Nikolai V Slavine1, Padmakar V Kulkarni2, Roderick W McColl3. 1. Translational Research Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, USA. 2. Pre-Clinical Imaging Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, USA. 3. Clinical Medical Physics Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas, USA.
Abstract
PURPOSE: To test and evaluate an efficient iterative image processing strategy to improve the quality of sub-optimal pre-clinical PET images. A novel iterative resolution subsets-based method to reduce noise and enhance resolution (RSEMD) has been demonstrated on examples of PET imaging studies of Alzheimer's disease (AD) plaques deposition in mice brains. MATERIALS AND METHODS: The RSEMD method was applied to imaging studies of non-invasive detection of beta-amyloid plaque in transgenic mouse models of AD. Data acquisition utilized a Siemens Inveon® micro PET/CT device. Quantitative uptake of the tracer in control and AD mice brains was determined by counting the extent of plaque deposition by histological staining. The pre-clinical imaging software inviCRO® was used for fitting the recovery PET images to the mouse brain atlas and obtaining the time activity curves (TAC) from different brain areas. RESULTS: In all of the AD studies the post-processed images proved to have higher resolution and lower noise as compared with images reconstructed by conventional OSEM method. In general, the values of SNR reached a plateau at around 10 iterations with an improvement factor of about 2 over sub-optimal PET brain images. CONCLUSIONS: A rapidly converging, iterative deconvolution image processing algorithm with a resolution subsets-based approach RSEMD has been used for quantitative studies of changes in Alzheimer's pathology over time. The RSEMD method can be applied to sub-optimal clinical PET brain images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of AD progression at the earliest stages.
PURPOSE: To test and evaluate an efficient iterative image processing strategy to improve the quality of sub-optimal pre-clinical PET images. A novel iterative resolution subsets-based method to reduce noise and enhance resolution (RSEMD) has been demonstrated on examples of PET imaging studies of Alzheimer's disease (AD) plaques deposition in mice brains. MATERIALS AND METHODS: The RSEMD method was applied to imaging studies of non-invasive detection of beta-amyloid plaque in transgenicmouse models of AD. Data acquisition utilized a Siemens Inveon® micro PET/CT device. Quantitative uptake of the tracer in control and ADmice brains was determined by counting the extent of plaque deposition by histological staining. The pre-clinical imaging software inviCRO® was used for fitting the recovery PET images to the mouse brain atlas and obtaining the time activity curves (TAC) from different brain areas. RESULTS: In all of the AD studies the post-processed images proved to have higher resolution and lower noise as compared with images reconstructed by conventional OSEM method. In general, the values of SNR reached a plateau at around 10 iterations with an improvement factor of about 2 over sub-optimal PET brain images. CONCLUSIONS: A rapidly converging, iterative deconvolution image processing algorithm with a resolution subsets-based approach RSEMD has been used for quantitative studies of changes in Alzheimer's pathology over time. The RSEMD method can be applied to sub-optimal clinical PET brain images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of AD progression at the earliest stages.
Authors: Leon J Thal; Kejal Kantarci; Eric M Reiman; William E Klunk; Michael W Weiner; Henrik Zetterberg; Douglas Galasko; Domenico Praticò; Sue Griffin; Dale Schenk; Eric Siemers Journal: Alzheimer Dis Assoc Disord Date: 2006 Jan-Mar Impact factor: 2.703
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