Academic Journal
Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design
| العنوان: | Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design |
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| المؤلفون: | Matthias Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf |
| المساهمون: | The Pennsylvania State University CiteSeerX Archives |
| المصدر: | http://eprints.pascal-network.org/archive/00006336/01/seeger.pdf. |
| سنة النشر: | 2010 |
| المجموعة: | CiteSeerX |
| الوصف: | The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standard signal processing primitives, resulting in an efficient optimization algorithm for Cartesian and spiral trajectories. On clinical resolution brain image data from a Siemens 3T scanner, automatically optimized trajectories lead to significantly improved images, compared to standard low-pass, equispaced, or variable density randomized designs. Insights into the nonlinear design optimization problem for MRI are given. |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.8014; http://eprints.pascal-network.org/archive/00006336/01/seeger.pdf |
| الاتاحة: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.331.8014 http://eprints.pascal-network.org/archive/00006336/01/seeger.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: | edsbas.BDFB8A |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |