Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Summary

We describe a standardized method to evaluate magnetic resonance imaging artifacts caused by implants to estimate the suitability of the implants for magnetic resonance imaging and/or the vulnerability of different pulse sequences to metallic artifacts simultaneously.

Abstract

As the number of magnetic resonance imaging (MRI) scanners and patients with medical implants is constantly growing, radiologists increasingly encounter metallic implant-related artifacts in MRI, resulting in reduced image quality. Therefore, the MRI suitability of implants in terms of artifact volume, as well as the development of pulse sequences to reduce image artifacts, are becoming more and more important. Here, we present a comprehensive protocol which allows for a standardized evaluation of the artifact volume of implants on MRI. Furthermore, this protocol can be used to analyze the vulnerability of different pulse sequences to artifacts. The proposed protocol can be applied to T1- and T2-weighted images with or without fat-suppression and all passive implants. Furthermore, the procedure enables the separate and three-dimensional identification of signal loss and pile-up artifacts. As previous investigations differed greatly in evaluation methods, the comparability of their results was limited. Thus, standardized measurements of MRI artifact volumes are necessary to provide better comparability. This may improve the development of the MRI suitability of implants and better pulse sequences to finally improve patient care.

Introduction

MRI has become an indispensable diagnostic tool. As a result, the number of MRI systems used in routine diagnostics is further increasing¹. At the same time, the number of patients with implants is increasing as well^2,3. In 2012, for instance, more than 1 million knee and joint replacements have been performed in the USA alone⁴. The prevalence of such implants was about 7 million in 2010, which corresponds to more than 10% of females in the age group 80-89 years⁵. As a result, the image quality and the diagnostic significance of MRI examinations are often impaired by artifacts due to metallic implants, resulting in a decreased diagnostic accuracy. Therefore, the MRI suitability of implants and the artifact vulnerability of pulse sequences are becoming increasingly important. Numerous approaches have been published to evaluate these characteristics. Due to strong discrepancies in the used evaluation methods, however, the respective results are hard to compare.

An evaluation of the MRI suitability of materials can be performed by calculating their magnetic susceptibility⁶. However, the vulnerability of different pulse sequences to artifacts cannot be compared with that approach for a given implant. Vice versa, the artifact volumes for a given pulse sequence can only be roughly estimated for different implants. In addition, the analysis is often performed with artificially shaped implants^7,8. As the material volume and shape have an influence on the artifact size⁶, these features should be taken into account as well. As an alternative to magnetic susceptibility, the artifact size can be evaluated. Frequently, studies only rely on the qualitative evaluation of the artifact size⁹ or focus on the two-dimensional artifact size only covering one slice of the implant artifact^10,11. Moreover, manual segmentation approaches are often used, which is not only time-consuming but also prone to intra- and inter-reader differences¹¹. Finally, protocols often do not allow to test for non-fat-saturated and fat-saturated sequences at the same time¹². This, however, would be desirable, since the applied fat suppression technique profoundly affects the artifact size.

Here, we present a protocol which allows for the reliable, semiautomatic, threshold-based, three-dimensional quantification of signal loss and pile-up artifacts of the entire implant, or all slices containing visible implant artifacts. Furthermore, it allows for testing T1- and T2-weighted images with or without fat-saturation. The protocol can be used to evaluate the MRI suitability of different implants or the vulnerability of different pulse sequences to metallic artifacts for a given implant.

Protocol

1. Phantom Preparation

1. Determine the implant volume (e.g., by using the water displacement method).
   NOTE: The volume of the CCT-T sample and the Z-T sample measured 0.65 mL and 0.73 mL, respectively.
2. Fix the implant position in the middle of a non-ferromagnetic, plastic, waterproof box by using a thin thread. Use a box that is larger than the expected MRI artifacts.
   NOTE: If no rough estimates of the artifact volumes of the implant and/or pulse sequence of interest are available, perform a test scan by placing the phantom in a box, approximately 10x larger than the phantom, filled with water. The artifact volumes in this study ranged from 7.3 mL (for the CCT-T sample) and 0.09 mL (for the Z-T sample).
3. Carefully melt a mixture of semisynthetic fat (58.8%), water (40%), and macrogol-8-stearate (1.2%), using a water bath at 50 °C.
   NOTE: For the samples in this study, we used a 500 mL mixture for the embedding of each sample.
   1. When the mixture becomes fluid, stop heating and start with slow stirring, and stop heating. Ensure that there is no separation of the fat and water phases.
4. As soon as clotting begins, slowly start embedding the implant with the mixture. For this, pour the embedding mixture slowly into the phantom box with the implant.
   NOTE: Pouring must be performed slowly to avoid air inclusion.
5. Place the phantom box with the embedded implant into the refrigerator at 4 °C overnight for desiccation. The next day, remove any residual fluid parts by decantation.

2. MRI Examination

1. Place the phantom (box with the embedded implant) in the MRI in the same orientation as in the in vivo situation. Position the middle of the phantom in the isocenter of the MRI.
2. For measurements, use a coil which allows for a homogeneous signal distribution within the imaging volume without severe and obvious signal drops (e.g., a head coil).
3. When planning the MRI scans at the MRI console, ensure that the phantom box, including some air at the edges of the box, is within the imaging volume.
4. Next, perform the MRI examination.

