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A preliminary section is dedicated to a short review of the effect of positron range on spatial resolution and methods to correct for this effect.
B1 mapping (TE = 37.06 and 55.59 ms; TR = 500 ms; FA = 230:−10:130°; 4 mm isotropic resolution) was acquired to correct the T1 maps for inhomogeneities in the transmit radiofrequency field (Lutti et al. 2010).
Some of these methods, specifically designed for human brain studies, use high-resolution anatomical images to correct for partial volume effects [ 9, 11, 12].
Also consider that getting the iPhone OS on to the Apple TV will require some work as developers will yet again (just as with the iPad) have to work on scaling apps to correct resolutions.
Here, the algorithm is used to correct for resolution degradation in these images.
Through implementation of ResMod to correct for resolution limits by optimising each of the five communities further without considering the inter-module links, the two smaller groups within each module become apparent and the total number of modules is correctly identified as ten.
Can we expect increasingly accurate resolution to a correct model given increasingly accurate data?
The application of iMod to detect modules and ResMod to correct for potential resolution limitations is illustrated in this section through a number of real network examples.
The results stabilize at around 5 cm resolution to reach around 95% correct classification.
The algorithm is based on regression modelling that uses sufficiently small windows (5 bp default) to retain high resolution whilst correcting for one or multiple experimental control measurements simultaneously.
Adaptive optics combined with OCT or fundus photography allow for high-resolution images due to the improvement in lateral resolution by correcting for aberrations in the eye.
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