Dixon Sequence with Superimposed Model-Based Bone Compartment Provides Highly Accurate PET/MR Attenuation Correction of the Brain

Abstract

Simultaneous PET/MR of the brain is a promising new technology for characterizing patients with suspected cognitive impairment or epilepsy. Unlike CT though, MR signal intensities do not provide a direct correlate to PET photon attenuation correction (AC) and inaccurate radiotracer standard uptake value (SUV) estimation could limit future PET/MR clinical applications. We tested a novel AC method that supplements standard Dixon-based tissue segmentation with a superimposed model-based bone compartment. Methods We directly compared SUV estimation for MR-based AC methods to reference CT AC in 16 patients undergoing same-day, single 18FDG dose PET/CT and PET/MR for suspected neurodegeneration. Three Dixon-based MR AC methods were compared to CT – standard Dixon 4-compartment segmentation alone, Dixon with a superimposed model-based bone compartment, and Dixon with a superimposed bone compartment and linear attenuation correction optimized specifically for brain tissue. The brain was segmented using a 3D T1-weighted volumetric MR sequence and SUV estimations compared to CT AC for whole-image, whole-brain and 91 FreeSurfer-based regions-of-interest. Results Modifying the linear AC value specifically for brain and superimposing a model-based bone compartment reduced whole-brain SUV estimation bias of Dixon-based PET/MR AC by 95% compared to reference CT AC (P < 0.05) – this resulted in a residual −0.3% whole-brain mean SUV bias. Further, brain regional analysis demonstrated only 3 frontal lobe regions with SUV estimation bias of 5% or greater (P < 0.05). These biases appeared to correlate with high individual variability in the frontal bone thickness and pneumatization. Conclusion Bone compartment and linear AC modifications result in a highly accurate MR AC method in subjects with suspected neurodegeneration. This prototype MR AC solution appears equivalent than other recently proposed solutions, and does not require additional MR sequences and scan time. These data also suggest exclusively model-based MR AC approaches may be adversely affected by common individual variations in skull anatomy.

Hybrid Imaging: Instrumentation and Data Processing

“…The creation of such parametric images can be divided into [208], (E) Anazodo et al [209], (F) Izquierdo-Garcia et al [210], (G) Burgos et al [211], (H) Merida et al [212]; MLAA-based methods: (I) Benoit et al [213]; Segmentation-based methods: (J) Cabello et al [214], (K) Juttukonda et al [215], (L) Ladefoged et al [216]. Graphical analyses methods include the Gjedde-Patlak equation [251,252], which describes irreversibly bound tracers, such as [ 18 F]FDG and the Logan-plot, which can be used to describe the reversibly bound tracers [253].…”

Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0

“…Thus, MRI-based attenuation correction needs to be accurate and free from artefacts to provide accurate PET quantification.The latest version of MRI attenuation-correction software should be used, including ultrashort echo time (UTE), zero TE (ZTE) sequences, or bone-models for bone detection in brain PET/MRI, where available [16, 17]. …”

Clinical Evaluation of Zero-Echo-Time Attenuation Correction for Brain 18F-FDG PET/MRI: Comparison with Atlas Attenuation Correction

“…These MR-based AC methods are roughly classified into 3 families, as well as combinations thereof. The first family is that of template-/atlas-/model-based approaches (5)(6)(7). The second one is that of segmentation approaches (8)(9)(10)(11)(12).…”

“…However, these methods are not recommended for brain studies, because neglecting bone introduces a significant bias in the area of the cortex (15). In addition, the mMR has a clinical dual ultrashort echo (UTE)-based segmentation method (8) and a prototype modelbased method (7). In turn, the SIGNA has a clinical atlas-based method and a prototype zero-echo-time (ZTE)-based segmentation method (5,16,17).…”