This study examines the consequences of ignoring metal-implant-induced MR artifacts in the dental region for PET, measured using a combined PET/MR system with standard, vendor-supplied MR attenuation correction. The resulting estimated bias in AC-PET is severe in regions in and near the artifact regions.
Dental artifacts are considered a challenging problem to which no satisfactory solution is yet available [24]. Specialized multispectral MR sequences for imaging near metal [25] help improve the overall quality of the MR images. However, artifacts remain in metal, and the performance of multispectral sequences in the context of AC has not been studied. We note that this type of sequences requires long acquisition times (4 to 10 min has been reported even when using accelerated [26] or off-resonance suppressed schemes [27]), as compared to the 19 s acquisition time for the standard Dixon VIBE.
The effect of ignoring metal artifacts in PET/MR imaging of the head and neck region was studied also by Buchbender [28]. Their study included 19 patients with metal implants, 11 had dental work. The authors measured the SUVMEAN in a volume of interest (VOI) inside all signal voids and found the values to be significantly lower in the artifact region than in the unbiased contralateral reference region (0.05 ± 0.07 versus 0.42 ± 0.22).
So far, 44% of our PET/MR patient population had a metal artifact due to dental implants in the MR-ACDIXON image. Artifacts caused a regional bias in reconstructed activity mainly in the area of the signal void. The effect of the artifact decreased with the distance to the signal void in a non-simple pattern, and biased uptake regions were noticed in regions as far away as the cerebellum. The magnitude of the effect correlated with the actual size of the artifact (Figure 6), and for some artifacts, a complete loss of activity was observed (Figure 4). Of note, such artifacts will be detrimental for the use of PET/MR in a clinical setup where accurate PET measurements are important, such as in the case of response evaluation [15].
Our study included 144 patients and used an automatic algorithm for artifact correction, which is based on a combination of a template of likely artifact areas and a level set segmentation method that delineates the outer contour. The automated correction method was able to substantially reduce the artifacts in 98% of the included patients. The time to correct each attenuation map was about 1 min for the patients in GroupINNER and about 40 min for GroupOUTER using a standard MacBook Pro (Apple Inc., 1 Infinite Loop, Cupertino, CA, USA) and non-optimized code. It is, therefore, feasible to have images ready for reading with a delay less than an hour. This study included patients injected with five different tracers ([18F]-FDG, [18F]-FET, [11C]-PiB, [64Cu]-DOTATATE, and [68Ga]-DOTATOC), so the method appears to be robust to the tracer type. The method only makes use of the NAC-PET image and the Dixon-water image to correct for the artifacts; both are readily available in all PET/MR exams, so the method does not require any extra scan time.
For both patient groups, the change in SUVMEAN following correction was largest near the signal voids (tongue, −3.4% GroupINNER, −30% GroupOUTER), but for GroupINNER the difference decreased in the lower tongue (Table 2 and Figure 5). In regions further away, such as in the cerebellum, no significant differences between the PET values were seen (Figure 4D) as a result of the artifact correction in GroupINNER. In nine of the patients in GroupINNER, \( {\varepsilon}_{\mathrm{mean}}^{\mathrm{rel}} \) was lower than −5% in the lower tongue. This was due to the artifact region partly extending into the area of the ROI, resulting in subregions with large relative differences.
The size of the artifacts of the patients in GroupOUTER increased compared to the artifacts of GroupINNER. The resulting bias in AC-PET was severe in regions in and near the signal voids (Figure 4H). Note the similarity of tracer distribution of Figure 4F,G near and in the signal void suggesting that our method has recovered the PET signal with minimal bias.
Of note, the bias is present also in areas further away from the implants in GroupOUTER (cerebellum, \( {\varepsilon}_{\mathrm{mean}}^{\mathrm{rel}} \) −1.8%, range, −7% to 0%; \( {\varepsilon}_{\max}^{\mathrm{rel}} \) −1.8%, range, −11% to 2.4%). In selected cases, this bias may markedly affect regions used commonly as reference regions in kinetic modeling. In addition, the kinetic modeling will be biased even with artifact correction in place because of the systematic underestimation and radial bias of the tissue activity concentration from standard MR-AC due to the lack of bone [29].
Interestingly, a positive bias is observed in regions near the artifacts (Figure 7). As hypothesized in [30], this effect could be due to scatter correction. However, this was investigated using reconstructions with and without scatter correction, and the same positive bias was observed. We, therefore, conclude that this effect could be related to the impact of an erroneous attenuation correction on the OSEM reconstruction algorithm, a subject that could be further analyzed. The size of the positive bias is dependent on the size of the artifact. The positive bias was mainly present in regions with low tracer uptake. There was a maximum relative change of 35% in the left masticatory muscle in the sample GroupOUTER patient, but the absolute change was only 0.1 SUV in that specific voxel. The positive bias was mainly observed in the ROI placed in the masticatory muscles but was observed also in the lower tongue with a positive bias of 10% in a single patient as this ROI was placed directly in-between a bilateral dental artifact in an area with low tracer uptake (\( {\varepsilon}_{\mathrm{mean}}^{\mathrm{abs}} \) 0.14 SUV).
The metal artifacts are usually less severe in the CT images than in the MR-AC maps, since the number of voxels affected by the metal implant is much larger on MR than the actual implant (Figure 3A,B) [12]. Previous studies by Pauchard [11] have shown MR distortions to be dependent on the size and orientation of the implants with respect to the gradient field, so the overall artifact size cannot be predicted from the knowledge of the size of the implant as such. For example, two CT images from a PET/CT study of the same patient acquired 4 months apart suggest that the amount and location of the metal fillings is unchanged between the examinations since the streak artifacts are similar and the number of fillings is the same (Figure 9). However, the attenuation maps from the PET/MR scan of the same patient at the same two-time points were very different and the size of the susceptibility-induced artifacts varied. This is a problem that can have severe implications for follow-up examinations, which can be solved with our inpainting correction method.
The clinical interpretation of the tracer uptake patterns on AC-PET images is difficult in patients with large metal-induced artifacts. In one patient, the tracer uptake increased 19% after correction in a VOI with 50% isocontour levels defined encompassing an entire tumor, and this tumor was located outside the area spanned by the artifact (Figure 8). A tumor located inside the artifact area could potentially have a largely decreased contrast with possible implications for the clinical reading of the attenuation-corrected PET image as a result of the artifact as well as on the MR sequences.
This study has some limitations. First, the absence of a reference value for the correct attenuation map was addressed by visual inspection of each of the MR-ACINPAINTED maps to ensure that the inpainting was done correctly. Furthermore, we manually corrected the artifacts in four patients from GroupINNER and seven patients from GroupOUTER, and by comparing the MR-AC maps and their resulting AC-PET images, we found that our automatic correction method produced very similar results. However, despite careful inspection of the attenuation maps, it is not possible to determine the accuracy of the reported improvements without the reference data, as residual errors introduced by the inpainting algorithm might affect the PET uptake. Further investigations using phantom data or simulated artifacts are required to assess the absolute bias.
Second, the ROI delineation was done partly on the anatomical MR images, which in some cases had severe signal voids. The correct delineation of the ROIs tongue and lower tongue was, therefore, challenged in 10% of the patients, as we had to draw the ROI on the corrected PETINPAINTED image instead. Third, we did not take the attenuation of bone and metal into account. Thus, the correction method outlined is an improvement of the quantitative measures of tissue activity concentration that will improve the clinical evaluation; but compared to a true AC estimates, there will still be a residual error. A related study where the much larger metal endoprosthesis was included only showed local differences to soft tissue only corrections [12], which leads to the conclusion that the overall effect would be minimal.