Contains Nonbinding Recommendations 
Assessment of Radiofrequency-
Induced Heating in the Magnetic 
Resonance (MR) Environment for 
Multi-Configuration Passive Medical 
Devices
 
Guidance for Industry and  
Food and Drug Administration Staff  
Document issued on March 22, 2016. 
The draft of this document was issued on June 29, 2015. 
For questions about this document, contact the Division of Biomedical Physics, Office of 
Science and Engineering Laboratories at (301) 796-2580 or Wolfgang Kainz, Ph.D., at (301) 
661-7595 or by email wolfgang.kainz@fda.hhs.gov. 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Devices and Radiological Health  
 
Office of Science and Engineering Laboratories (OSEL) 
Division of Biomedical Physics 
  Contains Nonbinding Recommendations 
Preface 
Public Comment 
You may submit electronic comments and suggestions at any time for Agency consideration to 
http://www.regulations.gov. Submit written comments to the Division of Dockets Management, 
Food and Drug Administration, 5630 Fishers Lane, Room 1061, (HFA-305), Rockville, MD 
20852. Identify all comments with the docket number FDA-2015-D-2104. Comments may not be 
acted upon by the Agency until the document is next revised or updated. 
Additional Copies 
Additional copies are available from the Internet. You may also send an e-mail request to 
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number 1500001 to identify the guidance you are requesting. 
  Contains Nonbinding Recommendations 
Table of Contents 
I.  Introduction ...........................................................................................................................................................1
II.  Scope.....................................................................................................................................................................2
III.  Overview of Heating Assessment .....................................................................................................................2
IV.  Recommended Methods to Reduce the Number of Configurations and Combinations for Testing .................6
V.  Hypothetical Example ...........................................................................................................................................9
  Contains Nonbinding Recommendations 
Assessment of Radiofrequency-
Induced Heating in the Magnetic 
Resonance (MR) Environment for 
Multi-Configuration Passive Medical 
Devices
 
