PURPOSE: To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS: Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS: Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION: The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.
PURPOSE: To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS: Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS: Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION: The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.
Authors: Helmut Ringl; Mathias Lazar; Michael Töpker; Ramona Woitek; Helmut Prosch; Ulrika Asenbaum; Csilla Balassy; Daniel Toth; Michael Weber; Stefan Hajdu; Grzegorz Soza; Andreas Wimmer; Thomas Mang Journal: Eur Radiol Date: 2015-02-14 Impact factor: 5.315
Authors: Martin Kolopp; Nicolas Douis; Ayla Urbaneja; Cédric Baumann; Pedro Augusto Gondim Teixeira; Alain Blum; Laurent Martrille Journal: Int J Legal Med Date: 2019-11-16 Impact factor: 2.686
Authors: Georg Bier; Deedar Farhad Mustafa; Christopher Kloth; Katja Weisel; Hendrik Ditt; Konstantin Nikolaou; Marius Horger Journal: AJR Am J Roentgenol Date: 2016-01 Impact factor: 3.959
Authors: Simon Lennartz; Nils Große Hokamp; Charlotte Zäske; David Zopfs; Grischa Bratke; Andreas Glauner; David Maintz; Thorsten Persigehl; De-Hua Chang; Tilman Hickethier Journal: Br J Radiol Date: 2020-07-24 Impact factor: 3.039
Authors: Amit Gupta; Verena Carola Obmann; Michelle Jordan; Simon Lennartz; Markus Michael Obmann; Nils Große Hokamp; David Zopfs; Lenhard Pennig; Gina Fürtjes; Nikhil Ramaiya; Robert Gilkeson; Kai Roman Laukamp Journal: Quant Imaging Med Surg Date: 2021-01