3. Image Analysis and Post-processing

1. Export the images without any loss of quality (e.g., by compression) from the MRI console (e.g., using the DICOM format). Import the images in an MRI post-processing software which allows for placing the region of interests (ROI), evaluating ROI signal intensities, a threshold-based segmentation, and a quantification of the segmentation volumes (see Table of Materials).
2. To define the threshold for pile-up artifacts and check for a homogeneous signal distribution within the imaging volume, place lines perpendicular to each other and adjacent to the outer border of the visible artifact on the slice with the maximum artifact size (Figure 1a).
   NOTE: Pile-up artifacts are displacement artifacts, presenting with areas with artificially high signal intensities. They occur in the slice direction and the readout direction.
   1. Place a background ROI (ROI_Background) with 10 mm in diameter outside each of the four intersection points (Figure 1a). Place the lines and the background regions of interests using the segmentation editor.
   2. Measure the mean signal intensity and standard deviation (SD) of all voxels within these 4 ROI_Background values and for each ROI_Background separately. Use the tool Material Statistics in the project view.
   3. Ensure that the mean signal intensity of each ROI­_Background is within the range of ± 1.5 SD of the mean signal of each of the other 3 counterparts to guarantee a homogeneous signal distribution.
   4. Calculate the threshold for pile-up artifacts by adding 3 SD of ROI_Background to the mean signal intensity of all voxels of these 4 ROI_Background values. Perform a semiautomatic threshold-based segmentation of pile-up artifacts by selecting all voxels with the signal intensities larger than the threshold adjacent to the signal loss artifact in every slice. Use the masking tool of the segmentation editor to visualize the predefined signal intensity range and restrict the segmentation to it.
3. To define the threshold for signal loss artifacts, place 4 regions of interests (ROIs) in air-containing regions (ROI_Air; each 10 mm in diameter) at the corners of the phantom box and measure the mean signal intensity and SD of all voxels within these 4 ROI_Air as described in step 3.2, using the segmentation editor and "Material Statistics", respectively.
   NOTE: Signal loss artifacts present with voxels having artificial low signal intensities. They are caused by dephasing and displacement artifacts.
   1. Place an ROI in the core of the signal loss artifact (ROI_Core) defined by the largest connected area of low signal intensities (Figure 1a). Manually increase the size of the ROI_Core until the largest possible size within the signal loss artifact whose mean signal intensity is lower than the mean ROI_Air + 3x of the respective SD is found. Finally, measure the mean signal intensity and SD of the ROI_Core.
   2. Calculate the signal intensity threshold for signal loss artifacts by adding 3 SD of the ROI_Core to the mean of the ROI_Core. Perform a semiautomatic threshold-based segmentation of signal loss artifacts by selecting all voxels connected to the ROI_Core with signal intensities below the threshold.
   3. Use the masking tool of the segmentation editor to visualize the predefined signal intensity range and restrict the segmentation to it. If possible, use the "Fill" function in the tap "Selection" of the segmentation editor to include all voxels within the segmentation that are not yet selected. If applicable, manually add the additional unequivocal signal loss artifacts to the segmentation.
4. Subtract the physical implant volume from the calculated artifact volume to obtain the true artifact volume. Repeat the analysis at least 3x. A time interval of at least two weeks should separate the multiple reads to exclude a learning bias.

Results

With the above-mentioned protocol, we evaluated the artifact volume of 2 different dental implants made of Titanium (T; see the Table of Materials) supporting different crowns [porcelain-fused-to-metal non-precious alloy (CCT-T) and monolithic zirconia (Z-T); Figure 1b and 1c]. The CCT-T sample represents a highly paramagnetic material composition predicting large artifacts (Cobalt 61%, Chrome 21%, and Tungsten 11%; CCT). The...

Discussion

The number of patients with metallic implants and the number of MRI examinations is currently increasing^1,2,3. In the past, MRI examinations were avoided after joint replacements. Today, however, MRI is not only requested for imaging such patients but should also allow for the evaluation of complications directly adjacent to joint arthroplasty. Thus, the MRI safety and MRI suitability of implants, as well as the robust pulse seq...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Aqua B. Braun Ecotainer	B. Braun Melsungen AG, Melsungen, Germany
Semisynthetic fat: Witepsol W25	Caelo Caesar & Loretz GmbH, Hilder, Germany	4051
Macrogol-8-stearate	Caelo Caesar & Loretz GmbH, Hilder, Germany	3023
Plastic box: not specified
Implants: Nobel Replace	Nobel Biocare, Zürich, Switzerland
Water bath Haake S5P	Thermo Scientific, Waltham, MA, USA
Measuring cylinder Blaubrand Eterna, Class A, Boro 3.3	BRAND GmbH + Co Kg, Wertheim, Germany	32708
Coil: Variety	Noras MRI products GmbH, Höchberg, Germany
MRI: Magnetom Trio	Siemens Healthcare GmbH, Erlangen, Germany
Postprocesing software: Amira 6.4	Thermo Scientific, Waltham, MA, USA