{"title":"脑网络自动生成的时空粗-细扩散模型。","authors":"Qiankun Zuo, Jiaojiao Yu, Conghuan Ye, Ling Chen, Hao Tian, Yixian Wu, Yudong Zhang","doi":"10.1002/mp.17833","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Functional magnetic resonance imaging (fMRI) has emerged as a transformative tool in analyzing and understanding brain diseases. It is a challenge to learn effective features from the high-dimensional fMRI. Most studies have focused on extracting connectivity-based features for disease analysis. However, they heavily rely on the software toolboxes to construct connectivity-based features, which may suffer from large errors because of different manual parameter settings and thus lead to bad performance in brain disorder analysis.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>A novel brain denoiser model is proposed to transform four-dimensional fMRI (4D fMRI) into a brain network in a unified framework for brain disease analysis.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>By introducing anatomical knowledge, the proposed model first reduces the 4D fMRI into a 2D coarse region-of-interest(ROI)-based time series and then diffuses it into noisy status by gradually adding Gaussian noise. Moreover, the coarse-to-fine transformer refinement is designed to capture multi-scale temporal dynamics and iteratively remove unrelated multi-frequency noise. Besides, the low-frequency preservation module is devised to enhance the effective signal at low frequencies during the denoising process. This can improve the signal-to-noise ratio at each timestep, which ensures accurate restoration of ROI time series and improves the performance of brain network construction.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>We evaluate the performance of the Brain Denoiser on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating its ability to effectively suppress noise while preserving the underlying neural signals. Comparative analyses with related competing methods demonstrate the superiority of the proposed model.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Generally, the proposed model presents a robust and innovative solution for brain network generation, paving the way for efficient analysis of brain disease.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 7","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal coarse-to-fine diffusion model for automatic brain network generation\",\"authors\":\"Qiankun Zuo, Jiaojiao Yu, Conghuan Ye, Ling Chen, Hao Tian, Yixian Wu, Yudong Zhang\",\"doi\":\"10.1002/mp.17833\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Functional magnetic resonance imaging (fMRI) has emerged as a transformative tool in analyzing and understanding brain diseases. It is a challenge to learn effective features from the high-dimensional fMRI. Most studies have focused on extracting connectivity-based features for disease analysis. However, they heavily rely on the software toolboxes to construct connectivity-based features, which may suffer from large errors because of different manual parameter settings and thus lead to bad performance in brain disorder analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>A novel brain denoiser model is proposed to transform four-dimensional fMRI (4D fMRI) into a brain network in a unified framework for brain disease analysis.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>By introducing anatomical knowledge, the proposed model first reduces the 4D fMRI into a 2D coarse region-of-interest(ROI)-based time series and then diffuses it into noisy status by gradually adding Gaussian noise. Moreover, the coarse-to-fine transformer refinement is designed to capture multi-scale temporal dynamics and iteratively remove unrelated multi-frequency noise. Besides, the low-frequency preservation module is devised to enhance the effective signal at low frequencies during the denoising process. This can improve the signal-to-noise ratio at each timestep, which ensures accurate restoration of ROI time series and improves the performance of brain network construction.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>We evaluate the performance of the Brain Denoiser on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating its ability to effectively suppress noise while preserving the underlying neural signals. Comparative analyses with related competing methods demonstrate the superiority of the proposed model.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Generally, the proposed model presents a robust and innovative solution for brain network generation, paving the way for efficient analysis of brain disease.</p>\\n </section>\\n </div>\",\"PeriodicalId\":18384,\"journal\":{\"name\":\"Medical physics\",\"volume\":\"52 7\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/mp.17833\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/mp.17833","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Spatiotemporal coarse-to-fine diffusion model for automatic brain network generation
Background
Functional magnetic resonance imaging (fMRI) has emerged as a transformative tool in analyzing and understanding brain diseases. It is a challenge to learn effective features from the high-dimensional fMRI. Most studies have focused on extracting connectivity-based features for disease analysis. However, they heavily rely on the software toolboxes to construct connectivity-based features, which may suffer from large errors because of different manual parameter settings and thus lead to bad performance in brain disorder analysis.
Purpose
A novel brain denoiser model is proposed to transform four-dimensional fMRI (4D fMRI) into a brain network in a unified framework for brain disease analysis.
Methods
By introducing anatomical knowledge, the proposed model first reduces the 4D fMRI into a 2D coarse region-of-interest(ROI)-based time series and then diffuses it into noisy status by gradually adding Gaussian noise. Moreover, the coarse-to-fine transformer refinement is designed to capture multi-scale temporal dynamics and iteratively remove unrelated multi-frequency noise. Besides, the low-frequency preservation module is devised to enhance the effective signal at low frequencies during the denoising process. This can improve the signal-to-noise ratio at each timestep, which ensures accurate restoration of ROI time series and improves the performance of brain network construction.
Results
We evaluate the performance of the Brain Denoiser on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating its ability to effectively suppress noise while preserving the underlying neural signals. Comparative analyses with related competing methods demonstrate the superiority of the proposed model.
Conclusions
Generally, the proposed model presents a robust and innovative solution for brain network generation, paving the way for efficient analysis of brain disease.
期刊介绍:
Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments
Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.