Any of these biases can, of course, be removed by independently processing the inputs at the cost of increased variability. As soon as longitudinal information is incorporated as “prior knowledge”, bias is introduced, for example due to temporal smoothing, and accuracy may suffer particularly when measuring large longitudinal change. Therefore, it is theoretically possible that changes of greater magnitude are underestimated by initializing each time point with common information from the template as done in FreeSurfer. While a more conservative estimate of change is often preferable in a power analysis, than an overestimation, we aim for accurate and unbiased results. The longitudinal stream in FreeSurfer therefore allows for more flexibility by using a probabilistic voting scheme of independently processed label maps from all time points, to determine the probability of a specific voxel having a specific label by weighting labels across time according to their intensity similarity. This fused segmentation is usually very similar to the time point’s independent segmentation (slightly temporally smoothed). It is not the final solution, but then used to initialize the segmentation algorithm for each time point instead of the fixed segmentation of the subject template to allow for larger departures from the subject average, evident, for example, in several years of neurodegeneration.
Also note that, as soon as longitudinal data is employed, one needs to delay processing until all time points are available to remain unbiased. This is often not feasible and it is of course possible to add time points later and process them with the “old” template, created from the initial subset of time points. While this is clearly introducing bias, it is unclear how large the effect will be and likely depends strongly on the specific situation (e.g., how many time points were used for the template creation and how many were added, etc.). Reprocessing everything with a new template can change earlier results as the new template might be shifted towards a more diseased state. However, we believe it is favorable to have the template somewhere in the middle of the time series than closer towards the front, in order not to be biased towards a more healthy state.
The above discussion highlights several challenges of longitudinal image processing and underlines the importance of selecting methods carefully to avoid introducing a bias by treating individual inputs differently, which can be easily prevented, or by biasing towards no change when encouraging reliability too intensely. For the second case a good trade-off needs to be aimed for.