BACKGROUND AND PURPOSE: Intracranial hemorrhage is a commonly acknowledged complication of interventional neuroradiology procedures, and the ability to image hemorrhage at the time of the procedure would be very beneficial. A new C-arm system with 3D functionality extends the capability of C-arm imaging to include soft-tissue applications by facilitating the detection of low-contrast objects. We evaluated its ability to detect small intracranial hematomas in a swine model. MATERIALS AND METHODS: Intracranial hematomas were created in 7 swine by autologous blood injection of various hematocrits (19%-37%) and volumes (1.5-5 mL). Four animals received intravascular contrast before obtaining autologous blood (group 1), and 3 did not (group 2). We scanned each animal by using the C-arm CT system, acquiring more than 500 images during a 20-second rotation through more than 200 degrees . Multiplanar reformatted images with isotropic resolution were reconstructed on the workstation by using product truncation, scatter, beam-hardening, and ring-artifact correction algorithms. The brains were harvested and sliced for hematoma measurement and compared with imaging findings. RESULTS: Five intracranial hematomas were created in group 1 animals, and all were visualized. Six were created in group 2, and 3 were visualized. One nonvisualized hematoma was not confirmed at necropsy. All the others in both groups were confirmed. In group 1 (with contrast), small hematomas were detectable even when the hematocrit was 19%-20%. In group 2 (without contrast) C-arm CT was able to detect small hematomas (<1.0 cm(2)) created with hematocrits of 29%-37%. The area of hematoma measured from the C-arm CT data was, on average, within 15% of the area measured from harvested brain. CONCLUSIONS: The image quality obtained with this implementation of C-arm CT was sufficient to detect experimentally created small intracranial hematomas. This capability should provide earlier detection of hemorrhagic complications that may occur during neurointerventional procedures.
BACKGROUND AND PURPOSE:Intracranial hemorrhage is a commonly acknowledged complication of interventional neuroradiology procedures, and the ability to image hemorrhage at the time of the procedure would be very beneficial. A new C-arm system with 3D functionality extends the capability of C-arm imaging to include soft-tissue applications by facilitating the detection of low-contrast objects. We evaluated its ability to detect small intracranial hematomas in a swine model. MATERIALS AND METHODS:Intracranial hematomas were created in 7 swine by autologous blood injection of various hematocrits (19%-37%) and volumes (1.5-5 mL). Four animals received intravascular contrast before obtaining autologous blood (group 1), and 3 did not (group 2). We scanned each animal by using the C-arm CT system, acquiring more than 500 images during a 20-second rotation through more than 200 degrees . Multiplanar reformatted images with isotropic resolution were reconstructed on the workstation by using product truncation, scatter, beam-hardening, and ring-artifact correction algorithms. The brains were harvested and sliced for hematoma measurement and compared with imaging findings. RESULTS: Five intracranial hematomas were created in group 1 animals, and all were visualized. Six were created in group 2, and 3 were visualized. One nonvisualized hematoma was not confirmed at necropsy. All the others in both groups were confirmed. In group 1 (with contrast), small hematomas were detectable even when the hematocrit was 19%-20%. In group 2 (without contrast) C-arm CT was able to detect small hematomas (<1.0 cm(2)) created with hematocrits of 29%-37%. The area of hematoma measured from the C-arm CT data was, on average, within 15% of the area measured from harvested brain. CONCLUSIONS: The image quality obtained with this implementation of C-arm CT was sufficient to detect experimentally created small intracranial hematomas. This capability should provide earlier detection of hemorrhagic complications that may occur during neurointerventional procedures.
Authors: Rastislav Pjontek; Belgin Önenköprülü; Bernhard Scholz; Yiannis Kyriakou; Gerrit A Schubert; Omid Nikoubashman; Ahmed Othman; Martin Wiesmann; Marc A Brockmann Journal: J Neurointerv Surg Date: 2015-09-07 Impact factor: 5.836
Authors: Marc Melià-Sorolla; Carlos Castaño; Núria DeGregorio-Rocasolano; Luis Rodríguez-Esparragoza; Antoni Dávalos; Octavi Martí-Sistac; Teresa Gasull Journal: Int J Mol Sci Date: 2020-09-08 Impact factor: 5.923