{"id":222,"date":"2016-06-09T08:23:16","date_gmt":"2016-06-09T08:23:16","guid":{"rendered":"https:\/\/mri.sbollmann.net\/?p=222"},"modified":"2020-06-14T08:27:29","modified_gmt":"2020-06-14T08:27:29","slug":"ultrashort-echo-time-imaging-using-pointwise-encoding-time-reduction-with-radial-acquisition-petra-average-7t-model","status":"publish","type":"post","link":"https:\/\/mri.sbollmann.net\/index.php\/2016\/06\/09\/ultrashort-echo-time-imaging-using-pointwise-encoding-time-reduction-with-radial-acquisition-petra-average-7t-model\/","title":{"rendered":"Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA) average 7T model"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1624\" height=\"812\" src=\"https:\/\/i1.wp.com\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16.png?fit=750%2C375&amp;ssl=1\" alt=\"\" class=\"wp-image-223\" srcset=\"https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16.png 1624w, https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16-300x150.png 300w, https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16-1024x512.png 1024w, https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16-768x384.png 768w, https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16-1536x768.png 1536w\" sizes=\"auto, (max-width: 1624px) 100vw, 1624px\" \/><\/figure>\n\n\n\n<p>For the ISMRM 2016 we build a minimum deformation average (MDA) from a population of subjects based upon high resolution 7T MR imaging.<\/p>\n\n\n\n<p>View online and download the model in MINC or NiFTI format here:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a rel=\"noreferrer noopener\" target=\"_blank\" href=\"http:\/\/tissuestack.org\/desktop.html?ds=43&amp;plane=z&amp;x=27.25&amp;y=-11.75&amp;z=33&amp;zoom=7\">View model online<\/a> via tissuestack.org<\/li><li><a rel=\"noreferrer noopener\" href=\"https:\/\/imaging.org.au\/uploads\/Human7T\/petraModel_L11_asym-mincanon_v0.8.mnc\" target=\"_blank\">Download mnc (171 MB)<\/a> <\/li><li><a rel=\"noreferrer noopener\" href=\"https:\/\/imaging.org.au\/uploads\/Human7T\/petraModel_L11_asym-mincanon_v0.8.nii\" target=\"_blank\">Download nii (195 MB)<\/a><\/li><li>code: <a rel=\"noreferrer noopener\" href=\"https:\/\/github.com\/CAISR\/volgenmodel-nipype\" target=\"_blank\">https:\/\/github.com\/CAISR\/volgenmodel-nipype<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Acquisition details<\/h2>\n\n\n\n<p>28 participants (21-34 years of age, 26.5 years on average, 14 males) on a 7 T whole-body research scanner (Siemens Healthcare, Erlangen, Germany), with maximum gradient strength of 70 mT\/m and a slew rate of 200 mT\/m\/s. A 7 T Tx\/32 channel Rx head array (Nova Medical, Wilmington, MA, USA) was used for radio frequency transmission and signal reception. We acquired data using a prototype ultra-short-TE sequence PETRA (Grodzki DM, Jakob PM, Heismann B. Ultrashort echo time imaging using pointwise encoding time reduction with radial acquisition (PETRA). Magn. Reson. Med. 2012;67:510\u2013518. doi: 10.1002\/mrm.23017.): TR = 1.99 ms, TE = 0.07 ms, flip angle = 2\u00b0, FOV = 288x288x288 mm3, matrix = 288x288x288 (1 mm isometric voxels), no GRAPPA, TA = 2 min. The volgenmodel pipeline was used to construct a minimum deformation model.First, the initial model was generated based on one individual dataset blurred using a kernel size of 4 mm to remove individual features. Then all original input images were aligned via a 12 parameter affine transformation and a normalized cross correlation objective function. The original input datasets were then resampled to the model space and transformed using a concatenation of the inverse transformation from model to participant space and the average transform. Finally, the next model stage was computed by using a robust averaging process of the resampled data, including only data within two standard deviations of the existing mean. After the affine transformation, non-linear fitting was used with incrementally decreasing step and smoothing kernel sizes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>For the ISMRM 2016 we build a minimum deformation average (MDA) from a population of subjects based upon high resolution 7T MR imaging. View online and download the model in MINC or NiFTI format here: View model online via tissuestack.org Download mnc (171 MB) Download nii (195 MB) code: https:\/\/github.com\/CAISR\/volgenmodel-nipype [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":223,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21,15],"tags":[],"class_list":["post-222","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-minimum-deformation-averaging","category-ultra-high-field"],"jetpack_featured_media_url":"https:\/\/mri.sbollmann.net\/wp-content\/uploads\/2020\/06\/image-16.png","_links":{"self":[{"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/posts\/222","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/comments?post=222"}],"version-history":[{"count":1,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/posts\/222\/revisions"}],"predecessor-version":[{"id":224,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/posts\/222\/revisions\/224"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/media\/223"}],"wp:attachment":[{"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/media?parent=222"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/categories?post=222"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mri.sbollmann.net\/index.php\/wp-json\/wp\/v2\/tags?post=222"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}