Resolution and noise performance of sparse view X-ray CT reconstruction via Lp-norm regularization |
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| Institution: | 1. School of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, People’s Republic of China;2. Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin, People’s Republic of China;3. Tianjin Medical University Cancer Institute and Hospital, Tianjin, People’s Republic of China;4. Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People’s Republic of China;1. Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Service de Recherche en Dosimétrie, Laboratoire de Dosimétrie des Rayonnements Ionisants, Fontenay-aux-Roses, France;2. Université de Bourgogne-Franche-Comté, Montbéliard, France;1. Radiation Safety and Medical Physics Department, University Clinical Centre Sarajevo, Bolnicka 25, 71000 Sarajevo, Bosnia and Herzegovina;2. Division of Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, 221 85 Lund, Sweden;3. Dosimetry Laboratory, Dosimetry and Medical Radiation Physics Section, International Atomic Energy Agency, Friedenstrasse 1, Seibersdorf, Austria;1. College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China;2. Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin 300072, China;3. School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China;1. Medical Physics Service, Hospital Clínico San Carlos and IdISSC, 28040 Madrid, Spain;2. Radiology Department, Medicine Faculty, Universidad Complutense de Madrid, 28040 Madrid, Spain;3. Philips Healthcare, Best, The Netherlands |
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| Abstract: | ObjectivesAdaptive steepest descent projection onto convex sets (ASD-POCS) algorithms with Lp-norm (0 < p ≤ 1) regularization have shown great promise in sparse-view X-ray CT reconstruction. However, the difference in p value selection can lead to varying algorithm performance of noise and resolution. Therefore, it is imperative to develop a reliable method to evaluate the resolution and noise properties of the ASD-POCS algorithms under different Lp-norm priors.MethodsA comparative performance evaluation of ASD-POCS algorithms under different Lp-norm (0 < p ≤ 2) priors were performed in terms of modulation transfer function (MTF), noise power spectrum (NPS) and noise equivalent quanta (NEQ). Simulation data sets from the EGSnrc/BEAMnrc Monte Carlo system and an actual mouse data set were used for algorithms comparison.ResultsA considerable MTF improvement can be achieved with the decrement of p. L1 regularization based algorithm obtains the best noise performance, and shows superiority in NEQ evaluation. The advantage of L1-norm prior is also confirmed by the reconstructions from the actual mouse data set through contrast to noise ratio (CNR) comparison.ConclusionAlthough the ASD-POCS algorithms using small Lp-norm (p ≤ 0.5) priors yield a higher MTF than do the high Lp-norms, the best noise-resolution performance is achieved when p is between 0.8 and 1. The results are expected to be a reference to the choice of p in Lp-norm (0 < p ≤ 2) regularization. |
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| Keywords: | Sparse view Modulation transfer function (MTF) Noise power spectrum (NPS) Noise equivalent quanta (NEQ) |
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