Cone-beam breast computed tomography using ultra-fast image reconstruction with constrained,total-variation minimization for suppression of artifacts |
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| Affiliation: | 1. Department of Medical Imaging, University of Arizona, Tucson, AZ 85724, United States;2. Department of Biomedical Engineering, University of Arizona, Tucson, AZ 85724, United States;1. Dip. Fisica, Sapienza Univ. di Roma, Rome, Italy;2. INFN Sezione di Roma, Rome, Italy;3. SLAC National Accelerator Laboratory, Menlo Park, United States;4. National Center for Radiation Protection and Computational Physics, Istituto Superiore di Sanitá, Italy;5. INFN, Laboratori Nazionali del Sud, Catania, Italy;6. Université Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France;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. Department of Medical Physics, Graduate School of Medicine, Tokyo Women’s Medical University, Shinjuku-ku, Tokyo 162-8666, Japan;2. Particle Therapy Division, Research Center for Innovative Oncology, National Cancer Center, Kashiwa, Chiba 277-8577, Japan;3. Faculty of Mediine, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan;4. Department of Radiation Oncology, Tokyo Women''s Medical University, Shinjuku-ku, Tokyo 162-8666, Japan;1. Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;2. Center for Clinical Research, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan |
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| Abstract: | Compressed sensing based iterative reconstruction algorithms for computed tomography such as adaptive steepest descent-projection on convex sets (ASD-POCS) are attractive due to their applicability in incomplete datasets such as sparse-view data and can reduce radiation dose to the patients while preserving image quality. Although IR algorithms reduce image noise compared to analytical Feldkamp-Davis-Kress (FDK) algorithm, they may generate artifacts, particularly along the periphery of the object. One popular solution is to use finer image-grid followed by down-sampling. This approach is computationally intensive but may be compensated by reducing the field of view. Our proposed solution is to replace the algebraic reconstruction technique within the original ASD-POCS by ordered subsets-simultaneous algebraic reconstruction technique (OS-SART) and with initialization using FDK image. We refer to this method as Fast, Iterative, TV-Regularized, Statistical reconstruction Technique (FIRST). In this study, we investigate FIRST for cone-beam dedicated breast CT with large image matrix. The signal-difference to noise ratio (SDNR), the difference of the mean value and the variance of adipose and fibroglandular tissues for both FDK and FIRST reconstructions were determined. With FDK serving as the reference, the root-mean-square error (RMSE), bias, and the full-width at half-maximum (FWHM) of microcalcifications in two orthogonal directions were also computed. Our results suggest that FIRST is competitive to the finer image-grid method with shorter reconstruction time. Images reconstructed using the FIRST do not exhibit artifacts and outperformed FDK in terms of image noise. This suggests the potential of this approach for radiation dose reduction in cone-beam breast CT. |
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| Keywords: | Breast Computed tomography Breast CT Statistical image reconstruction TV-POCS ASD-POCS Artifacts |
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