Figure 7 shows an image of the evaluation module.
LIRA uses Markov chain Monte Carlo to simultaneously fit an image and the necessary smoothing parameters, allowing for quantification of the standard error of the fitted image and evaluation of the goodness-of-fit of a proposed model.
On MRI, we used fat-saturated T1-weighted imaging for assessment of LN status, and we often used precontrastT2-weighted images for evaluation of extranodal fat infiltration.
Input images for evaluation of plane position estimation.
Figure 7 Input images for evaluation of plane position estimation.
Objective evaluation uses objective evaluation parameters directly after the fusion of the image of the objective evaluation and analysis.
Then, calibration data is used to generate virtual images of the evaluation scene for each camera.
The key techniques used here will be described in detail according to the processing steps including (A) the fitting of parametric image, (B) segmentation of first image, (C) evaluation of phantom and (D) fat extraction.
Images of each landscape without WTs also added, making a total of 39 images for evaluation.
No joint effect of influencing factors (number of images and type of evaluation) was found.