 
Guidance for Industry and  
Food and Drug Administration Staff  
This guidance represents the current thinking of the Food and Drug Administration (FDA or 
Agency) on this topic.  It does not establish any rights for any person and is not binding on 
FDA or the public.  You can use an alternative approach if it satisfies the requirements of the 
applicable statutes and regulations.  To discuss an alternative approach, contact the FDA staff 
or Office responsible for this guidance as listed on the title page.  
I.  Introduction 
This guidance provides an assessment paradigm for radiofrequency (RF)-induced heating for 
multi-configuration passive medical devices in the magnetic resonance (MR) environment, 
including multi-component and single-component device types with various dimensions and 
shape.  Multi-component passive devices, such as orthopedic fixation devices, may result in a 
very large number of possible device configurations and combinations of individual components.  
Single-component devices, such as cardiovascular stents, are also frequently available in 
multiple sizes or configurations.  For these multi-configuration passive devices, it can be 
challenging to leverage RF-induced heating testing from one device configuration or 
combination to other device configurations or combinations because the geometry or 
configuration of the device can affect heating in an unknown manner.  As a result, the total 
number of possible configurations or combinations that need to be assessed for RF-induced 
heating of some passive devices can be very large.   
This document provides an approach to reduce the number of possible device configurations or 
combinations to a manageable number for the testing of RF-induced heating in the MR 
environment.  Additionally, this document provides guidance on how to assess RF-induced 
device heating for multi-configuration passive medical devices.  The information provided in this 
guidance is intended to be used to support MR Conditional labeling claims in conjunction with 
  1  Contains Nonbinding Recommendations 
the information provided in FDAs current guidance document for Establishing Safety and 
Compatibility of Passive Implants in the Magnetic Resonance (MR) Environment 
(http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocu
ments/UCM107708.pdf).   
For the current edition of the FDA-recognized standard(s) referenced in this document, see the 
FDA Recognized Consensus Standards Database Web site at 
http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfStandards/search.cfm. 
FDAs guidance documents, including this guidance, do not establish legally enforceable 
responsibilities. Instead, guidances describe the Agencys current thinking on a topic and should 
be viewed only as recommendations, unless specific regulatory or statutory requirements are 
cited. The use of the word should in Agency guidance means that something is suggested or 
recommended, but not required.   
II.  Scope 
This document provides guidance on a recommended method to select worst-case device 
configurations or combinations to be tested for RF-induced heating in the MR environment.  
Additionally, this document provides guidance on how to assess RF-induced device heating for 
multi-configuration passive medical devices. 
This guidance applies to multi-configuration passive devices consisting of multiple components, 
as well as single-component devices, which can be used in multiple configurations.  A passive 
device is one that functions without the supply of electrical power. These devices may be 
completely implanted (e.g., cardiovascular stents, spinal fixation devices), partially implanted in 
the patients body (e.g., external fracture fixation devices), or used entirely externally (e.g., 
electroencephalographs).  This document is applicable to all electrically conductive multi-
configuration passive medical devices intended to be used in the MR environment that include 
MR Conditional labeling, regardless of their size, or number and configuration of components.   
Active devices, or devices that require use of internal or external electrical power, are not within 
the scope of this guidance.  In addition, this guidance document does not establish a heating 
acceptance criterion in general or for any specific medical device.  Such a criterion will depend 
on the intended use and the benefit-risk profile of the device.  Rather, this guidance document 
recommends a basis to assess the temperature rise induced by interaction between the device and 
the electromagnetic fields in the MR environment. 
III.  Overview of Heating Assessment 
The methodology below describes the recommended method to reduce a large number of 
possible device configurations or combinations to a manageable number (i.e., test set) for the 
assessment of RF-induced heating in the MR environment, and the recommended method to 
conduct an assessment of RF-induced heating for the devices within the identified test set.  
  2  Contains Nonbinding Recommendations 
1.  Define and describe the MR Conditional scan conditions for the proposed 
labeling, such as magnetic field strength, whole-body average Specific 
Absorption Rate (SAR) levels, B1+  levels, coil types, landmark position (i.e., 
rms
position of the device relative to the MR bore), and scan area.  
2.  Provide a scientific rationale based on benefit-risk considerations, using 
information from previous human trials, animal data, and/or published literature 
to establish the heating acceptance criterion (i.e., the maximum in vitro or in vivo 
heating allowed for your medical device).  This criterion should be defined as the 
peak rise in temperature over a 15-minute period.   
3.  Define and describe all possible device configurations and combinations (CC ) 
all
using tables, lists, and/or drawings.  The description should include device size, 
geometry, surface properties, and design variations, in addition to all materials 
used, their electrical properties (i.e., the electrical conductivity and the 
permittivity at the frequency of interest) and thermal properties (i.e., thermal 
conductivity and specific heat capacity).  While it is not necessary to describe 
each individual configuration/combination, the limits in dimensions, geometry, 
and the different types of configurations/combinations should be clearly 
identified, preferably in a tabular or matrix format. 
4.  Use a scientific rationale or a scientific method, such as those described in section 
IV below, to reduce CC  to a subset of potential worst-case device configurations 
all
and combinations (CC ) for heating assessments.  The scientific method used to 
test
reduce CC  to CC  should include a detailed description of the algorithm and 
all test
parameters used.  The scientific rationale should note any clinically relevant 
information and known worst-case factors for RF-induced heating.  Additionally, 
the rationale should include a consideration of the risk and the potential harm to 
the patient. 
Factors influencing RF-induced heating include, but are not limited to, the 
following: 
a.  Device Dimensions and Resonant Effects: The half-wavelength of the 
electromagnetic field inside a patient for 1.5T systems is about 25 cm, and for 
3.0T systems about 12 cm.  For implants with dimensions on the order of a 
half-wavelength to a wavelength (i.e., 25 to 50 cm or 12 to 24 cm), resonant 
effects between the device and electromagnetic field can lead to significantly 
high heating (i.e., more than twice the background heating).1  RF-induced 
heating can change significantly if the device dimensions change by about 
                                                           
1 Kainz, W., (2007). MR Heating Tests of MR Critical Implants, Journal of Magnetic Resonance 
Imaging, vol. 26, pp. 450451. 
  3  Contains Nonbinding Recommendations 
one-tenth of a wavelength.2  Therefore, device heating for dimension 
increments of about one-tenth of the wavelength should be assessed (i.e., 
approximately 5 cm for 1.5T and 2.4 cm for 3.0T). 
b.  Device Geometry: The RF-induced heating can depend on the: 1) shape of the 
device, 2) cross-section of the device, and 3) position of the device relative to 
the RF transmit coil.  
c.  Device Components (e.g., screws): All possible component configurations 
should be considered.  Because a worst-case screw configuration cannot be 
determined by length alone, all screw lengths and screws in all possible 
openings/holes of the device, and all possible directions should be considered 
(e.g., in increments of 15 degrees with a scientific justification for the subset 
of screw directions examined). 
d.  Device Configuration: Sub-components in electrical contact with each other 
can significantly change the RF-induced heating.  Therefore, devices should 
typically be studied as the entire construct rather than individual sub-
components. However, if sub-components are not electrically connected and 
you can demonstrate that the heating of the individual sub-components is 
independent of each other, then the individual sub-components can be 
assessed independently. 
e.  Surface Properties: Devices with smoother surfaces typically heat less, while 
devices with sharp edges tend to heat more. 
f.  Device Construction: Devices constructed of a continuous conductive material 
(e.g., bare metal stents) may exhibit different heating properties than devices 
constructed of multiple individual components connected through non-
conductive materials (e.g., endovascular stent grafts). 
5.  The minimum number of configurations/combinations within CC  depends on 
test
the number of CC , the proposed scan conditions, the device size and geometry, 
all
and the electrical and thermal properties of the device materials used.  
6.  Once an appropriately justified CC  has been defined, you should perform an 
test
assessment to identify which device configuration/combination within CC  is 
test
worst-case for each MR environment (i.e., scan condition) in which your device is 
intended to be used. This assessment can be done by: 
a.  in vitro temperature measurements according to ASTM F2182,3  
                                                           
2 Ting, S. et al., (2015). Retrospective Analysis of Data in RF Heating Tests of Small Passive 
Medical Implants. Abstract No. 1856, International Society for Magnetic Resonance in Medicine 
(ISMRM) 23rd Annual Meeting and Exhibition, Toronto, CA. 
  4  Contains Nonbinding Recommendations 
b.  computer modeling to determine temperature,  
c.  computer modeling to determine local SAR, or 
d.  a combination of a, b, and/or c. 
Note  that  all  results  using  computer  modeling  should  be  validated  against 
measurements using standardized methods, e.g., ASTM F2182, and include a 
detailed uncertainty analysis.4   
Using one of the above methods, the location of the maximum heating on the 
device surface should be determined for all devices in CC .  The heating at this 
test
location for a specified local SAR should then be determined experimentally or 
computationally.  The local SAR should be determined before performing the 
heating tests. 
With the exception of simple elongated structures (e.g., stents), where the location 
of the maximum heating is usually at the end of the structure, the location of the 
maximum heating on the device surface should be estimated with an experimental 
or computational method.  Also, if the geometry of your device is highly irregular 
and has geometrical features oriented in more than one direction, testing of all 
devices in CC  in all three exposure orientations (i.e., alignment of the three 
test
major axes of the device relative to the tangential induced electric field) should be 
performed.  If the device is located outside of the patient, you should determine 
the heating for a specified electric field in air.  If the device is partially outside 
and partially inside of the patient, you should determine the heating of the device 
in a clinically relevant position partially outside and partially inside of a test 
phantom for both a specified local SAR, and for a specified electric field in air. 
For all testing, you should report the computationally or experimentally assessed 
heating for the worst-case configuration as the peak rise in temperature for a 15-
minute RF exposure; you should also report the local SAR (e.g., 10 W/kg) and/or 
the local electric field (e.g., 100 V/m) at which the device was tested.  You should 
also report the location on the device surface where the worst-case heating was 
assessed.  Shorter evaluation times (e.g., 2 minutes) are possible to assess the 
relative heating between individual configurations/combinations and to assess the 
location of the maximum heating on the device surface of an individual 
configuration/combination.  However, the heating for the worst-case 
configuration/combination should be reported as the peak rise in temperature for a 
15-minute RF exposure. 
The scope of ASTM F2182 is limited to devices entirely implanted inside the 
body.  However, for medical devices with other implantation conditions (e.g., 
                                                                                                                                                                                           
3 ASTM F2182, Standard Test Method for Measurement of Radio Frequency Induced Heating 
On or Near Passive Implants During Magnetic Resonance Imaging. 
4 Taylor, B. and Kuyatt, C., (1994). Guidelines for Evaluating and Expressing the Uncertainty of 
NIST Measurement Results. Technical Note 1297, Gaithersburg, MD, NIST. 
  5  Contains Nonbinding Recommendations 
external fixation devices, catheters), RF-induced heating can be evaluated 
experimentally and/or computationally using a method similar to that described in 
ASTM F2182, with modifications for the specific medical device and the context 
of use.  ASTM F2182 should not be used to assess the worst-case MR-induced 
RF-induced heating for medical devices used in multi-channel transmit RF coils.   
7.  Provide an estimate of the accuracy of the results (i.e., an uncertainty analysis for 
all measured or computed results).5  In addition, validation data for all 
computational models should be provided. 
8.  If the observed worst-case in vitro heating exceeds your specified heating 
acceptance criterion, i.e., the peak rise in temperature over a 15-minute period, 
you may estimate the expected worst-case in vivo heating to demonstrate the 
safety of your device in the MR environment.  Because in vitro testing outlined in 
ASTM F2182 does not consider the actual in situ electric fields, in vitro heating 
results may be substantially higher than the actual in vivo heating.  The estimated 
in vivo assessment should consider the patient population for which your device is 
indicated and should include the worst-case scan conditions.  Alternately, you 
may elect to define additional constraints for your MR Conditional labeling.   
For complex multi-component devices we recommend that you submit a pre-submission to 
obtain feedback on your plan for identifying and assessing CC  and your plan for conducting in 
test
vitro RF-induced heating measurements before conducting the assessments.  Please refer to 
FDAs Guidance Requests for Feedback on Medical Device Submissions: The Pre-Submission 
Program and Meetings with Food and Drug Administration Staff 
(http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocumen
ts/ucm311176.pdf).   
IV.  Recommended Methods to Reduce the Number of 
Configurations and Combinations for Testing 
The number of possible device configurations can become very large for RF-induced heating 
testing if multiple parameters vary between configurations or if each parameter has a range of 
options.  For example, if a device is defined by three parameters (e.g., length, width, and 
thickness), and each of these parameters has 100 different options there will be 1003 = 1,000,000 
possible combinations for CC .  Because many combinations or configurations within CC  will 
all all
be very similar to neighboring combinations or configurations, statistical or stochastic sampling 
of the same parameter set and eliminating certain parameters based on a scientific rationale can 
significantly reduce the number of devices to be tested to a smaller subset (CC ), while still 
test
providing an accurate representation of CC . 
all
To reduce CC  to CC , first perform a sensitivity analysis to assess the effect of each 
all test
parameter on RF-induced heating.  One simple type of sensitivity analysis is the minimum-
                                                           
