Comparison of MR-based attenuation correction and CT-based attenuation correction of whole-body PET/MR imaging

Abstract

Purpose The objective of this paper was to evaluate the performance of the built-in MR-based attenuation correction method (MRAC) included in the combined whole-body Ingenuity TF PET/MR scanner and compare it to the gold standard CT-based attenuation correction (CTAC). Methods Twenty-six patients undergoing clinical whole body FDG-PET/CT imaging were subsequently scanned on the PET/MR (mean delay 100min). Patients were separated in two groups: alpha group (N=14) with no MR coils during PET/MR imaging and beta group (N=12) including MR coils (Neuro-Vascular, Spine, Cardiac or Torso coils). All images were coregistered to the same space (PET/MR). Voxel- and region-based (10 regions of interest, ROIs) comparisons were made between the 2 PET images from PET/MR: using MRAC and CTAC. Additional comparison of lesions performed by an experienced clinician was also reported. Results Body mass index (BMI) and lung density showed significant differences between alpha and beta groups. Right vs. left lung density was also significantly different within each group. Overall the beta group (with coils) presented higher MRAC PET values than the alpha group when compared to the CTAC (alpha: −0.2±33.6%, R2=0.98, p<0.001, beta: 10.31±69.86%, R2=0.97, p<0.001). Conclusion In comparison to CTAC, PET values with MRAC method were underestimated by less than 10% on average, although some ROIs and lesions do differ by more (such as spine, lung or heart). The beta group (with coils) showed increased overall PET quantification as well as increased variability when compared to the alpha group (no coils). PET reconstructed with MRAC showed some differences when compared to PET reconstructed with CTAC, mostly due to air pockets, metallic implants and attenuation differences in big bone areas (such as pelvis and spine) due to the segmentation limitation of the MRAC method.

MR-based attenuation correction for cardiac FDG PET on a hybrid PET/MRI scanner: comparison with standard CT attenuation correction

“…Neglecting bone tissue, however, may result in an underestimation of tissue attenuation which may be of particular concern for PET imaging of the heart which is encompassed by the rib cage and lies adjacent to the spinal column. Several studies have shown that in PET/MRI, application of MRderived AC has led to a substantial underestimation of uptake of 18 F-labelled fluorodeoxyglucose (FDG) into metastases compared to PET/CT [7,8]. A recent publication briefly assessed this issue by assessing absolute cardiac FDG uptake through regional measurement of the maximum standardized uptake value (SUV max ) suggesting that AC derived from MRI and CT may be interchangeable [9].…”

Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach

“…As a consequence, large SUV bias and substantial patient to patient activity recovery variations were reported in the literature ( Hofmann et al, 2011 ). Izquierdo-Garcia et al reported more than 20% underestimation of SUV in the lung region and noticeable lung density variation from patient to patient (even from left to right lung in the same patient) ( Izquierdo-Garcia et al, 2014 ). To address this issue, Marshall et al used linear regression to correlate the intensity of the lungs in specific MR sequences (T2 weighted and extrapolated proton density images) and corresponding CT values ( Marshall et al, 2012 ).…”

“…Owing to the lack of a direct correspondence between MRI intensities and electron densities, alternative methods are sought for MRI-guided attenuation correction in PET/MRI. The strategies proposed for attenuation correction in PET/MRI can be classified into three major categories: tissue segmentation ( Martinez-Moller et al, 2009;Schulz et al, 2011;, template or atlas-based machine learning approaches ( Burgos et al, 2014;Hofmann et al, 2011;Hofmann et al, 2008;Izquierdo-Garcia et al, 2014;Johansson et al, 2011 ), and joint estimation of emission and attenuation ( Defrise et al, 2012;Mehranian and Zaidi, 2015c ). Early attempts to estimate the attenuation map from non-time of flight (TOF) emission data ( Panin et al, 2004 ) achieved limited success.…”

Generation of brain pseudo‐CTs using an undersampled, single‐acquisition UTE‐mDixon pulse sequence and unsupervised clustering

“…[5][6][7][8][9][10][11] Similar errors and issues have been reported for body imaging, with the largest number of errors occurring within and adjacent to bone tissue. [10][11][12][13][14][15] These results are not surprising as at least four tissue types, including bone, are required in order to achieve accurate SUVs, 5,16 whereas current commercial PET/MR attenuation correction (MR-AC) solutions use a three-or four-class segmentation that neglect bone. 13 The three-class segmentation method identifies air (external to the patient) and lungs and classifies the rest of the patient as soft tissue.…”