Table 1. .

Common technical challenges for QIB deployment in the clinical setting

Suboptimal acquisition protocols in the clinical setting	Routine clinical MRI protocols may be less sophisticated than those specified for research studies, e.g. in many centres clinical T 1 weighted scans may be performed with two-dimensional acquisitions. Isotropic 3D data, which is more suitable for quantitative analysis, may not be available in routine clinical practice. Inconsistencies in scanning parameters can also cause significant variation in tissue contrast, making, for instance, automated GM/WM delineation for a subregion of interest challenging.
Interscanner variability	Image geometric accuracy varies between scanners and vendors, resulting in varying spatial distortions which, if uncorrected, may impact upon regional tissue-volume estimates. Quantification accuracy is predicated on high reproducibility between MRI instruments; this however is not generally a primary design concern in clinical systems, since this rarely affects routine clinical practice based on radiologists’ qualitative visual evaluation.
Image artefacts	Robust screening of incoming data to detect artefacts, such as those arising from patient motion and other errors must be established, as these may impede the automated algorithm in performing accurate quantification. Adaptive correction schemes prior to analysis, such as bias field or motion artefact correction, may minimize the number of data sets failing to yield reliable volume estimates for a given measurement strategy. Many software packages are automated, meaning they will produce a numerical result whatever the input data and often do not allow intermediate (e.g. segmentation) steps to be scrutinized.
Need for full automation	To move away from time-consuming manual or semi-automated techniques requiring frequent intervention and monitoring, often by highly expert practitioners, methods for clinical application must be fully automated. This also protects the process from inter operator variability. Such automated techniques must be generalizable across the range of MRI services in the health system, including both scanner type and acquisition protocol variations.

3D, three-dimensional.