled mri reconstruction,

Reference 23

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This paper cites Srdiff: Single image super-resolution with diffusion probabilistic mod- els,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Srdiff: Single image super-resolution with diffusion probabilistic mod- els,

Reference 24

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This paper cites Implicit diffusion models for continuous super-resolution,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Implicit diffusion models for continuous super-resolution,

Reference 25

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This paper cites LFSRDiff: Light Field Image Super-Resolution via Diffusion Models.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis LFSRDiff: Light Field Image Super-Resolution via Diffusion Models

Reference 26

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This paper cites Ambiguous medical image segmentation using diffusion models,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Ambiguous medical image segmentation using diffusion models,

Reference 27

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This paper cites Multi-Layer Dense Attention Decoder for Polyp Segmentation.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Multi-Layer Dense Attention Decoder for Polyp Segmentation

Reference 28

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This paper cites United adversarial learning for liver tumor segmenta- tion and detection of multi-modality non-contrast mri,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis United adversarial learning for liver tumor segmenta- tion and detection of multi-modality non-contrast mri,

Reference 29

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Observation 6398492b-9716-4f8d-bca1-850e6e5593cb · outbound

This paper cites Predicting mitral valve mteer surgery outcomes using machine learning and deep learning techniques,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Predicting mitral valve mteer surgery outcomes using machine learning and deep learning techniques,

Reference 30

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This paper cites Diffusion models for implicit image segmentation ensembles,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Diffusion models for implicit image segmentation ensembles,

Reference 31

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This paper cites Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepato- cellular carcinoma via integrating multi-modality mri,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepato- cellular carcinoma via integrating multi-modality mri,

Reference 32

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This paper cites Cola-diff: Conditional latent diffusion model for multi-modal mri synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Cola-diff: Conditional latent diffusion model for multi-modal mri synthesis,

Reference 33

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Observation 7c2016fb-ca2b-4e76-8c5e-78d5f93d0aac · outbound

This paper cites Diffusion models in vision: A survey,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Diffusion models in vision: A survey,

Reference 34

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This paper cites Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models

Reference 35

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This paper cites Fast patch-based pseudo-ct synthesis from t1-weighted mr images for pet/mr attenuation correction in brain studies,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Fast patch-based pseudo-ct synthesis from t1-weighted mr images for pet/mr attenuation correction in brain studies,

Reference 36

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Observation b3ec4d43-1a3e-4711-a737-96b850a14f9b · outbound

This paper cites Patch based synthesis of whole head mr images: Application to epi distortion correction,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Patch based synthesis of whole head mr images: Application to epi distortion correction,

Reference 37

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Observation a70e889d-a2da-4ebb-a368-3a57984d0b91 · outbound

This paper cites Magnetic resonance image example-based contrast synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Magnetic resonance image example-based contrast synthesis,

Reference 38

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This paper cites Pseudo- healthy image synthesis for white matter lesion segmentation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Pseudo- healthy image synthesis for white matter lesion segmentation,

Reference 39

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This paper cites Random forest regression for magnetic resonance image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Random forest regression for magnetic resonance image synthesis,

Reference 40

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Observation d1868213-0f40-40e6-aa1f-b4b0b3e1d639 · outbound

This paper cites Mr image synthesis by contrast learning on neighborhood en- sembles,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Mr image synthesis by contrast learning on neighborhood en- sembles,

Reference 41

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Observation 99c2db50-24eb-4a9d-a4c8-92289fa0204d · outbound

This paper cites Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization,

Reference 42

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This paper cites Cross-domain synthesis of medical images using efficient location-sensitive deep network,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Cross-domain synthesis of medical images using efficient location-sensitive deep network,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.959078Z

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Observation c7c53585-4eb8-4713-aa57-a263499db2d0 · outbound

This paper cites Whole image synthesis using a deep encoder-decoder network,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Whole image synthesis using a deep encoder-decoder network,

Reference 44

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Observation 3d7f13f5-cf35-41e5-9815-380d80e56542 · outbound

This paper cites Deep learning based imaging data completion for improved brain disease diagnosis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Deep learning based imaging data completion for improved brain disease diagnosis,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.927699Z

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Observation 2ae38ae3-a4e9-4e0f-bfc0-d0427ff6c06e · outbound

This paper cites Multi-modality mr image synthesis via confidence-guided aggregation and cross-modality refine- ment,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Multi-modality mr image synthesis via confidence-guided aggregation and cross-modality refine- ment,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.910805Z

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Observation c17db849-2e1f-4db1-a441-bebc7f91c759 · outbound

This paper cites Semi-supervised learning of mri synthesis without fully- sampled ground truths,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Semi-supervised learning of mri synthesis without fully- sampled ground truths,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.894558Z

