Table 5.
Recommendations for reporting of parameters of the reconstruction and analysis pipelines.
| Item | Notes | Recommended |
|---|---|---|
| Toolbox used | Specify toolbox name and version (or download date), e.g. FANSI, STISuite, MEDI, etc. | mandatory |
| Algorithms used | For each step of the recon pipeline (phase reconstruction, echo combination, masking, phase unwrapping, background field removal, dipole inversion), please specify the algorithm used. Indicate the numerical values of relevant parameters (even if default values were used), e.g. regularization parameters. |
mandatory, at least for non-default algorithms and parameters |
| Further processing | If further processing was necessary to make images compatible with image review environments (such as PACS) used in the study, any data manipulation (including geometrical transformations, interpolation, header data changes, etc.) should be reported |
|
| Referencing | Magnetic susceptibility values should always be reported in either ppm or ppb (parts-per-billion) and the reference region (see the Section 8) should be explicitly stated, even in the case the adopted method did implicit whole brain referencing. When the reference region used in the study is not the whole-brain mask, its [mean ± std] susceptibility value when referenced to the whole-brain mask should be reported, to enable post-hoc re-referencing for meta-analyses. Generally, it should be discussed in the Discussion section how potential pathological changes within the reference region may have biased the study outcome. |
mandatory |
| Data inclusion/exclusion criteria | Details on data inclusion/exclusion criteria should be reported. For example: which artifacts were taken into consideration, and which level of artifact severity was considered as a threshold for inclusion/exclusion. The description of this aspect, which is study-specific, can be supported by images with representative cases in the Supplementary Materials. | Mandatory, in studies where datasets were excluded based on image quality, or when datasets with visible artifacts were deemed acceptable for inclusion |