ISO/TR 21900:2018

TECHNICAL ISO/TR
REPORT 21900
First edition
2018-09
Guidance for uncertainty analysis
regarding the application of ISO/TS
Lignes directrices pour l’analyse de l’incertitude concernant
l’application de l’ISO/TS 10974
Reference number
©
ISO 2018
© ISO 2018
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ii © ISO 2018 – All rights reserved

Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Uncertainty background . 1
4.1 General . 1
4.2 Method 1 Evaluation . 2
4.3 Method 2 Evaluation . 3
5 Experiment Uncertainty (u ) . 3
exp
5.1 General . 3
5.2 Measurement tool uncertainty (probe) . 3
5.3 Probe position uncertainty . 3
5.4 Tissue simulating phantom . 4
5.5 RF field source . 4
5.6 Phantom position uncertainty . 4
5.7 AIMD influence . 4
5.8 Overall u consideration . 4
exp
6 AIMD model uncertainty (u ) . 5
Predict
6.1 Piecewise excitation method for deriving AIMD model . 5
6.2 AIMD model uncertainty (u ): Method 1 . 6
Predict
6.3 AIMD model uncertainty (u ): Method 2 .11
Predict
7 Clinical uncertainty (u ) .12
Clinical
8 Uncertainty in conversion to power (u ) .13
power
9 Final uncertainty (combination and application) .13
Bibliography .14
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
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ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
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.org/iso/foreword .html.
This document was prepared by Technical Committee ISO/TC 150, Implants for surgery, Subcommittee
SC 6, Active implants.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www .iso .org/members .html.
iv © ISO 2018 – All rights reserved

Introduction
Clause 8 of ISO/TS 10974:2018 describes methods (Tiers) for analyzing the RF power deposition for
active implantable medical device (AIMD). EM evaluations in a complex near-field exposure scenario
can be difficult and involve many uncertainty sources. Simulations requiring a model of the DUT and
clinical incident field have uncertainties that need to be carefully assessed.
The objective of the uncertainty analysis is to determine the confidence interval of the RF-induced
power deposition with respect to its true value. The acceptable level of uncertainty for an AIMD model
is relative to the safety margin afforded by the AIMD’s RF performance. For instance, if the expected
MRI RF induced AIMD power deposition in vivo is very low, it is less critical to have a highly accurate
model and more uncertainty can be tolerated in the model predictions.
TECHNICAL REPORT ISO/TR 21900:2018(E)
Guidance for uncertainty analysis regarding the
application of ISO/TS 10974
1 Scope
This document provides guidance for some methods that could be used to evaluate the sources of
uncertainty. It is important to note that there are many legitimate methods for analyzing the overall
uncertainty and that the methods in this document are illustrative only.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO/TS 10974:2018, Assessment of the safety of magnetic resonance imaging for patients with an active
implantable medical device
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO/TS 10974 apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https: //www .iso .org/obp
— IEC Electropedia: available at http: //www .electropedia .org/
4 Uncertainty background
4.1 General
The uncertainties are divided into random and systematic uncertainties.
Random errors result in measured values being distributed about the mean value. Measurement
variations are often well approximated by normal or lognormal distributions. Many of the sources
n
of uncertainty for the measurements described in this document are the result of exponential or r
functions, e.g., the decay of power levels as a function of distance from the AIMD, and therefore can be
approximated by lognormal distributions.
In addition to random errors, systematic errors should also be considered. Systematic error is the
error remaining once the random error is removed as shown in Figure 1. Systematic errors should be
eliminated wherever possible.
Key
X range of values Y range of occurrence
1 mean value 4 random error
2 individual value 5 systematic error
3 true value 6 distribution of values
Figure 1 — Relationship of measured, mean, and true values and association of random and
systematic errors
Uncertainty assessments of systems such as these can be dominated in magnitude by a small subset of
uncertainty sources. When independent uncertainty sources are combined smaller uncertainty sources
often contribute negligibly to the overall budget.
A variety of factors contribute to the uncertainty described in Clause 8 of ISO/TS 10974:2018. The
dominant sources of uncertainty are specific to the equipment, measurement methods, and numerical
simulation tools used for the assessment. Clause 8 of ISO/TS 10974:2018 requires an uncertainty
assessment for the measurement system (u ) and AIMD model (u ). There are two additional
exp Predict
sources of uncertainty being clinical uncertainty (from 8.6 of ISO/TS 10974:2018) and power to
temperature uncertainty (8.4.3 of ISO/TS 10974:2018). Techniques for evaluating these uncertainty
terms (u , u , u , u ) are described. As Clause 8 of ISO/TS 10974:2018 has multiple tiers
exp Predict Clinical Power
for evaluation of power deposition, the evaluation of each uncertainty source is specified per tier.
Two methods of uncertainty evaluations are developed in this document. In both methods, the
uncertainty of the entire assembly is determined. In one method, many of the components of the
assembly are grouped and a single uncertainty determination is made for many of them. In the second,
the sources of uncertainty in a system are identified and individually evaluated a priori and the
dominant sources are combined to obtain the system uncertainty.
[1]
GUM has provided approaches for evaluating the uncertainty of assemblies, regardless of component
count, and called their approaches Type A and Type B. Either or both Type A and Type B evaluations for
each method is appropriate.
4.2 Method 1 Evaluation
Method 1 determines the uncertainty of a complex measurement system by considering the variability
of the system as a whole. Method 1 is based on the assumption that a probability distribution of the
random variation of the evaluation results can be deduced from approximation of the measurement or
modelling system where the uncertainty is determined for an assembly or collection of many parts of
the system. In this approach, multiple elements of the system are assembled or ‘lumped’ together and
2 © ISO 2018 – All rights reserved

