An in silico testbed for fast and accurate MR labeling of orthopedic implants

  1. Gregory M Noetscher
  2. Peter J Serano
  3. Marc Horner
  4. Alexander Prokop
  5. Jonathan Hanson
  6. Kyoko Fujimoto
  7. James Brown
  8. Ara Nazarian
  9. Jerome Ackerman
  10. Sergey N Makaroff

  • Curated by eLife

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    eLife assessment

    This manuscript will provide a valuable method to evaluate the safety of MR in patients with orthopaedic implants, which is required in clinics. A strength of the work is that the in-silicon testbed is solid, based on the widely available human project, and validated. In addition, the toolbox will be open for clinical practice.

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Abstract

One limitation on the ability to monitor health in older adults using magnetic resonance (MR) imaging is the presence of implants, where the prevalence of implantable devices (orthopedic, cardiac, neuromodulation) increases in the population, as does the pervasiveness of conditions requiring MRI studies for diagnosis (musculoskeletal diseases, infections, or cancer). The present study describes a novel multiphysics implant modeling testbed using the following approaches with two examples: (1) an in silico human model based on the widely available Visible Human Project (VHP) cryo-section dataset; (2) a finite element method (FEM) modeling software workbench from Ansys (Electronics Desktop/Mechanical) to model MR radio frequency (RF) coils and the temperature rise modeling in heterogeneous media. The in silico VHP-Female model (250 parts with an additional 40 components specifically characterizing embedded implants and resultant surrounding tissues) corresponds to a 60-year-old female with a body mass index of 36. The testbed includes the FEM-compatible in silico human model, an implant embedding procedure, a generic parameterizable MRI RF birdcage two-port coil model, a workflow for computing heat sources on the implant surface and in adjacent tissues, and a thermal FEM solver directly linked to the MR coil simulator to determine implant heating based on an MR imaging study protocol. The primary target is MR labeling of large orthopedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool for 1.5 T orthopedic implant examinations.

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  1. Version published to 10.7554/elife.90440.3 on eLife
  2. Version published to 10.7554/elife.90440 on eLife
  3. Version published to 10.7554/elife.90440.2 on eLife
  4. eLife

    Author Response

    The following is the authors’ response to the original reviews.

    The Authors wish to thank the Reviewers for their detailed and insightful comments. By properly addressing these critiques, we sincerely believe our finished product will be substantially improved and provide greater insight to the academic community.

    Both Reviewers noted the importance of identifying the limitations of our study with particular emphasis on embedded implant heating due to switching gradient coils. Understanding the limitations of any model and/or simulation process is critical when adopting its use, especially when estimating the safety of embedded devices. For this reason, we have included the following text and corresponding references in our Discussion section:

    While the workflow presented herein establishes a validated approach to estimate RF heating due to the presence of a passive implant within a human subject undergoing an MR procedure, certain limitations and proper use stipulations of this methodology should be identified. These include:

    1. The approach of embedding a given passive implant must be carefully considered and supervised by an orthopaedic subject matter expert, preferably an orthopaedic surgeon. While the procedures described above focus on insertion and registration of an implant to make it numerically suitable for simulation, relevant anatomic and physiological considerations must also be addressed to ensure a physically realistic and appropriate result. This will enable a proper simulated fit and no empty spaces or unintended tissue deformations.

    2. Temperature changes presented are due only to RF energy deposition. The results do not take into account the impact of low-frequency induction heating of metallic implants naturally caused by the switching gradient coils. Important work on this subject matter has recently been reported in [21],[22],[23],[24],[25],[26],[27]. Unless an orthopaedic implant has a loop path, heating due to gradient fields is typically less than heating due to RF energy deposition. The present testbed would be applicable to the induction heating of implants (and the expected temperature rise of nearby tissues), after switching from Ansys HFSS (the full wave electromagnetic FEM solver) to Ansys Maxwell (the eddy current FEM solver). Two examples of this kind have already been considered in [25],[45].

    3. The procedures presented in this work have been based on the response of a single human model of advanced age and high morbidity.

    4. Finally, validation was achieved using available published data [42]-[44] and relies upon the legitimacy and veracity of that data. Coil geometry, power settings, and other relevant parameters were taken explicitly from these sources and modeled to enable a faithful comparison.

  5. eLife

    eLife assessment

    This manuscript will provide a valuable method to evaluate the safety of MR in patients with orthopaedic implants, which is required in clinics. A strength of the work is that the in-silicon testbed is solid, based on the widely available human project, and validated. In addition, the toolbox will be open for clinical practice.

  6. eLife

    Reviewer #1 (Public Review):

    Summary:

    In this work authors are trying to satisfy a real need in MR safety, when concerns can rise about the thermal increase due to metallic materials in patients carrying orthopedic implants. The "MR conditional" labeling of the implant obtained by ASTM in-vitro tests may help to plan the MR scan, but it is normally limited to a single specific MR sequence and a B0 value, and it is not always available. The adoption of an in-silico simulation testbed overcomes this limitation, providing a fast and reliable prediction of temperature increase from RF, in real-life scan conditions on human-like digital models. The FDA is pushing this approach.

