Yinghui Wang, Lu Wang, Yidan Feng, Zhi Chen, Jing Qin, Tian Li, Jing Cai
{"title":"通过呼吸同步框架协同重建的高时空分辨率腹部4D-MRI","authors":"Yinghui Wang, Lu Wang, Yidan Feng, Zhi Chen, Jing Qin, Tian Li, Jing Cai","doi":"10.1002/mp.18101","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>This study aims to develop a technique that leverages information across multiple frames to mitigate spatial undersampling, thereby enabling superior spatiotemporal resolution in abdominal 4D-MRI.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We introduce a novel reconstruction approach for 4D-MRI that leverages respiratory-synchronized frames to reconstruct target frames with enhanced image quality. Specifically, we introduce a multi-frame collaborative reconstruction network (MCR-Net) that capitalizes on inter-frame correlations and complementary information for faithful reconstruction. MCR-Net integrates two key mechanisms: the Inter-frame mutual-attention mechanism (IMM) and the structure-aware consolidation module (SaCM). IMM enhances feature extraction by exploiting correlations among neighboring respiratory-synchronized frames, thereby reinforcing shared anatomical features while suppressing random artifacts and noise. SaCM consolidates structural information across frames by leveraging context-aware residual learning, enhancing high-frequency details, and filtering irrelevant data during multi-frame fusion, thus significantly improving the clarity and anatomical integrity.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Experimental evaluations on clinical patient datasets (training: <i>n</i> = 20; validation: <i>n</i> = 6) demonstrate that our method significantly outperforms nine state-of-the-art reconstruction approaches in both visual quality and quantitative accuracy. MCR-Net achieves superior performance in MAE, SSIM, and PSNR, outperforming the next-best methods by 3.77%, 1.03%, and 6.74%, respectively. Furthermore, our experiments validate that MCR-Net enhances registration accuracy compared to original low-quality 4D-MRI by 10.66%, 3.60%, and 1.94% in MAE, SSIM, and NCC metrics. Additionally, simulations demonstrate that MCR-Net effectively maintains high image quality even under significantly increased undersampling ratios.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Our findings demonstrate that MCR-Net effectively suppresses artifacts and recovers missing anatomical structures from undersampled acquisitions, underscoring its potential to enhance 4D-MRI's spatiotemporal resolution and advance clinical applications in abdominal radiotherapy.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 9","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High spatiotemporal-resolution abdominal 4D-MRI through respiratory-synchronized frame collaborative reconstruction\",\"authors\":\"Yinghui Wang, Lu Wang, Yidan Feng, Zhi Chen, Jing Qin, Tian Li, Jing Cai\",\"doi\":\"10.1002/mp.18101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>This study aims to develop a technique that leverages information across multiple frames to mitigate spatial undersampling, thereby enabling superior spatiotemporal resolution in abdominal 4D-MRI.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We introduce a novel reconstruction approach for 4D-MRI that leverages respiratory-synchronized frames to reconstruct target frames with enhanced image quality. Specifically, we introduce a multi-frame collaborative reconstruction network (MCR-Net) that capitalizes on inter-frame correlations and complementary information for faithful reconstruction. MCR-Net integrates two key mechanisms: the Inter-frame mutual-attention mechanism (IMM) and the structure-aware consolidation module (SaCM). IMM enhances feature extraction by exploiting correlations among neighboring respiratory-synchronized frames, thereby reinforcing shared anatomical features while suppressing random artifacts and noise. SaCM consolidates structural information across frames by leveraging context-aware residual learning, enhancing high-frequency details, and filtering irrelevant data during multi-frame fusion, thus significantly improving the clarity and anatomical integrity.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Experimental evaluations on clinical patient datasets (training: <i>n</i> = 20; validation: <i>n</i> = 6) demonstrate that our method significantly outperforms nine state-of-the-art reconstruction approaches in both visual quality and quantitative accuracy. MCR-Net achieves superior performance in MAE, SSIM, and PSNR, outperforming the next-best methods by 3.77%, 1.03%, and 6.74%, respectively. Furthermore, our experiments validate that MCR-Net enhances registration accuracy compared to original low-quality 4D-MRI by 10.66%, 3.60%, and 1.94% in MAE, SSIM, and NCC metrics. Additionally, simulations demonstrate that MCR-Net effectively maintains high image quality even under significantly increased undersampling ratios.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Our findings demonstrate that MCR-Net effectively suppresses artifacts and recovers missing anatomical structures from undersampled acquisitions, underscoring its potential to enhance 4D-MRI's spatiotemporal resolution and advance clinical applications in abdominal radiotherapy.</p>\\n </section>\\n </div>\",\"PeriodicalId\":18384,\"journal\":{\"name\":\"Medical physics\",\"volume\":\"52 9\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18101\",\"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://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.18101","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}
High spatiotemporal-resolution abdominal 4D-MRI through respiratory-synchronized frame collaborative reconstruction
Background
Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.
Purpose
This study aims to develop a technique that leverages information across multiple frames to mitigate spatial undersampling, thereby enabling superior spatiotemporal resolution in abdominal 4D-MRI.
Methods
We introduce a novel reconstruction approach for 4D-MRI that leverages respiratory-synchronized frames to reconstruct target frames with enhanced image quality. Specifically, we introduce a multi-frame collaborative reconstruction network (MCR-Net) that capitalizes on inter-frame correlations and complementary information for faithful reconstruction. MCR-Net integrates two key mechanisms: the Inter-frame mutual-attention mechanism (IMM) and the structure-aware consolidation module (SaCM). IMM enhances feature extraction by exploiting correlations among neighboring respiratory-synchronized frames, thereby reinforcing shared anatomical features while suppressing random artifacts and noise. SaCM consolidates structural information across frames by leveraging context-aware residual learning, enhancing high-frequency details, and filtering irrelevant data during multi-frame fusion, thus significantly improving the clarity and anatomical integrity.
Results
Experimental evaluations on clinical patient datasets (training: n = 20; validation: n = 6) demonstrate that our method significantly outperforms nine state-of-the-art reconstruction approaches in both visual quality and quantitative accuracy. MCR-Net achieves superior performance in MAE, SSIM, and PSNR, outperforming the next-best methods by 3.77%, 1.03%, and 6.74%, respectively. Furthermore, our experiments validate that MCR-Net enhances registration accuracy compared to original low-quality 4D-MRI by 10.66%, 3.60%, and 1.94% in MAE, SSIM, and NCC metrics. Additionally, simulations demonstrate that MCR-Net effectively maintains high image quality even under significantly increased undersampling ratios.
Conclusion
Our findings demonstrate that MCR-Net effectively suppresses artifacts and recovers missing anatomical structures from undersampled acquisitions, underscoring its potential to enhance 4D-MRI's spatiotemporal resolution and advance clinical applications in abdominal radiotherapy.
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.