Felix Feldhaus1, Georg Böning2, Martin Jonczyk3, Johannes Kahn4, Uli Fehrenbach5, M Maurer6, D Renz7, Bernd Hamm8, Florian Streitparth9. 1. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: felix.feldhaus@charite.de. 2. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: georg.boening@charite.de. 3. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: martin.jonczyk@charite.de. 4. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: johannes.kahn@charite.de. 5. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: uli.fehrenbach@charite.de. 6. Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, CH-3010 Bern, Switzerland. Electronic address: Martin.Maurer@insel.ch. 7. Department of Radiology, University of Jena, Am Klinikum 1, 07747, Germany. Electronic address: diane.renz@med.uni-jena.de. 8. Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Radiology, Augustenburger Platz 1, 13353 Berlin, Germany. Electronic address: bernd.hamm@charite.de. 9. Department of Radiology, Ludwig-Maximilians-University, Marchioninistr. 15, 81377 München, Germany. Electronic address: florian.streitparth@med.uni-muenchen.de.
Abstract
PURPOSE: We determined whether the Smart MAR metal artifact reduction tool - a three-stage, projection-based, post processing algorithm - improves subjective and objective image quality and diagnostic confidence in patients with dental artifacts and suspected head and neck pathology compared to standard adaptive statistical iterative reconstructions (ASIR V) alone. METHOD: The study included 100 consecutive patients with nonremovable oral implants or dental fillings and suspected oropharyngeal cancer or abscess. CT raw data of a single-source multislice CT scanner were postprocessed using ASIR V alone and with additional Smart MAR reconstruction. Image quality of baseline ASIR V and Smart MAR-based reconstruction series was compared both quantitatively (5 regions of interest, ROIs) and qualitatively (two independent raters). RESULTS: Additional Smart MAR reconstruction significantly seems to improve both attenuation and noise adjacent to implants and in more distant areas (all p < 0.001) compared to standard ASIR V reconstructions alone. Signal-to-noise ratio (SNR; p = 0.001) and contrast-to-noise ratio were improved significantly (CNR; p = 0.001). Smart MAR improved visualization of tumor/abscess (detected in 36 of 100 patients, 36%) and representative oropharyngeal tissue (p < 0.001). In 8 of 36 patients (22%), tumor was only detected in Smart MAR series. Mean total DLP was 506.8mGy*cm; average CTDIvol was 5.5 mGy. CONCLUSIONS: The supplementary use of the Smart MAR post-processing tool seems to significantly improve both subjective and objective image quality as well as diagnostic confidence and lesion detection in CT of the head and neck. In 22% of cases, the tumor was detected only in Smart MAR reconstructed images.
PURPOSE: We determined whether the Smart MAR metal artifact reduction tool - a three-stage, projection-based, post processing algorithm - improves subjective and objective image quality and diagnostic confidence in patients with dental artifacts and suspected head and neck pathology compared to standard adaptive statistical iterative reconstructions (ASIR V) alone. METHOD: The study included 100 consecutive patients with nonremovable oral implants or dental fillings and suspected oropharyngeal cancer or abscess. CT raw data of a single-source multislice CT scanner were postprocessed using ASIR V alone and with additional Smart MAR reconstruction. Image quality of baseline ASIR V and Smart MAR-based reconstruction series was compared both quantitatively (5 regions of interest, ROIs) and qualitatively (two independent raters). RESULTS: Additional Smart MAR reconstruction significantly seems to improve both attenuation and noise adjacent to implants and in more distant areas (all p < 0.001) compared to standard ASIR V reconstructions alone. Signal-to-noise ratio (SNR; p = 0.001) and contrast-to-noise ratio were improved significantly (CNR; p = 0.001). Smart MAR improved visualization of tumor/abscess (detected in 36 of 100 patients, 36%) and representative oropharyngeal tissue (p < 0.001). In 8 of 36 patients (22%), tumor was only detected in Smart MAR series. Mean total DLP was 506.8mGy*cm; average CTDIvol was 5.5 mGy. CONCLUSIONS: The supplementary use of the Smart MAR post-processing tool seems to significantly improve both subjective and objective image quality as well as diagnostic confidence and lesion detection in CT of the head and neck. In 22% of cases, the tumor was detected only in Smart MAR reconstructed images.