Dynamic PET reconstruction using wavelet regularization with adapted basis functions

Article Article scientifique Article Dynamic PET reconstruction using wavelet regularization with adapted basis functions eng 2008 author Verhaeghe Jeroen author Van De Ville Dimitri 0000-0002-2879-3861 638484 author Khalidov Ildar 640925 author D'Asseler Yves author Lemahieu Ignace author Unser Michael 943-959 10.1109/TMI.2008.923698 18599400 0278-0062 http://ieeexplore.ieee.org/ielx5/42/4550617/04492756.pdf?tp=&arnumber=4492756&isnumber=4550617 Tomographic reconstruction from positron emission tomography (PET) data is an ill-posed problem that requires regularization. An attractive approach is to impose an l(1) -regularization constraint, which favors sparse solutions in the wavelet domain. This can be achieved quite efficiently thanks to the iterative algorithm developed by Daubechies et al., 2004. In this paper, we apply this technique and extend it for the reconstruction of dynamic (spatio-temporal) PET data. Moreover, instead of using classical wavelets in the temporal dimension, we introduce exponential-spline wavelets (E-spline wavelets) that are specially tailored to model time activity curves (TACs) in PET. We show that the exponential-spline wavelets naturally arise from the compartmental description of the dynamics of the tracer distribution. We address the issue of the selection of the "optimal" E-spline parameters (poles and zeros) and we investigate their effect on reconstruction quality. We demonstrate the usefulness of spatio-temporal regularization and the superior performance of E-spline wavelets over conventional Battle-LemariE wavelets in a series of experiments: the 1-D fitting of TACs, and the tomographic reconstruction of both simulated and clinical data. We find that the E-spline wavelets outperform the conventional wavelets in terms of the reconstructed signal-to-noise ratio (SNR) and the sparsity of the wavelet coefficients. Based on our simulations, we conclude that replacing the conventional wavelets with E-spline wavelets leads to equal reconstruction quality for a 40% reduction of detected coincidences, meaning an improved image quality for the same number of counts or equivalently a reduced exposure to the patient for the same image quality. Algorithms Artificial Intelligence Cluster Analysis Computer Simulation Data Interpretation, Statistical Feedback Fourier Analysis Humans Imaging, Three-Dimensional/*methods Information Storage and Retrieval/methods Kidney/radionuclide imaging Liver/radionuclide imaging Pattern Recognition, Automated Positron-Emission Tomography/*methods *Signal Processing, Computer-Assisted Time Factors Dewey 616.0757 62 Département de radiologie et informatique médicale 227 false 552 Traitement d'images médicales 893 IEEE transactions on medical imaging 27 7 1038f941-565a-473e-b249-f96a72c10b75 Article Restricted file1.pdf 2314649 application/pdf restrictedAccess Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale

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Dynamic PET reconstruction using wavelet regularization with adapted basis functions	IEEE transactions on medical imaging	2008	659	0

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Khalidov, Ildar