Comparison of advanced iterative reconstruction methods for SPECT/CT. 2012

Peter Knoll, and Daniela Kotalova, and Gunnar Köchle, and Ivan Kuzelka, and Greg Minear, and Siroos Mirzaei, and Martin Sámal, and Ladislav Zadrazil, and Helmar Bergmann
Department of Nuclear Medicine and PET Center, Wilhelminenspital, Vienna, Austria.

OBJECTIVE Corrective image reconstruction methods which produce reconstructed images with improved spatial resolution and decreased noise level became recently commercially available. In this work, we tested the performance of three new software packages with reconstruction schemes recommended by the manufacturers using physical phantoms simulating realistic clinical settings. METHODS A specially designed resolution phantom containing three (99m)Tc lines sources and the NEMA NU-2 image quality phantom were acquired on three different SPECT/CT systems (General Electrics Infinia, Philips BrightView and Siemens Symbia T6). Measurement of both phantoms was done with the trunk filled with a (99m)Tc-water solution. The projection data were reconstructed using the GE's Evolution for Bone(®), Philips Astonish(®) and Siemens Flash3D(®) software. The reconstruction parameters employed (number of iterations and subsets, the choice of post-filtering) followed theses recommendations of each vendor. These results were compared with reference reconstructions using the ordered subset expectation maximization (OSEM) reconstruction scheme. RESULTS The best results (smallest value for resolution, highest percent contrast values) for all three packages were found for the scatter corrected data without applying any post-filtering. The advanced reconstruction methods improve the full width at half maximum (FWHM) of the line sources from 11.4 to 9.5mm (GE), from 9.1 to 6.4mm (Philips), and from 12.1 to 8.9 mm (Siemens) if no additional post filter was applied. The total image quality control index measured for a concentration ratio of 8:1 improves for GE from 147 to 189, from 179. to 325 for Philips and from 217 to 320 for Siemens using the reference method for comparison. The same trends can be observed for the 4:1 concentration ratio. The use of a post-filter reduces the background variability approximately by a factor of two, but deteriorates significantly the spatial resolution. CONCLUSIONS Using advanced reconstruction algorithms the largest improvement in image resolution and contrast is found for the scatter corrected slices without applying post-filtering. The user has to choose whether noise reduction by post-filtering or improved image resolution fits better a particular imaging procedure.

UI MeSH Term Description Entries
D007090 Image Interpretation, Computer-Assisted Methods developed to aid in the interpretation of ultrasound, radiographic images, etc., for diagnosis of disease. Image Interpretation, Computer Assisted,Computer-Assisted Image Interpretation,Computer-Assisted Image Interpretations,Image Interpretations, Computer-Assisted,Interpretation, Computer-Assisted Image,Interpretations, Computer-Assisted Image
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D011856 Radiographic Image Enhancement Improvement in the quality of an x-ray image by use of an intensifying screen, tube, or filter and by optimum exposure techniques. Digital processing methods are often employed. Digital Radiography,Image Enhancement, Radiographic,Radiography, Digital,Enhancement, Radiographic Image,Enhancements, Radiographic Image,Image Enhancements, Radiographic,Radiographic Image Enhancements
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D012680 Sensitivity and Specificity Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed) Specificity,Sensitivity,Specificity and Sensitivity
D012984 Software Sequential operating programs and data which instruct the functioning of a digital computer. Computer Programs,Computer Software,Open Source Software,Software Engineering,Software Tools,Computer Applications Software,Computer Programs and Programming,Computer Software Applications,Application, Computer Software,Applications Software, Computer,Applications Softwares, Computer,Applications, Computer Software,Computer Applications Softwares,Computer Program,Computer Software Application,Engineering, Software,Open Source Softwares,Program, Computer,Programs, Computer,Software Application, Computer,Software Applications, Computer,Software Tool,Software, Computer,Software, Computer Applications,Software, Open Source,Softwares, Computer Applications,Softwares, Open Source,Source Software, Open,Source Softwares, Open,Tool, Software,Tools, Software
D014057 Tomography, X-Ray Computed Tomography using x-ray transmission and a computer algorithm to reconstruct the image. CAT Scan, X-Ray,CT Scan, X-Ray,Cine-CT,Computerized Tomography, X-Ray,Electron Beam Computed Tomography,Tomodensitometry,Tomography, Transmission Computed,X-Ray Tomography, Computed,CAT Scan, X Ray,CT X Ray,Computed Tomography, X-Ray,Computed X Ray Tomography,Computerized Tomography, X Ray,Electron Beam Tomography,Tomography, X Ray Computed,Tomography, X-Ray Computer Assisted,Tomography, X-Ray Computerized,Tomography, X-Ray Computerized Axial,Tomography, Xray Computed,X Ray Computerized Tomography,X Ray Tomography, Computed,X-Ray Computer Assisted Tomography,X-Ray Computerized Axial Tomography,Beam Tomography, Electron,CAT Scans, X-Ray,CT Scan, X Ray,CT Scans, X-Ray,CT X Rays,Cine CT,Computed Tomography, Transmission,Computed Tomography, X Ray,Computed Tomography, Xray,Computed X-Ray Tomography,Scan, X-Ray CAT,Scan, X-Ray CT,Scans, X-Ray CAT,Scans, X-Ray CT,Tomographies, Computed X-Ray,Tomography, Computed X-Ray,Tomography, Electron Beam,Tomography, X Ray Computer Assisted,Tomography, X Ray Computerized,Tomography, X Ray Computerized Axial,Transmission Computed Tomography,X Ray Computer Assisted Tomography,X Ray Computerized Axial Tomography,X Ray, CT,X Rays, CT,X-Ray CAT Scan,X-Ray CAT Scans,X-Ray CT Scan,X-Ray CT Scans,X-Ray Computed Tomography,X-Ray Computerized Tomography,Xray Computed Tomography
D016477 Artifacts Any visible result of a procedure which is caused by the procedure itself and not by the entity being analyzed. Common examples include histological structures introduced by tissue processing, radiographic images of structures that are not naturally present in living tissue, and products of chemical reactions that occur during analysis. Artefacts,Artefact,Artifact
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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