their combined uncertainty is assessed. Estimates of the standard deviation of this distribution are
obtained by repeated evaluations and statistical analysis of the obtained values.
4.3 Method 2 Evaluation
Method 2 generally dissects the assembly into its constituent parts, determines the uncertainty of each
individually, and then determines the uncertainty of the group by combining the uncertainty of each
of the components. Method 2 is based on reasonably assumed probability distributions that account
for the available information about the quantities concerned, and the standard deviation of these
distributions. This type of evaluation is performed by evaluating the independent sources of uncertainty
of the components of the measurement or modelling system. In this approach, the components of the
system are separated, and the uncertainty of each component is determined. In a subsequent step, the
uncertainty of each is combined together. Techniques for handling the types of distribution of these
uncertainties and normalizing to a standard distribution from non-standard distributions (such as
[1]
rectangular, triangular, and U shaped) are well known . Root sum square (RSS) is a common method
for combining individual uncertainty components, however the method assumes that the terms are
independent of each other.
In practice, some level of overlap between methods 1 and 2 is likely to exist in uncertainty evaluations.
5 Experiment Uncertainty (u )
exp
5.1 General
The measurement system of Clause 8 of ISO/TS 10974:2018 is comprised of the RF field source, tissue
simulating phantom, and probes for measuring temperature rise, SAR or E-field. It also comprises DUT
fixturing to enable accurate positioning of the probe relative to the DUT. The parameter u accounts
exp
for the combined uncertainty of the RF field source, tissue simulating phantom, the measurement
probe, and the positioning of the measurement probe relative to the DUT.
5.2 Measurement tool uncertainty (probe)
For Clause 8 of ISO/TS 10974:2018, measurements of RF hotspots are done using SAR or temperature
probes.
For SAR measurements, absolute measurements are necessary and the absolute accuracy is a
contributor to the overall uncertainty. The absolute SAR uncertainty is determined from the calibration
of the SAR probe. Depending on its use, the probes linearity, isotropy, distortion of the field, and noise
level could influence its uncertainty.
For temperature probes, all temperature rise measurements are relative. Temperature probe placement
accuracy will likely have a greater impact on measurement uncertainty than the repeatability of the
temperature probe due to the spatial distribution of temperature.
5.3 Probe position uncertainty
Temperature or SAR decreases exponentially as a function of distance from the source of the RF
hotspot. Therefore, probe positioning is an important contributor to the overall uncertainty.
Assuming a 10 °C peak temperature, a 1 °C temperature change can be observed in less than 250 µm.
If ∆T measurements are accurate to within 0,1 °C, this is equivalent to better than 25 µm in probe
placement accuracy. Therefore, temperature probe placement accuracy is one of the dominant sources
of uncertainty and should be evaluated. When SAR probes are used, probe placement accuracy is
equally important.
5.4 Tissue simulating phantom
In order to minimize differences between measurements and model predictions, it is desirable
to control the conductivity of the TSM (tissue simulating medium) in addition to the background
temperature. Background temperature control is necessary because conductivity and permittivity
have large temperature coefficients as described in ISO/TS 10974:2018 Annex H.1.2. Permittivity is
relatively insensitive to slight variation in mixing method and similar to conductivity, the variation
during experiment can be controlled by limiting the bulk TSM temperature rise. TSM with various
conductivity values within the tolerance range as specified in 8.3.2 of ISO/TS 10974:2018 can be used
to evaluate the weighting coefficient for its contribution to the uncertainty in the temperature or SAR
measurement.
5.5 RF field source
