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Images of the mean raw dMRI signal for all five subjects (columns), for each b -value (rows). These constitute the input for the <t>optimisation</t> algorithm. Note that the images are windowed independently for each b -value to allow visualisation of the contrast in the high b -value images
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Images of the mean raw dMRI signal for all five subjects (columns), for each b -value (rows). These constitute the input for the <t>optimisation</t> algorithm. Note that the images are windowed independently for each b -value to allow visualisation of the contrast in the high b -value images
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Image Search Results


Images of the mean raw dMRI signal for all five subjects (columns), for each b -value (rows). These constitute the input for the optimisation algorithm. Note that the images are windowed independently for each b -value to allow visualisation of the contrast in the high b -value images

Journal: NMR in biomedicine

Article Title: A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

doi: 10.1002/nbm.4348

Figure Lengend Snippet: Images of the mean raw dMRI signal for all five subjects (columns), for each b -value (rows). These constitute the input for the optimisation algorithm. Note that the images are windowed independently for each b -value to allow visualisation of the contrast in the high b -value images

Article Snippet: Given a fixed desired number of shells, the optimal b -values for each shell are determined using a simple nonlinear optimisation approach (the Nelder–Mead simplex algorithm, as implemented in Matlab’s fminsearch routine).

Techniques: