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

Abstract

a b s t r a c tQuantitative whole-body PET/MR imaging is challenged by the lack of accurate and robust strategies for attenuation correction. In this work, a new pseudo-CT generation approach, referred to as sorted atlas pseudo-CT (SAP), is proposed for accurate extraction of bones and estimation of lung attenuation properties. This approach improves the Gaussian process regression (GPR) kernel proposed by Hofmann et al. which relies on the information provided by a co-registered atlas (CT and MRI) using a GPR kernel to predict the distribution of attenuation coefficients. Our approach uses two separate GPR kernels for lung and non-lung tissues. For non-lung tissues, the co-registered atlas dataset was sorted on the basis of local normalized cross-correlation similarity to the target MR image to select the most similar image in the atlas for each voxel. For lung tissue, the lung volume was incorporated in the GPR kernel taking advantage of the correlation between lung volume and corresponding attenuation properties to predict the attenuation coefficients of the lung. In the presence of pathological tissues in the lungs, the lesions are segmented on PET images corrected for attenuation using MRI-derived three-class attenuation map followed by assignment of soft-tissue attenuation coefficient. The proposed algorithm was compared to other techniques reported in the literature including Hofmann's approach and the three-class attenuation correction technique implemented on the Philips Ingenuity TF PET/MR where CT-based attenuation correction served as reference. Fourteen patients with head and neck cancer undergoing PET/CT and PET/MR examinations were used for quantitative analysis. SUV measurements were performed on 12 normal uptake regions as well as high uptake malignant regions. Moreover, a number of similarity measures were used to evaluate the accuracy of extracted bones. The Dice similarity metric revealed that the extracted bone improved from 0.58 ± 0.09 to 0.65 ± 0.07 when using the SAP technique compared to Hofmann's approach. This enabled to reduce the SUV mean bias in bony structures for the SAP approach to -1.7 ± 4.8% as compared to -7.3 ± 6.0% and -27.4 ± 10.1% when using Hofmann's approach and the three-class attenuation map, respectively. Likewise, the three-class attenuation map produces a relative absolute error of 21.7 ± 11.8% in the lungs. This was reduced on average to 15.8 ± 8.6% and 8.0 ± 3.8% when using Hofmann's and SAP techniques, respectively. The SAP technique resulted in better overall PET quantification accuracy than both Hofmann's and the three-class approaches owing to the more accurate extraction of bones and better prediction of lung attenuation coefficients. Further improvement of the technique and reduction of the computational time are still required.

Comparison of atlas-based techniques for whole-body bone segmentation

“…Basically two categories have emerged: atlas-guided attenuation map generation approaches ( Hofmann et al, 2011 ;Bezrukov et al, 2013 ;Arabi and Zaidi, 2016a ;Marshall et al, 2013 ;Arabi and Zaidi, 2016b ) and emission-based approaches ( Rezaei et al, 2012 ;Mehranian and Zaidi, 2015 ). Atlas-guided methods primarily rely on prior information provided by registration of an atlas into target image coordinates to allow classification of bone tissues.…”

“…Various strategies were proposed to incorporate bone tissue in PET/MRI attenuation maps in whole-body imaging ( Hofmann et al, 2011 ;Bezrukov et al, 2015 ;Ay et al, 2014 ;Arabi and Zaidi, 2016a ;Bezrukov et al, 2013 ;Marshall et al, 2013 ;Paulus et al, 2015 ). In whole-body imaging, almost all proposed methods, except joint attenuation-activity reconstruction techniques, rely on prior knowledge present in atlas images to predict bone from MRI.…”

Whole‐body bone segmentation from MRI for PET/MRI attenuation correction using shape‐based averaging

“…The study population comprised 21 patients who underwent whole-body 18 F-FDG PET/CT and MRI scanning for staging of head and neck malignancies. The study protocol was approved by the institutional ethics committee and the patients signed written informed consent.…”

“…CT images were acquired for attenuation correction using an unenhanced low dose CT scan (120 kVp, 60 mAs, 24 × 1.5 collimation, pitch of 1.2 and 1 s/rotation) after a localization scout scan. PET data acquisition started at 146.2 ± 20 min post-injection of 18 F-FDG (371 ±23 MBq) with 3 min per bed position for a total of 4-5 beds, resulting in a total acquisition time of 15-18 min. MRI examinations were performed on the Ingenuity TF PET/MR (Philips Healthcare, Cleveland, USA) 23 using a whole-body MRI Dixon volumetric interpolated T1-weighted sequence 24 using the following parameters: flip angle 10˚, TE 1 1.1 ms, TE 2 3 voxel size, and a total acquisition time of 2-3 min.…”

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

“…The study found that both atlas/machine learning methods had reduced SUV mean error in the lungs, compared with three class MRAC. However, computation time was long per target image (1100 min) owing to the multiple registrations and Gaussian process regression training …”