5 See footnote 4 above. 
  6  Contains Nonbinding Recommendations 
maximum differentiability.  Using this method, the sensitivity of RF-induced heating to each 
parameter can be assessed by testing its maximum and minimum value, while other parameters 
are set at their mean values.  Once the critical parameters are determined, a selection method 
such as, but not limited to, those outlined below, or a combination thereof, should be used to 
identify device configurations and combinations for heating assessments:  
1.  Constant Value Justification:  If many parameters are varied between design 
configurations, it is possible that some parameters have little or no influence on 
RF-induced heating (i.e., less than 5%) and may justifiably be set at a constant 
value to reduce CC .  The maximum-minimum approach outlined above may be 
all
an appropriate sensitivity analysis to support this justification.  
2.  Sampling Methods: CC  can often be reduced substantially, while providing a 
all
comprehensive response evaluation, by using a non-deterministic, pseudo-random 
sampling technique such as a Monte-Carlo analysis (e.g., Haldar and Mahadevan, 
20006).  A Monte-Carlo analysis generally consists of the following components:  
a.  Creating a deterministic model which can reliably reach a solution for the 
range of random distribution of device parameters in the problem. 
b.  Defining the appropriate probabilistic characteristics of each random 
parameter.  These could take the form of distribution parameters (e.g., the 
mean and standard deviation of a normal distribution), or as a set of 
cumulative distribution percentiles (without a named distribution).  For the 
purpose of identifying worst-case  configurations/combinationsfor RF-
 
induced heating tests, a uniform distribution may provide an effective 
parameter sweep.  For a design parameter with a specific set of nominal 
values (e.g., length = 10, 15 or 20 mm) a discrete random variable, with equal 
likelihood for each nominal value, may be most effective. 
c.  Generating samples of these random parameters for testing (to identify 
devices for CC ).  Several sampling methods are commonly used: a) random 
test
sampling, b) Latin hypercube sampling, and c) importance sampling (Helton 
et al., 20067).   
In random sampling, the input parameters are sampled according to their 
probability density functions, with each sample independent of the others.  
This approach offers the advantage of conceptual simplicity and the ability to 
easily add new samples if sufficient accuracy has not been achieved (see item 
f below).   
                                                           
6 Haldar, A. and Mahadevan, S., (2000). Probability, Reliability and Statistical Methods in 
Engineering Design. New York: John Wiley & Sons, Inc., ch. 9. 
7 Helton, J. et al., (2006). Survey of Sampling-Based Methods for Uncertainty and Sensitivity 
Analysis. Reliability, Engineering and System Safety, vol. 91, pp. 1175-1209. 
  7  Contains Nonbinding Recommendations 
The Latin hypercube method may increase sampling efficiency relative to 
random sampling.  In this method, the number of samples (N) are selected at 
the outset.  Each parameter is stratified into N, i.e., the number of samples, 
equally likely intervals, each of which is randomly sampled only once (Helton 
et al., 20068).  The relative efficiency of this method arises from the 
stratification that prevents overlapping samples and guarantees more complete 
parameter space filling than random sampling.   
Importance sampling concentrates sampling near parameter values that are 
most likely to induce higher levels of RF-induced heating.  This method can 
be used to focus tests towards those combinations of parameters to which RF-
induced heating is most sensitive.  The disadvantage of this approach is that it 
requires distortion of the sampling distributions, which should be justified and 
corrected for, when evaluating the system results.   
In generating samples, it is first necessary to estimate the number of samples 
needed to achieve the required accuracy.  An estimate can be calculated from  
N = 4 / 2 
where N is the estimated number of samples and  is the uncertainty of the 
mean RF-induced heating; e.g., <10% (Haldar and Mahadevan, 20009). 
d.  Solving the deterministic model for all samples. 
e.  Combining the individual model solutions into probabilistic system 
information.  In the present application, the maximum expected level of RF-
induced heating is quantified.  In practice, the maximum heating identified 
from all tests will depend on the number of tests, such that a higher maximum 
will be likely when more samples are taken.  Therefore we recommend 
reporting a consistent percentile of RF-induced heating, such as the 95th or 
99th percentile; other percentile values are possible.  You should choose the 
percentile base on the risk associated with RF-induced heating of your 
particular device. 
f.  Evaluating the accuracy of the simulation study and the necessity of additional 
analyses.  As noted in item e above, the observed RF-induced heating results 
will depend upon the number of samples, with uncertainty remaining about 
intermediate, un-sampled, cases.  Therefore, we recommend that you evaluate 
the degree of convergence of the RF-induced heating results to ensure the 
conclusions are not dependent upon the specific sample used.   
                                                           