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Observation d20106b7-eef2-44f2-a286-f787284f0875 · outbound

This paper cites Random forest flair reconstruction from t 1, t 2, and p d-weighted mri,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Random forest flair reconstruction from t 1, t 2, and p d-weighted mri,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.878478Z

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This paper cites Rs-net: Regression-segmentation 3d cnn for synthesis of full resolution missing brain mri in the presence of tumours,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Rs-net: Regression-segmentation 3d cnn for synthesis of full resolution missing brain mri in the presence of tumours,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.862291Z

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Observation 3ec32ab8-ebe3-4e84-893c-912c0e5de2ad · outbound

This paper cites Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Robust multimodal brain tumor segmentation via feature disentanglement and gated fusion,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.846759Z

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Observation d9cbec0a-d521-413e-b8bb-db8a4035e771 · outbound

This paper cites Hi-net: hybrid-fusion network for multi-modal mr image synthesis,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Hi-net: hybrid-fusion network for multi-modal mr image synthesis,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.830138Z

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Observation 1c30bdbf-4cb7-489d-97ce-e43d92c8bf19 · outbound

This paper cites Collagan: Collaborative gan for missing image data imputation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Collagan: Collaborative gan for missing image data imputation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.814334Z

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This paper cites Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.798935Z

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Observation 324f02fc-e0af-4fca-8112-e5cd4b221a1a · outbound

This paper cites A domain gap aware generative adversarial network for multi-domain image translation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis A domain gap aware generative adversarial network for multi-domain image translation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.782352Z

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This paper cites Multi-modal mri image synthesis via gan with multi-scale gate mergence,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Multi-modal mri image synthesis via gan with multi-scale gate mergence,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.767067Z

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This paper cites Progressively volumetrized deep generative models for data-efficient contextual learning of mr image recovery,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Progressively volumetrized deep generative models for data-efficient contextual learning of mr image recovery,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.750661Z

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Observation 075953ff-d29c-4bb7-9e77-7426e8c78d2d · outbound

This paper cites Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Rfnet: Region-aware fusion network for incomplete multi-modal brain tumor segmentation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.733415Z

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This paper cites Conditional Generative Adversarial Nets.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Conditional Generative Adversarial Nets

Reference 58

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Observation f1a38c8b-b671-4436-84fa-e5e34d31fdcd · outbound

This paper cites Multi-modal modality- masked diffusion network for brain mri synthesis with random modality missing,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Multi-modal modality- masked diffusion network for brain mri synthesis with random modality missing,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.717201Z

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Observation 9463a758-a777-467b-9fb3-c4ff05bf364e · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis U-net: Convolutional networks for biomedical image segmentation,

Reference 60

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FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Unresolved cited work

Reference 61

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This paper cites Gauss and the history of the fast fourier transform,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Gauss and the history of the fast fourier transform,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.682279Z

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Observation 787cc7c5-09a2-43d8-b457-07428fce77d2 · outbound

This paper cites Reconstruction of multispatial, multispectral im- age data using spatial frequency content,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Reconstruction of multispatial, multispectral im- age data using spatial frequency content,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.666353Z

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Observation bd988f12-ffaf-4e06-9317-2f650f86d3d7 · outbound

This paper cites Curriculum learning,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Curriculum learning,

Reference 64

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This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 65

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This paper cites Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,

Reference 66

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Observation f59dce6f-830f-4e62-af4f-2bb6845e3716 · outbound

This paper cites Information extraction from images.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Information extraction from images

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.630747Z

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Observation b6858090-8a10-421d-9df5-3c38035d5168 · outbound

This paper cites A global optimisation method for robust affine registration of brain images,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis A global optimisation method for robust affine registration of brain images,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:59:52.613521Z

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Observation 1e13583a-b06a-4bbe-9792-019fc6b954d3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Adam: A Method for Stochastic Optimization

Reference 69

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This paper cites Image quality assessment: from error visibility to structural similarity,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Image quality assessment: from error visibility to structural similarity,

Reference 70

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Observation 121cce21-48dc-4495-aa4b-02de6c1b33d6 · outbound

This paper cites Image synthesis in multi-contrast mri with conditional generative adversarial networks,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Image synthesis in multi-contrast mri with conditional generative adversarial networks,

Reference 71

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Observation 34d9c220-5c0a-45ff-8750-8d666746e3e6 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis High- resolution image synthesis with latent diffusion models,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T21:59:52.359276Z

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Observation 6276ad25-1366-4c9b-b9bc-f7cb313d81e2 · outbound

This paper cites Denoising Diffusion Implicit Models.

FgC2F-UDiff: Frequency-guided and Coarse-to-fine Unified Diffusion Model for Multi-modality Missing MRI Synthesis Denoising Diffusion Implicit Models

Reference 73

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