In order to minimize differences between measurements and model predictions, it is important
to know the E along the lead pathway matches the exposure assumed for prediction. It is typical
tan
to consider and control the RF incident fields along the lead pathway as well as the contribution of
phantom position uncertainty to the RF Field uncertainty. Uncertainty of the RF incident fields along
the lead pathway is more salient than field error at points in the phantom not near the lead pathways,
and hence more useful to control and quantify.
Small changes in the lead pathway can produce a significant change in the measured ∆T, particularly
for lead pathways that are producing significant phase cancelation or phase enhancement. The AIMD
mounting fixture(s) and probe measurement fixture(s) need to be carefully designed in order to
minimize the positional variation of the lead over the entire pathway. Small changes in the position of
any section of the lead can change the magnitude or phase of the incident E and result in a change in
tan
the measured ΔT or SAR.
The fixturing of the lead pathway on the RF field might distort an idealized simulation of this RF field
and lead to uncertainty in the RF field source. Incident field distortion caused by fixturing could be a
source of uncertainty, unless accounted for in the simulated incident fields from which E along the
tan
lead pathways are derived.
5.6 Phantom position uncertainty
Uncertainty in the phantom position can also cause differences between the measured and simulated
E exposures leading to differences in the simulated and actual E along the AIMD. This effect should
tan tan
be closely controlled or quantified as it can be a contributor to the overall uncertainty.
5.7 AIMD influence
The electric field induced in numerical human body models that do not contain AIMDs are used to
define RF heating test environment specified in Tiers 2, and 3 of Clause 8. These methods assume that
the perturbation of the induced electric field due to the AIMD can be neglected or is accounted for in the
RF heating model validation. Care should be taken to ensure that these assumptions are valid for the
AIMD being evaluated (particularly when multiple parts of the AIMD are in close proximity).
5.8 Overall u consideration
exp
The above description of a typical power deposition measurement system identified a number of
contributors to the overall uncertainty.
In using a Method 2 analysis, the uncertainty of each of the above terms is determined. A specific
experiment isolating each of these variables is evaluated, as much as reasonably possible. Then each of
these individual contributions are combined using a RSS method to create an overall u .
exp
In using a Method 1 analysis, an experiment or series of experiments are devised that combines
the equipment or measurement components that are contributors to the overall uncertainty. The
measurements from repeated experiments of these assemblies could use the probes (temperature
4 © ISO 2018 – All rights reserved

and/or incident E field), over the range of measurement probe placements, and over the range of TSM
parameters, and the range of applied RF field are used to calculate a measurement variation, (i.e.
temperature measurement variation/uncertainty).
6 AIMD model uncertainty (u )
Predict
6.1 Piecewise excitation method for deriving AIMD model
Clause 8 of ISO/TS 10974:2018 does not describe a specific method for determination of the AIMD
model, but contains Formula (1) for power deposition, P , predicted by the AIMD model.
hotspot
L
PA= Sz Ez dz (1)
() ()
hotspothotspot tan
∫
where E (z) is the incident tangential E field along the length of the lead. The AIMD model consists of
tan
A, the calibration factor and S (z), the transfer function along an AIMD of length L.
hotspot
[5]
The piecewise excitation method is one among several methods that can be used to derive S (z)
hotspot
for each AIMD hotspot. The piecewise excitation method involves measurement of the induced scattered
complex E-field at the AIMD hotspot under test as the steady state response for each piecewise unit step
E applied at successive discrete locations along t
...