# Interoperability Reference Models for Applications of Artificial Intelligence in Medical Imaging
## 2 Materials and Methods
### Methods for Adapting Medical Imaging
#### 2.2.3 Minimization of Measurement Uncertainty in Medical Imaging  
With medical imaging, there are a variety of measurement equipment and methods such as MRI, CT, X-ray, and others, but as aforementioned in this study, the main focus will be on MRIs as they are the subject of research regarding artificial intelligence. In designing a measurement uncertainty minimization method, an analysis of the elements influencing uncertainty and a survey of the minimization methods used is needed. Based on research by [PERSON] and [PERSON], an analysis of data elements needed to design a medical imaging measurement uncertainty method was conducted. According to research by them, measurement uncertainty elements influencing T1 mapping values are as displayed in Table 2[19]. T1 mapping is a cardiac magnetic resonance (CMR) imaging method that allows for the early detection of cardiovascular fibrosis [20]. Figure 2: Basic Profile De-Identifier Elements Setting within the Clinical Trial Processor (CTP). Uncertainty elements and factors that affect T1 mapping values are protocol parameters, sequence design, scanner adjustments, fit model, tissue characteristics, patient, and others. Factors such as the flip angle and heart rate affect T1 values and may cause uncertainty. This study proposes a method of using standardized phantoms in order to minimize the uncertainty elements that exist within such medical imaging. Environmental measurement factors affect measurement uncertainty, and if these factors are assigned a standardized value, all medical imaging measurement values can be calibrated and quantified according to those standards. As such, a standardized phantom that can provide standardized values is needed. [PERSON] and [PERSON] conducted research on T1 value measurement phantoms that can be used in cardiovascular MRI diagnoses, and the standardized phantoms used in this study were composed in the structure shown in Figure 3 [21]. Phantoms are composed of a gel matrix that surrounds multiple tubes. Each tube is a mixture of agarose and NiCl2, and the T1 value can be created according to the situation or target needed to be measured by T1 values in the blood before and after the insertion of a contrast medium by changing the concentration of NiCl2. As T1 values from standardized phantoms are used as standardized values, it is important to maintain stable and consistent levels. The preservation periods are affected by the storage and usage temperature. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Detail** \\ \hline \multirow{4}{*}{Protocol parameters} & Matrix size \\  & Parallel imaging \\  & Partial Fourier \\  & Flip angle \\  & Echo-spacing (BW and TR) \\  & Raw filter \\ \hline \multirow{4}{*}{Sequence design} & Slice profile \\  & Inversion pulse efficiency and BW \\  & SSFP steady state run-up \\ \hline \multirow{4}{*}{Scanner adjustments} & Shim \\  & Center frequency adjustment \\  & B1 transmit ampl (flip angle) \\  & z-FOV \\ \hline \multirow{4}{*}{Fit model} & 2 vs. 3 parameters \\  & Multi-fit MagIR vs SPIR \\ \hline \multirow{4}{*}{Tissue characteristics} & T2 \\  & MT \\ \cline{1-1}  & Fatty infiltration \\ \cline{1-1}  & Flow \\ \hline \multirow{4}{*}{Patient} & Heart rate \\ \cline{1-1}  & Respiratory motion \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MRI Measurement Uncertainty Elements in Medical Imaging [19]. Figure 3: Phantom Composition for Cardiovascular MRI T1 Mapping Measurement. As using extensions on HL7 FHIR requires data elements that did not exist previously, a process defining each data element is needed. This process is called profiling and each profile on specific resources and extensions are saved in StructureDefinition within the HL7 FHIR server. The profiling module used Forge [23] and the data elements of the phantom are defined in Tables 6 and 7. The Phantom resource must be referenced as it must be used with the pre-existing ImagingStudy resource.