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Optimal gating compared to 3D and 4D PET reconstruction for characterization of lung tumours

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      PurposeWe investigated the added value of a new respiratory amplitude-based PET reconstruction method called optimal gating (OG) with the aim of providing accurate image quantification in lung cancer.MethodsFDG-PET imaging was performed in 26 lung cancer patients during free breathing using a 24-min list-mode acquisition on a PET/CT scanner. The data were reconstructed using three methods: standard 3D PET, respiratory-correlated 4D PET using a phase-binning algorithm, and OG. These datasets were compared in terms of the maximum SUV (SUVmax) in the primary tumour (main endpoint), noise characteristics, and volumes using thresholded regions of SUV 2.5 and 40% of the SUVmax.ResultsSUVmax values from the 4D method (13.7 ± 5.6) and the OG method (14.1 ± 6.5) were higher (4.9 ± 4.8%, p < 0.001 and 6.9 ± 8.8%, p < 0.001, respectively) than that from the 3D method (13.1 ± 5.4). SUVmax did not differ between the 4D and OG methods (2.0 ± 8.4%, p = NS). Absolute and relative threshold volumes did not differ between methods, except for the 40% SUVmax volume in which the value from the 3D method was lower than that from the 4D method (−5.3 ± 7.1%, p = 0.007). The OG method exhibited less noise than the 4D method. Variations in volumes and SUVmax of up to 40% and 27%, respectively, of the individual gates of the 4D method were also observed.ConclusionThe maximum SUVs from the OG and 4D methods were comparable and significantly higher than that from the 3D method, yet the OG method was visibly less noisy than the 4D method. Based on the better quantification of the maximum and the less noisy appearance, we conclude that OG PET is a better alternative to both 3D PET, which suffers from breathing averaging, and the noisy images of a 4D PET.

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      Acquiring a four-dimensional computed tomography dataset using an external respiratory signal.

      Four-dimensional (4D) methods strive to achieve highly conformal radiotherapy, particularly for lung and breast tumours, in the presence of respiratory-induced motion of tumours and normal tissues. Four-dimensional radiotherapy accounts for respiratory motion during imaging, planning and radiation delivery, and requires a 4D CT image in which the internal anatomy motion as a function of the respiratory cycle can be quantified. The aims of our research were (a) to develop a method to acquire 4D CT images from a spiral CT scan using an external respiratory signal and (b) to examine the potential utility of 4D CT imaging. A commercially available respiratory motion monitoring system provided an 'external' tracking signal of the patient's breathing. Simultaneous recording of a TTL 'X-Ray ON' signal from the CT scanner indicated the start time of CT image acquisition, thus facilitating time stamping of all subsequent images. An over-sampled spiral CT scan was acquired using a pitch of 0.5 and scanner rotation time of 1.5 s. Each image from such a scan was sorted into an image bin that corresponded with the phase of the respiratory cycle in which the image was acquired. The complete set of such image bins accumulated over a respiratory cycle constitutes a 4D CT dataset. Four-dimensional CT datasets of a mechanical oscillator phantom and a patient undergoing lung radiotherapy were acquired. Motion artefacts were significantly reduced in the images in the 4D CT dataset compared to the three-dimensional (3D) images, for which respiratory motion was not accounted. Accounting for respiratory motion using 4D CT imaging is feasible and yields images with less distortion than 3D images. 4D images also contain respiratory motion information not available in a 3D CT image.
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        Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer.

        PET with (18)F-FDG ((18)F-FDG PET) is increasingly used in the definition of target volumes for radiotherapy, especially in patients with non-small cell lung cancer (NSCLC). In this context, the delineation of tumor contours is crucial and is currently done by different methods. This investigation compared the gross tumor volumes (GTVs) resulting from 4 methods used for this purpose in a set of clinical cases. Data on the primary tumors of 25 patients with NSCLC were analyzed. They had (18)F-FDG PET during initial tumor staging. Thereafter, additional PET of the thorax in treatment position was done, followed by planning CT. CT and PET images were coregistered, and the data were then transferred to the treatment planning system (PS). Sets of 4 GTVs were generated for each case by 4 methods: visually (GTV(vis)), applying a threshold of 40% of the maximum standardized uptake value (SUV(max); GTV(40)), and using an isocontour of SUV = 2.5 around the tumor (GTV(2.5)). By phantom measurements we determined an algorithm, which rendered the best fit comparing PET with CT volumes using tumor and background intensities at the PS. Using this method as the fourth approach, GTV(bg) was defined. A subset of the tumors was clearly delimitable by CT. Here, a GTV(CT) was determined. We found substantial differences between the 4 methods of up to 41% of the GTV(vis). The differences correlated with SUV(max), tumor homogeneity, and lesion size. The volumes increased significantly from GTV(40) (mean 53.6 mL) < GTV(bg) (94.7 mL) < GTV(vis) (157.7 mL) and GTV(2.5) (164.6 mL). In inhomogeneous lesions, GTV(40) led to visually inadequate tumor coverage in 3 of 8 patients, whereas GTV(bg) led to intermediate, more satisfactory volumes. In contrast to all other GTVs, GTV(40) did not correlate with the GTV(CT). The different techniques of tumor contour definition by (18)F-FDG PET in radiotherapy planning lead to substantially different volumes, especially in patients with inhomogeneous tumors. Here, the GTV(40) does not appear to be suitable for target volume delineation. More complex methods, such as system-specific contrast-oriented algorithms for contour definition, should be further evaluated with special respect to patient data.
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          The Netherlands protocol for standardisation and quantification of FDG whole body PET studies in multi-centre trials.

          Several studies have shown the usefulness of positron emission tomography (PET) quantification using standardised uptake values (SUV) for diagnosis and staging, prognosis and response monitoring. Many factors affect SUV, such as patient preparation procedures, scan acquisition, image reconstruction and data analysis settings, and the variability in methodology across centres prohibits exchange of SUV data. Therefore, standardisation of 2-[(18)F] fluoro-2-deoxy-D-glucose (FDG) PET whole body procedures is required in multi-centre trials. A protocol for standardisation of quantitative FDG whole body PET studies in the Netherlands (NL) was defined. This protocol is based on standardisation of: (1) patient preparation; (2) matching of scan statistics by prescribing dosage as function of patient weight, scan time per bed position, percentage of bed overlap and image acquisition mode (2D or 3D); (3) matching of image resolution by prescribing reconstruction settings for each type of scanner; (4) matching of data analysis procedure by defining volume of interest methods and SUV calculations and; (5) finally, a multi-centre QC procedure is defined using a 20-cm diameter phantom for verification of scanner calibration and the NEMA NU 2 2001 Image Quality phantom for verification of activity concentration recoveries (i.e., verification of image resolution and reconstruction convergence). This paper describes a protocol for standardization of quantitative FDG whole body multi-centre PET studies. The protocol was successfully implemented in the Netherlands and has been approved by the Netherlands Society of Nuclear Medicine.

            Author and article information

            [1 ]Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre, Dr. Tanslaan 12, NL-6229 ET Maastricht, The Netherlands
            [2 ]Siemens Medical Solutions, Knoxville, TN 37932 USA
            +31-884455666 , +31-884455667 ,
            Eur J Nucl Med Mol Imaging
            European Journal of Nuclear Medicine and Molecular Imaging
            Springer-Verlag (Berlin/Heidelberg )
            11 January 2011
            11 January 2011
            May 2011
            : 38
            : 5
            : 843-855
            © The Author(s) 2011
            Original Article
            Custom metadata
            © Springer-Verlag 2011

            Radiology & Imaging

            respiratory motion, lung cancer, respiratory gating, pet, suv


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