    Strengths:

    The presented in-silico testbed looks valuable and validated. It is based on the widely available Visible Human Project (VHP) datasets, and the testbed is available on-line. The approval of the testbed by the FDA as a medical device development tool (MDDT) is a good premise for the large-scale adoption of this kind of solution.

    Weaknesses:

    A couple of limitations of the study are now clearly highlighted to the readers in this revised version of the paper. The following aspects:
    - the lack of the equivalent modeling for the gradients-related heating;
    - the way the implant is embedded in the VHP model that should take in consideration how to manage the removed and stretched tissues;
    are now correctly taken in consideration in the discussion, providing additional literature.

  7. eLife

    Reviewer #2 (Public Review):

    Summary:

    In this article, the authors provide a method of evaluating safety of orthopedic implants in relation to Radiofrequency induced heating issues. The authors provide an open source computational heterogeneous human model and explain computational techniques in a finite element method solver to predict the RF induced temperature increase due to an orthopedic implant while being exposed to MRI RF fields at 1.5 T.

    Strengths:

    The open access computational human model along with their semiautomatic algorithm to position the implant can help realistically model the implant RF exposure in patient avoiding over- or under-estimation of RF heating measured using rectangular box phantoms such as ASTM phantom. Additionally, using numerical simulation to predict radiofrequency induced heating will be much easier compared to the experimental measurements in MRI scanner, especially when the scanner availability is limited.

    Weaknesses:

    The proposed method only used radiofrequency (RF) field exposure to evaluate the heating around the implant. However, in the case of bulky implants the rapidly changing gradient field can also produce significant heating due to large eddy currents. So the gradient induced heating still remains an issue to be evaluated to decide on the safety of the patient. Moreover, the method is limited to a single human model and might not be representative of patients with different age, sex and body weights.

  8. Version published to 10.1101/2023.07.16.549234v2 on bioRxiv
  9. Version published to 10.7554/elife.90440.1 on eLife
  10. eLife

    eLife assessment

    This manuscript will provide a valuable method to evaluate the safety of MR in patients with orthopaedic implants, which is required in clinics. A strength of the work is that the in-silicon testbed is solid, based on the widely available human project, and validated. In addition, the toolbox will be open for clinical practice.

  11. eLife

    Reviewer #1 (Public Review):

    Summary:
    In this work, the authors are trying to satisfy a real need in MR safety, when concerns can arise about the thermal increase due to metallic materials in patients carrying orthopedic implants. The "MR conditional" labeling of the implant obtained by ASTM in-vitro tests may help to plan the MR scan, but it is normally limited to a single specific MR sequence and a B0 value, and it is not always available. The adoption of an in-silico simulation testbed overcomes this limitation, providing a fast and reliable prediction of temperature increase from RF, in real-life scan conditions on human-like digital models. The FDA is pushing this approach.

    Strengths:
    The presented in-silico testbed looks valuable and validated. It is based on the widely available Visible Human Project (VHP) datasets, and the testbed is available online. The approval of the testbed by the FDA as a medical device development tool (MDDT) is a good premise for the large-scale adoption of this kind of solution.

    Weaknesses:
    There are a couple of limitations in the study that must be clearly highlighted to the readers.

    While the RF-related heating is very well modeled, the gradients-related heating is out of the scope of this paper and not considered. Readers must be warned that RF causes only a part of the heating, and literature is reporting cases where also gradient switching can contribute, as correctly mentioned in this work. A cautious attitude should consider this as a significant limitation of the study.

    Moreover, the way the implant is embedded in the VHP model is shortly documented in the materials and methods and mostly focuses on implant registration on bone tissue. It is not clear how to manage the empty space and the soft tissue stretching/reshaping generated by the simulated surgery (for example, by the cut of the femoral head in total hip arthroplasty). It is reported by literature that the level of accuracy in the simulated surgery can impact in some cases (RF vs. gradients heating, massive vs. thin or elongated implants) on temperature predictions.

  12. eLife

    Reviewer #2 (Public Review):

    Summary:
    In this article, the authors provide a method of evaluating the safety of orthopedic implants in relation to radiofrequency-induced heating issues. The authors provide an open-source computational heterogeneous human model and explain computational techniques in a finite element method solver to predict the RF-induced temperature increase due to an orthopedic implant while being exposed to MRI RF fields at 1.5 T.

    Strengths:
    The open-access computational human model along with their semiautomatic algorithm to position the implant can help realistically model the implant RF exposure in patients avoiding over- or under-estimation of RF heating measured using rectangular box phantoms such as ASTM phantom. Additionally, using numerical simulation to predict radiofrequency-induced heating will be much easier compared to the experimental measurements in an MRI scanner, especially when the scanner availability is limited.

    Weaknesses:
    The proposed method only used radiofrequency (RF) field exposure to evaluate the heating around the implant. However, in the case of bulky implants, the rapidly changing gradient field can also produce significant heating due to large eddy currents. So the gradient-induced heating still remains an issue to be evaluated to decide on the safety of the patient. Moreover, the method is limited to a single human model and might not be representative of patients with different age, sex, and body weights. Additionally, the authors compare the temperature rise predicted by their method to an earlier study. However, there is no information about how they controlled the input power in their simulation testbed compared to the earlier study in showing validation of the method.

  13. Version published to 10.1101/2023.07.16.549234v1 on bioRxiv