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

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          Abstract

          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.

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          Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy.

          P. Aljabar, R A Heckemann, A Hammers … (2009)
          Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.
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            MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences.

            Stefaan Vandenberghe, M Lonneux, Yves Fierens … (2010)
            One of the challenges in PET/MRI is the derivation of an attenuation map to correct the PET image for attenuation. Different methods have been suggested for deriving the attenuation map from an MR image. Because the low signal intensity of cortical bone on images acquired with conventional MRI sequences makes it difficult to detect this tissue type, these methods rely on some sort of anatomic precondition to predict the attenuation map, raising the question of whether these methods will be usable in the clinic when patients may exhibit anatomic abnormalities. We propose the use of the transverse relaxation rate, derived from images acquired with an ultrashort echo time sequence to classify the voxels into 1 of 3 tissue classes (bone, soft tissue, or air), without making any assumptions on patient anatomy. Each voxel is assigned a linear attenuation coefficient corresponding to its tissue class. A reference CT scan is used to determine the voxel-by-voxel accuracy of the proposed method. The overall accuracy of the MRI-based attenuation correction is evaluated using a method that takes into account the nonlocal effects of attenuation correction. As a proof of concept, the head of a pig was used as a phantom for imaging. The new method yielded a correct tissue classification in 90% of the voxels. Five human brain PET/CT and MRI datasets were also processed, yielding slightly worse voxel-by-voxel performance, compared to a CT-derived attenuation map. The PET datasets were reconstructed using the segmented MRI attenuation map derived with the new method, and the resulting images were compared with segmented CT-based attenuation correction. An average error of around 5% was found in the brain. The feasibility of using the transverse relaxation rate map derived from ultrashort echo time MR images for the estimation of the attenuation map was shown on phantom and clinical brain data. The results indicate that the new method, compared with CT-based attenuation correction, yields clinically acceptable errors. The proposed method does not make any assumptions about patient anatomy and could therefore also be used in cases in which anatomic abnormalities are present.
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              MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration.

              Bernd J. Pichler, Verena Scheel, Jason Farquhar … (2008)
              For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating radionuclide source, or from the CT scan in a combined PET/CT scanner. In the case of PET/MRI scanners currently under development, insufficient space for the rotating source exists; the attenuation map can be calculated from the MR image instead. This task is challenging because MR intensities correlate with proton densities and tissue-relaxation properties, rather than with attenuation-related mass density. We used a combination of local pattern recognition and atlas registration, which captures global variation of anatomy, to predict pseudo-CT images from a given MR image. These pseudo-CT images were then used for attenuation correction, as the process would be performed in a PET/CT scanner. For human brain scans, we show on a database of 17 MR/CT image pairs that our method reliably enables estimation of a pseudo-CT image from the MR image alone. On additional datasets of MRI/PET/CT triplets of human brain scans, we compare MRI-based attenuation correction with CT-based correction. Our approach enables PET quantification with a mean error of 3.2% for predefined regions of interest, which we found to be clinically not significant. However, our method is not specific to brain imaging, and we show promising initial results on 1 whole-body animal dataset. This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Neuroimage
                Title: NeuroImage
                Publisher: Elsevier BV
                ISSN (Electronic): 1095-9572
                ISSN (Print): 1053-8119
                Publication date (Electronic): Feb 15 2017
                Volume: 147
                Affiliations
                [1 ] Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Copenhagen, Denmark.
                [2 ] Lawson Health Research Institute, London, ON, Canada.
                [3 ] Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
                [4 ] Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2HE, London, UK.
                [5 ] Translational Imaging Group, Centre for Medical Image Computing, University College London, NW1 2HE, London, UK; Dementia Research Centre, Institute of Neurology, University College London, WC1N 3AR, London, UK.
                [6 ] Institute of Nuclear Medicine, University College London, London, UK.
                [7 ] LILI-EQUIPEX - Lyon Integrated Life Imaging: hybrid MR-PET, CERMEP Imaging Centre, Lyon, France; Siemens Healthcare France SAS, Saint-Denis, France.
                [8 ] LILI-EQUIPEX - Lyon Integrated Life Imaging: hybrid MR-PET, CERMEP Imaging Centre, Lyon, France.
                [9 ] LILI-EQUIPEX - Lyon Integrated Life Imaging: hybrid MR-PET, CERMEP Imaging Centre, Lyon, France; King's College London & Guy's and St Thomas' PET Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
                [10 ] Joint Department of Biomedical Engineering, The University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27599, USA.
                [11 ] Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universitaet Muenchen, Munich, Germany.
                [12 ] Siemens Healthcare GmbH, Erlangen, Germany.
                [13 ] Siemens Healthcare GmbH, Erlangen, Germany; University of Surrey, Guildford, Surrey, UK.
                [14 ] Siemens Medical Solutions USA, Inc., Knoxville, TN, USA.
                [15 ] Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63130, USA.
                [16 ] Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Copenhagen, Denmark. Electronic address: fling@rh.dk.
                Article
                Publisher Item ID: S1053-8119(16)30717-0
                DOI: 10.1016/j.neuroimage.2016.12.010
                PubMed ID: 27988322
                SO-VID: aee776e4-9228-4f12-95b4-fed3cd1c6cae
                History

                Keywords: Attenuation correction,Brain,PET/MRI
                Data availability:
                Keywords: Attenuation correction, Brain, PET/MRI

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