For all volunteers the AAMM technique significantly (p < 0.01) outperformed the other two methods in all of the intervals as can be seen by comparing to the error curves shown in Fig. 8 and the figures in Table 1 in the supplementary materials. Significance was assessed using a 1-tailed Wilcoxon signed rank test since the error distributions were generally not symmetric. The estimation errors for AAMM and its non-adaptive counterpart, AAMM (no adapt.), were similar in the beginning of the application phase, but as anticipated, as the application phase went on, the AAMM technique continually improved its accuracy by incorporating more and more data into the model. On average the motion estimation of AAMM improved by 22.94% in T5 with respect to its non-adaptive counterpart. However, the method has already significantly adapted to the breathing pattern in T2, i.e. after between 3 and 7 min of imaging, where motion estimations where on average 16.87% more accurate than at the beginning of the adaptation phase. By visually inspecting the curves for AAMM in Fig. 8 it can be seen that for many volunteers (in particular volunteers A, D, E, and F) the error curves start to flatten approximately around the 7 min mark. From this it can be concluded that a longer calibration scan of around 12 min would be optimal, that is the 5 min that were used for calibration in this experiment plus 7 min worth of data added during the application phase. Note that this time could be significantly reduced if a non-cardiac-gated sequence was used.
