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

The overall goal of this method is to evaluate the suitability of implants for magnetic resonance imaging, or estimate the vulnerability of pulse sequences to metal artifacts. This method can help to answer key questions in the radiology field, such as the evaluation of MRI suitability of implants and the estimation of vulnerability of pulse sequences from metal artifacts. The main advantage of this methodology is that it allows for a three-dimensional artifact evaluation in fat-suppressed and not fat-suppressed T1 and T2 weightings at the same time.

The implications of this technique extend towards diagnosis of peri-implant diseases because it allows for optimization of MRI protocols with regard to reduction of peri-implant artifacts. Before constructing the phantom for this protocol, first determine the volume of the implant to be studied. Here, water displacement method is used, and the volume of the samples measured 0.65 milliliters and 0.73 milliliters respectively.

Select a non-ferromagnetic plastic waterproof box that is larger than the expected MRI artifacts, then fix the implant position in the middle of the box using a thin thread. Next, carefully melt a mixture of semi-synthetic fat, water, and macrogol-8-stearate using a water bath at 50 degrees Celsius. Here, a 500 milliliter mixture was used for the embedding of each sample.

Once the mixture becomes fluid, turn off the heat and then start stirring it slowly. While doing this, ensure that there is no separation of the fat and water phases in the mixture. Then, as soon as clotting begins, slowly pour the embedding mixture into the phantom box with the implant, being sure to pour gradually to avoid air inclusion.

Finally, place the phantom into a refrigerator at four degrees Celsius overnight for desiccation, and decant any remaining residual fluid the next day. For the MRI examination, use a radiofrequency coil that allows for a homogeneous signal distribution within the imaging volume without severe and obvious signal drops. Be sure to position the phantom with the embedded implant within the radio frequency coil in the same orientation as it would be placed in vivo.

Position the middle of the phantom in the iso center of the MRI. Next, set up the imaging sequences at the MRI console, making sure that the phantom box, including some air at the edges of the box, is within the imaging volume. Then acquire the images that will be evaluated for artifacts.

Export the images from the MRI console without compression preferably using DICOM format. Then, import The images into post processing software that allows for placing regions of interest, or ROIs, evaluating signal intensities, threshold based segmentation, and a quantification of segmented volumes. To define the threshold for pile-up artifacts, and check for homogeneous signal distribution, first use the segmentation editor to place perpendicular lines adjacent to the outer border of the visible artifact on the slice with the maximum artifact size.

Then, place a background ROI 10 millimeters in diameter outside each of the four intersection points. Next, in the project view, you use the material statistics tool to measure the mean signal intensity and standard deviation of all voxels within these four background ROI values and for each background ROI separately. ensure that the mean signal intensity of each background ROI is within 1.5 standard deviations of the mean signal of each of the other three counterparts to guarantee a homogeneous signal distribution.

Then, calculate the threshold for pile-up artifacts by adding three standard deviations of the background ROI to the mean signal intensity of all voxels of the four ROI values. Next, perform a semi-automatic threshold based segmentation of pile-up artifacts. Use the masking tool to visualize the predefined signal intensity range and restrict the segmentation to it, selecting all voxels with signal intensities greater than the threshold adjacent to the signal loss artifact.

To define the threshold for signal loss artifacts, first place four ROIs in air containing regions 10 millimeter in diameter at the corners of the phantom box, then measure the mean signal intensity and standard deviation of all voxels within them. Now, place an ROI in the core of the signal loss artifact defined by the largest connected area of low signal intensities. Then manually increase the size of this ROI to the largest possible size as defined on the screen here.

Finally, measure the mean signal intensity and standard deviation of this core ROI. Also, calculate the signal intensity threshold for signal loss artifacts as seen here. Then perform a semi-automatic threshold based segmentation of these artifacts by selecting all voxels connected to the core ROI with signal intensities below this threshold.

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 segmentation editor to include all voxels that are not yet selected. Subtract the physical implant volume from the calculated artifact volume to obtain the true artifact volume, and repeat this analysis at least three times.

A time interval of at least two weeks should separate these multiple reads to exclude any learning bias. Typical positioning of the regions of interest for measuring the thresholds for pile-up artifacts and signal distribution in signal loss artifacts is seen here. The blue contour represents the semi-automatic segmentation for signal loss artifacts.

The small red areas correspond to the result of pile-up artifacts. Dental implants supporting different single crowns used in the phantoms are seen here. These graphs show the mean three-dimensional artifact volume for each implant sample with standard deviations after subtracting the physical implant volume.

Four MRI sequences were evaluated. Here, renderings of the artifact volumes for a cobalt chromium tungsten titanium implant and a zirconia titanium implant are shown. The blue areas represent signal loss artifacts and the red areas represent pile-up artifacts.

Below we see the colored source images for all evaluated T-2 weighted sequences. While attempting this procedure, it's important to choose identical sequence parameters as in the Invivo scan, to allow for accurate transferability of the in vitro results. After watching this video you should have a good understanding of how to evaluate signal loss and pile-up artifacts in a standardized way.

This allows you to assess the MRI suitability of implants, or vulnerability of pulse sequences to metal artifacts.