Incorporation of local dependent reliability information into the Prior Image Constrained Compressed Sensing (PICCS) reconstruction algorithm |
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| Institution: | 1. Department of Radiation Oncology, University of Würzburg, Josef-Schneider-Str. 11, 97080 Würzburg, Germany;2. Department of Experimental Radiation Oncology, University Medical Center Mannheim, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany;1. Unit of Medical Physics, European Institute of Oncology, Milano, Italy;2. Department of Radiation Oncology, European Institute of Oncology, Milano, Italy;3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy;4. Department of Health Sciences, Università degli Studi di Milano, Milano, Italy;1. Medical Physics Department, University Hospital “Maggiore della Carità”, Novara, Italy;2. Medical Physics Department, Ospedale S.Andrea, Asl5 Spezzino, La Spezia, Italy;3. Nuclear Medicine Department, Ospedale S.Andrea, Asl5 Spezzino, La Spezia, Italy;4. Nuclear Medicine Department, University Hospital, “Maggiore della Carità”, Novara, Italy;1. Department of Nuclear Medicine, Faculty of Medicine, University of Debrecen, Hungary;2. Department of Biomedical Laboratory and Imaging Science, Faculty of Medicine, University of Debrecen, Hungary;3. Rotating Gamma Ltd., Hungary;4. Department of Neurosurgery, Medical Center, University of Debrecen, Hungary;5. ScanoMed Ltd., Hungary;1. Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany;2. Medical Research Centre, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany;3. Development and Stem Cells Program, Monash Biomedicine Discovery Institute, and Department of Anatomy and Developmental Biology, Monash University, Melbourne, Victoria, Australia;1. Experimentelle Ophthalmologie, Universität des Saarlandes, Kirrberger Straße 100, Gebäude 22, 66424 Homburg/Saar;2. Klinik für Augenheilkunde, Universitätsklinikum des Saarlandes, Kirrberger Straße 100, Gebäude 22, 66424 Homburg/Saar;3. Klinik für Augenheilkunde, Semmelweis Universität, Mária utca 39, 1085 Budapest, Ungarn |
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| Abstract: | PurposeThe reduction of dose in cone beam computer tomography (CBCT) arises from the decrease of the tube current for each projection as well as from the reduction of the number of projections. In order to maintain good image quality, sophisticated image reconstruction techniques are required. The Prior Image Constrained Compressed Sensing (PICCS) incorporates prior images into the reconstruction algorithm and outperforms the widespread used Feldkamp-Davis-Kress-algorithm (FDK) when the number of projections is reduced. However, prior images that contain major variations are not appropriately considered so far in PICCS. We therefore propose the partial-PICCS (pPICCS) algorithm. This framework is a problem-specific extension of PICCS and enables the incorporation of the reliability of the prior images additionally.Material and MethodsWe assumed that the prior images are composed of areas with large and small deviations. Accordingly, a weighting matrix considered the assigned areas in the objective function. We applied our algorithm to the problem of image reconstruction from few views by simulations with a computer phantom as well as on clinical CBCT projections from a head-and-neck case. All prior images contained large local variations. The reconstructed images were compared to the reconstruction results by the FDK-algorithm, by Compressed Sensing (CS) and by PICCS. To show the gain of image quality we compared image details with the reference image and used quantitative metrics (root-mean-square error (RMSE), contrast-to-noise-ratio (CNR)).ResultsThe pPICCS reconstruction framework yield images with substantially improved quality even when the number of projections was very small. The images contained less streaking, blurring and inaccurately reconstructed structures compared to the images reconstructed by FDK, CS and conventional PICCS. The increased image quality is also reflected in large RMSE differences.ConclusionsWe proposed a modification of the original PICCS algorithm. The pPICCS algorithm incorporates prior images as well as information about location dependent uncertainties of the prior images into the algorithm. The computer phantom and experimental data studies indicate the potential to lowering the radiation dose to the patient due to imaging while maintaining good image quality. |
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| Keywords: | CBCT image reconstruction prior information Compressed Sensing Kegelstrahl-CT Bildrekonstruktion Vorwissen Compressed Sensing |
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