Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs.
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http://dx.doi.org/10.3390/diagnostics12051140 | DOI Listing |
Invest Radiol
January 2025
From the Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany (Y.C.L., N.M., P.A.K., A.I., T.D., J.A.L., D.K.); and Siemens Healthineers AG, Erlangen, Germany (S.F., V.H., B.S.).
Objectives: The aim of this study was to assess the impact of an iterative metal artifact reduction (iMAR) algorithm combined with virtual monoenergetic images (VMIs) for artifact reduction in photon-counting detector computed tomography (PCDCT) during interventions.
Materials And Methods: Using an abdominal phantom, we conducted evaluations on the efficacy of iMAR and VMIs for mitigating image artifacts during interventions on a PCDCT. Four different puncture devices were employed under 2 scan modes (QuantumSn at 100 kV, Quantumplus at 140 kV) to simulate various clinical scenarios.
PLoS One
September 2024
Institut Sénégalais de Recherches Agricoles, ISRA, Centre de Recherches Océanographiques de Dakar Thiaroye, CRODT, Dakar, Senegal.
The Canary Current Large Marine Ecosystem (CCLME) is one of the most productive Large Marine Ecosystems worldwide. Assessing the abundance, biomass and distribution of zooplankton in the southern part of this system, off the coast of West Africa, remains challenging due to limited sampling efforts and data availability. However, zooplankton is of primary importance for pelagic ecosystem functioning.
View Article and Find Full Text PDFQuant Imaging Med Surg
July 2024
Department of Radiology, Liuzhou Municipal Liutie Central Hospital, Liuzhou, China.
Background: Dual-energy computed tomography (DECT) and iterative metal artifact reduction (iMAR) algorithms are valuable tools for reducing metal artifacts. Different parameters of these technologies and their combination can achieve different performance. This study compared various polychromatic and monochromatic images obtained via DECT with and without using iMAR algorithm to reduce artifacts in patients with dental implants.
View Article and Find Full Text PDFDiagnostics (Basel)
March 2024
Siemens Healthineers Ltd., Seoul 06620, Republic of Korea.
Purpose: To develop and validate a deep-learning-based algorithm (DLA) that is designed to segment and classify metallic objects in topograms of abdominal and spinal CT.
Methods: DLA training for implant segmentation and classification was based on a U-net-like architecture with 263 annotated hip implant topograms and 2127 annotated spine implant topograms. The trained DLA was validated with internal and external datasets.
Invest Radiol
July 2024
From the Siemens Healthineers, CT Physics, Forchheim, Germany (J.A.A., M.H., C.H.); Clinic of Diagnostic and Interventional Radiology, Philipps-University Marburg, Marburg, Germany (J.A.A., A.H.M.); and Infoteam Software AG, Bubenreuth, Germany (P.K.).
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