A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients

Abstract

Aim: To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use.

Methods: In total, 11 AC methods were evaluated: two vendor-implemented (MR-AC_DIXON and MR-AC_UTE), five based on template/atlas information (MR-AC_SEGBONE (Koesters et al., 2016), MR-AC_ONTARIO (Anazodo et al., 2014), MR-AC_BOSTON (Izquierdo-Garcia et al., 2014), MR-AC_UCL (Burgos et al., 2014), and MR-AC_MAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-AC_MLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-AC_MUNICH (Cabello et al., 2015), MR-AC_CAR-RiDR (Juttukonda et al., 2015), and MR-AC_RESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [¹⁸F]FDG (210), [¹¹C]PiB (51), and [¹⁸F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis.

Results: The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-AC_DIXON (-11.3±3.5)%, MR-AC_UTE (-5.7±2.0)%, MR-AC_ONTARIO (-4.3±3.6)%, MR-AC_MUNICH (3.7±2.1)%, MR-AC_MLAA (-1.9±2.6)%, MR-AC_SEGBONE (-1.7±3.6)%, MR-AC_UCL (0.8±1.2)%, MR-AC_CAR-RiDR (-0.4±1.9)%, MR-AC_MAXPROB (-0.4±1.6)%, MR-AC_BOSTON (-0.3±1.8)%, and MR-AC_RESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-AC_BOSTON, MR-AC_MAXPROB, MR-AC_RESOLUTE and MR-AC_UCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-AC_CAR-RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir.

Conclusions: All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

Keywords: Attenuation correction; Brain; PET/MRI.

Fig. 1.

Attenuation correction images for a sample patient that minimizes the difference of the overall brain error to the median error across all methods. (A) CT, (B) Dixon, (C) UTE, (D) Segbone, (E) Ontario, (F) Boston, (G) UCL, (H) MaxProb, (I) MLAA, (J) Munich, (K) CAR-RiDR, (L) RESOLUTE.

Fig. 2.

PET images for a sample patient that minimizes the difference of the overall brain error to the median error across all methods. (A) CT, (B) Dixon, (C) UTE, (D) Segbone, (E) Ontario, (F) Boston, (G) UCL, (H) MaxProb, (I) MLAA, (J) Munich, (K) CAR-RiDR, (L) RESOLUTE.

Fig. 3.

Global performance of all methods for the FDG patients (n=201, including patients with fat/water tissue inversion). The median (red line), 25th and 75th percentiles (box), 1.5*IQR (whiskers), outliers (red dots), mean and SD are shown for each method. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4.

Summed joint histograms of PET activity within the brain mask for FDG (n=192, excluding patients with fat/water tissue inversion) for PET_CT versus each of the methods. The R² scores are average ± SD of the individual patients. The joint entropy (JE) is calculated for all patients.

Fig. 5.

Global and regional ROI analysis across all FDG patients (n=192, excluding patients with fat/water tissue inversion). The gray lines indicate 1 SD.

Fig. 6.

Averaged Rel_% images across all FDG patients (n=201, including patients with fat/water tissue inversion) for each method: (A) Dixon, (B) UTE, (C) Segbone, (D) Ontario, (E) Boston, (F) UCL, (G) MaxProb, (H) MLAA, (I) Munich, (J) CAR-RiDR, (K) RESOLUTE.

Fig. 7.

Standard deviation images across all FDG Rel_% images (n=201, including patients with fat/water tissue inversion) for each method: (A) Dixon, (B) UTE, (C) Segbone, (D) Ontario, (E) Boston, (F) UCL, (G) MaxProb, (H) MLAA, (I) Munich, (J) CAR-RiDR, (K) RESOLUTE.

Fig. 8.

Outlier analysis for the FDG patients (n=192, excluding patients with fat/water tissue inversion). Note different scale of x-axis.