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Publication Detail
Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge
  • Publication Type:
  • Authors:
    Pizzolato M, Palombo M, Bonet-Carne E, Tax CMW, Grussu F, Ianus A, Bogusz F, Pieciak T, Ning L, Larochelle H, Descoteaux M, Chamberland M, Blumberg SB, Mertzanidou T, Alexander DC, Afzali M, Aja-Fernández S, Jones DK, Westin CF, Rathi Y, Baete SH, Cordero-Grande L, Ladner T, Slator PJ, Hajnal JV, Thiran JP, Price AN, Sepehrband F, Zhang F, Hutter J
  • Publication date:
  • Pagination:
    195, 208
  • Status:
  • Book title:
    Mathematics and Visualization
In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion-summarized by the b-value-constitutes yet another explorable “dimension” to modify the image contrast, which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it.
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