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The second category comprises exams that are performed in a clinical context and in a real world situation. This means, for example, performing exams in open MR scanners versus a dedicated 1.5 T scanner. When images are taken in open MRI scanners, it is assumed that the MR scanner is turned off, the patient is at rest, and so on. This variation can be modelled at the MR system level with knowledge about scanner functioning conditions. All these factors introduce different degrees of inaccuracy in these exams. The goal of this study is to assess the impact of these inaccuracies on the PSIR exam results.

An accurate evaluation of the quality of the performed exams is key to further development of the online examination system. Currently, a system for quality assessment exists, which individually grades MR images based on a set of predefined image criteria [9]. This section shows that the implemented online examination system is able to provide a valuable, up-to-date source of quality information. Furthermore, the implementation of a quality assessment function for the online examination system starts to develop a knowledge base. This knowledge base will contain all the information required to be able to assess the quality of the image and the exam at the individual MR system level. The questions to be answered by the system include, for example, how well were the images acquired? How were, for example, respiratory motion and biological trunk motion corrected? Which examinations could be used to perform the examination? Principally, the implementation of this feature could be helpful in clinical practice for two different purposes: firstly, to highlight the influence of the MR scanner type on quality, and secondly, to identify the examinations that should be repeated, thereby saving manpower. The section ends with an example of a PSIR exam and the implemented MR scanner type assessment.

https://datastudio.google.com/reporting/624c82ff-3319-4d3d-b738-c9ec40a23b5b/page/p_rn70zb5r1c
https://datastudio.google.com/reporting/acf6d438-2dd3-4fbb-8b7e-cbd4a921b0cd/page/p_if6fxb5r1c
https://datastudio.google.com/reporting/a369e84a-9959-4f03-903d-3195381ffd21/page/p_uff0ib5r1c
https://datastudio.google.com/reporting/d40559b1-2d1c-4c25-8c14-4a5e643f0da1/page/p_6xy18a5r1c
https://datastudio.google.com/reporting/0aa2a1bb-d0af-4500-a73c-e157812d1da5/page/p_3kkrka5r1c
https://datastudio.google.com/reporting/d1d3178f-59e1-40aa-ab0f-a976734e7baf/page/p_5k9kva5r1c
https://datastudio.google.com/reporting/8e0f10ce-3b9c-4b3f-9187-fbf83872eee9/page/p_9mzvja5r1c
https://datastudio.google.com/reporting/4384a814-0999-4b26-acfb-fa99bb81b806/page/p_llouka5r1c
https://datastudio.google.com/reporting/173a100f-6e6b-40f4-ae5c-555ae836d03a/page/p_xfkm694r1c
https://datastudio.google.com/reporting/8155231b-39b7-4d89-889f-8a5eec402902/page/p_fmowe94r1c
https://datastudio.google.com/reporting/51b88170-aab9-4a80-b075-91faf4450f66/page/p_tam5c94r1c

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