An In-Silico Testbed for Fast and Accurate MR Labeling of Orthopaedic Implants
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:<jats:list list-type="simple"><jats:label>-</jats:label>anin-silicohuman model based on the widely available Visible Human Project (VHP) cryo-section dataset;<jats:label>-</jats:label>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.Thein-silicoVHP 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 (BMI) of 36. The testbed includes the FEM-compatiblein-silicohuman 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 orthopaedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool (MDDT) for 1.5 T orthopaedic implant examinations..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 09. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Noetscher, Gregory M [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.07.16.549234 |
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| PPN (Katalog-ID): |
XBI040243796 |
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| 245 | 1 | 0 | |a An In-Silico Testbed for Fast and Accurate MR Labeling of Orthopaedic Implants |
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| 520 | |a 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:<jats:list list-type="simple"><jats:label>-</jats:label>anin-silicohuman model based on the widely available Visible Human Project (VHP) cryo-section dataset;<jats:label>-</jats:label>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.Thein-silicoVHP 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 (BMI) of 36. The testbed includes the FEM-compatiblein-silicohuman 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 orthopaedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool (MDDT) for 1.5 T orthopaedic implant examinations. | ||
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| 700 | 1 | |a Serano, Peter J. |4 aut | |
| 700 | 1 | |a Horner, Marc |4 aut | |
| 700 | 1 | |a Prokop, Alexander |4 aut | |
| 700 | 1 | |a Hanson, Jonathan |4 aut | |
| 700 | 1 | |a Fujimoto, Kyoko |4 aut | |
| 700 | 1 | |a Brown, James E. |4 aut | |
| 700 | 1 | |a Nazarian, Ara |4 aut | |
| 700 | 1 | |a Ackerman, Jerome |4 aut | |
| 700 | 1 | |a Makaroff, Sergey N |4 aut | |
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