{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:14:44Z","timestamp":1760238884925,"version":"build-2065373602"},"reference-count":112,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T00:00:00Z","timestamp":1598486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.<\/jats:p>","DOI":"10.3390\/jimaging6090086","type":"journal-article","created":{"date-parts":[[2020,8,27]],"date-time":"2020-08-27T08:05:18Z","timestamp":1598515518000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract"],"prefix":"10.3390","volume":"6","author":[{"given":"Joe","family":"Martin","sequence":"first","affiliation":[{"name":"MR Physics, Guy\u2019s and St Thomas\u2019 NHS Foundation Trust, St Thomas\u2019s Hospital, London SE1 7EH, UK"}]},{"given":"Matthieu","family":"Ruthven","sequence":"additional","affiliation":[{"name":"Clinical Physics, Barts Health NHS Trust, St Bartholomew\u2019s Hospital, London EC1A 7BE, UK"}]},{"given":"Redha","family":"Boubertakh","sequence":"additional","affiliation":[{"name":"Singapore Bioimaging Consortium (SBIC), Singapore 138667, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2209-2076","authenticated-orcid":false,"given":"Marc E.","family":"Miquel","sequence":"additional","affiliation":[{"name":"Clinical Physics, Barts Health NHS Trust, St Bartholomew\u2019s Hospital, London EC1A 7BE, UK"},{"name":"Centre for Advanced Cardiovascular Imaging, NIHR Barts Biomedical Research Centre (BRC), William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,27]]},"reference":[{"key":"ref_1","unstructured":"Fry, D.B. 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