8 Ibid. 
9 See footnote 6 above. 
  8  Contains Nonbinding Recommendations 
In random sampling, each sample point is independent of the others and 
additional random points can be added until a stopping criterion is converged 
upon.  A recommended stopping criterion could be when the standard 
deviation of the mean RF-induced heating (taken from bootstrapped datasets 
of e.g., 20 to 50 sub-samples converges within a pre-defined uncertainty of the 
mean RF-induced heating (e.g., <10%).  Note that this criterion can be applied 
to evaluate a completed Latin hypercube analysis; however, a post-hoc 
addition of sample points will disturb the original scheme and result in a non-
Latin hypercube sample and hence slower convergence to the stopping 
criterion than might be anticipated.   
An alternative approach, often used with Latin hypercube sampling, is to run 
multiple, equally-sized, sample sets (e.g., two or three sets of 100 samples).  
Once the analyses are completed, the results of the samples can be compared 
(e.g., T-test) to confirm that the mean RF-induced heating from the different 
samples is statistically equivalent.  Once this is confirmed, the data from the 
different samples can be combined into one dataset (Helton et al., 200610). 
V.  Hypothetical Example 
A fracture fixation system contains 10 plates of the same thickness, but different lengths (50mm 
to 250mm in 5mm increments) and two widths (8mm and 12mm).  The plate can be used with 5 
to 20 screws.  There is only one compatible screw diameter, but a continuously variable screw 
length (15-25mm), and the screws can be angled in any direction up to 30 degrees.   
First, a sensitivity analysis of parameters reveals that RF-induced heating does not change 
significantly (less than 5%) with the plate width, with the direction angle of the screws, or with 
various contouring (plate bending to fit the fracture) of the plate.  Therefore the plate width is 
kept constant at 12mm (which showed slightly higher heating than the 8mm width), the screw 
direction angle is kept constant at 0 degrees, and the plate is kept unbent.   
Next, the number of samples is estimated to give approximately 10% uncertainty in the mean 
RF-induced heating.  Using the equation in Section IV, 2.c. above (i.e., N = 4/2 = 4/0.12), 400 
random models (CC ) of the fracture fixation system are created for different combinations of 
test
plate length, screw number, and screw length.  Plate length is defined as a discrete random 
variable with equally likely possible values at intervals of 5mm from 50-250mm.  Screw number 
is defined as a discrete random variable with equally likely possible values at intervals of 1 from 
5-20 screws.  Screw length is defined as a uniform distribution with minimum and maximum 
values of 15mm and 25mm, respectively.  A random sampling method is used for each parameter 
to generate the 400 models to be solved.  
                                                           
10 See footnote 7 above. 
  9  Contains Nonbinding Recommendations 
After all 400 models are solved, the stopping criterion is evaluated to confirm that the calculated 
mean RF-induced heating does not vary by more than 10% regardless of the addition of more 
samples.  This is done in five steps:  
1.  The mean RF-induced heating of the 400 results is calculated (e.g., 3.5C);  
2.  the 400 heating results are randomly resampled into sub-samples (e.g., 25 sub-
samples), each containing 16 results;  
3.  the mean of each sub-sample is calculated as 25 different values;  
4.  the standard deviation of the 25 sub-sample means is calculated (e.g., 0.31C); 
and  
5.  the standard deviation from the mean of all 25 sub-samples is less than 10% of the 
mean RF-induced heating of the 400 results (i.e., 0.31C < 10% of 3.5C), 
indicating that the stopping rule has been satisfied.   
The device configurations/combinations that result in the highest heating are identified from all 
400 models tested.  The highest device heating is calculated as the 99th percentile of the 
distribution of RF-induced heating observed in all 400 tested models. 
  10