[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123925-en":3,"doc-seo-123925-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123925,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning","Diffusion-relaxation MRI enables quantitative characterisation of microstructural tissue properties such as orientation, size, and shape, but accurate protocols often demand long acquisition times. A physics-informed learning framework is proposed to select an optimal subset of diffusion-relaxation measurements, predict signals for non-measured acquisitions, and estimate quantitative parameters efficiently. Training and validation use in vivo and synthetic 5D diffusion-relaxation MRI from five subjects, with testing on a sixth subject. Physics-informed methods produce more consistent parameter estimates and maintain similar signal prediction error; fivefold shorter protocols yield parameter error distributions with very small effect sizes, supporting shorter imaging without quality loss.","Medical Image Analysis 94 (2024) 103134  \n| Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning\u003Cbr>Álvaro Planchuelo-Gómez a,b, Maxime Descoteaux c, Hugo Larochelle d, Jana Hutter e, Derek K. Jones a, Chantal M.W. Tax f,g,∗\u003Cbr>a Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom b Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain\u003Cbr>c Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada d Google DeepMind, Montréal, QC, Canada\u003Cbr>e Centre for Medical Engineering, Centre for the Developing Brain, King’s College London, London, United Kingdom f Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands\u003Cbr>g Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords: Quantitative MRI Machine learning Brain\u003Cbr>Diffusion-relaxation |  | Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposesa physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters.\u003Cbr>In vivo and synthetic brain 5D-Diffusion-􀁔1-􀁔2∗ -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physicsinformed machine learning methods were implemented and compared to two manual selection procedures and Cramér–Rao lower bound optimisation.\u003Cbr>The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Fivefold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value.\u003Cbr>The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions. |  |\n\n1. Introduction  \nClinical magnetic resonance images (MRI) typically show qualitative tissue contrast, with intensities arbitrarily scaled according to different MR-phenomena. Quantitative MRI, on the other hand, aims to extract reproducible measures more directly related to tissue properties that can be studied longitudinally and/or across populations. Examples of the phenomena that can be studied to assess tissue changes quantitatively in health and disease are diffusion and relaxation (Cercignani and Bouyagoub, 2018).  \nTo quantify tissue properties, distinct MRI experiments are typically performed to probe and subsequently quantify each individual phenomenon by acquiring multiple images in which one or a few experimental MRI parameters are varied. Diffusion MRI (dMRI) sensitises  \nthe MRI signal to the random molecular motion of water by acquiring MR data with externally applied field gradients with different amplitudes and along different orientations. Relaxation MRI measures the return to equilibrium of nuclear spin polarisation after a radiofrequency pulse, and relaxation times can provide valuable insights into tissue composition and pathology. Notwithstanding the ultimate promise of quantitative diffusion and relaxation measures to improve diagnosis, disease mon","cbCaiuKCdjtBBjqf","https://ap.wps.com/l/cbCaiuKCdjtBBjqf","pdf",6096320,1,15,"English","en",105,"# Introduction\n## Quantitative MRI and contrasts\n## Diffusion MRI and relaxation MRI\n## Motivation for joint diffusion-relaxation acquisition\n# Methods and Framework\n## Physics-informed learning for measurement subset selection\n## Signal prediction and quantitative parameter estimation\n## Protocol optimisation and comparison baselines\n# Experimental Setup\n## Data sources: in vivo and synthetic 5D diffusion-relaxation MRI\n## Training, validation, and testing protocol\n# Results and Discussion\n## Accuracy and consistency of parameter estimation\n## Signal prediction error and shorter protocol performance\n## Selected measurement subset characteristics\n# Conclusion","[{\"question\":\"What problem does the document address in diffusion-relaxation MRI acquisition?\",\"answer\":\"Diffusion-relaxation MRI can extract quantitative microstructural properties, but obtaining accurate estimates typically requires long acquisition times, limiting clinical adoption.\"},{\"question\":\"How does the proposed framework reduce scan time?\",\"answer\":\"It uses physics-informed machine learning to identify an optimal subset of diffusion-relaxation measurements, enabling shorter protocols while predicting non-measured signals and estimating quantitative parameters.\"},{\"question\":\"What were the main findings from training, validation, and testing?\",\"answer\":\"Physics-informed approaches produced more consistently accurate parameter estimates in simulations with similar signal prediction error, and fivefold shorter protocols resulted in very small differences in error distributions compared with the full protocol.\"}]","Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning | 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problem does the document address in diffusion-relaxation MRI acquisition?","Question",{"text":76,"@type":77},"Diffusion-relaxation MRI can extract quantitative microstructural properties, but obtaining accurate estimates typically requires long acquisition times, limiting clinical adoption.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed framework reduce scan time?",{"text":81,"@type":77},"It uses physics-informed machine learning to identify an optimal subset of diffusion-relaxation measurements, enabling shorter protocols while predicting non-measured signals and estimating quantitative parameters.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the main findings from training, validation, and testing?",{"text":85,"@type":77},"Physics-informed approaches produced more consistently accurate parameter estimates in simulations with similar signal prediction error, and fivefold shorter protocols resulted in very small differences in error distributions 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