However, this outcome does not affect the results of the correction model when using machine learning methods to perform the correction (i.e., the correct physics is necessary rather than the accuracy of the descriptors).
Nevertheless, as the comparison of Figure 2A,B suggests, performing the correction with some roughly correct value for V ̄ r will always decrease the bias between K s and K m.
After performing the correction procedure, the virtual measuring point is converted into the corrected virtual measuring point, whose energy measurement is used for the final billing procedure.
Performing the correction through the characteristic curve we achieve the result in Figure 5.
The energy corresponding to the peak of the spectrum has the strongest effect on the attenuation coefficients: we use this energy to perform the correction.
In this approach, the general regression neural network machine learning method is used to perform the correction for DFT methods on the basis of DFT calculations.
Koslowsky et al. have described an external fixator as fixation, a technique that provides the ability to do a percutaneous osteotomy and perform the correction through a three-dimensional correction of the ulnar axis [16].
To perform the correction, the values of the count rates observed in the experiment (or patient scan) should be used, while the values of the true count rates can be calculated using Eq. 1 and the corresponding τ values.
Whether K s ( c ) does offer any advantages over K s in terms of clinical relevance remains to be seen, but the improved correlation with K m seems justification enough to perform the correction.
