Ahmed E Othman1,2, Carolin Brockmann1, Zepa Yang3,4, Changwon Kim3,4, Saif Afat1, Rastislav Pjontek1, Omid Nikoubashman1, Marc A Brockmann1, Konstantin Nikolaou2, Martin Wiesmann1, Jong Hyo Kim5,6,7,8. 1. Department of Diagnostic and Interventional Neuroradiology, RWTH Aachen University, 52074, Aachen, Germany. 2. Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany. 3. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, 433-270, South Korea. 4. Department of Radiology, Seoul National University College of Medicine, Seoul, 110-744, South Korea. 5. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, 433-270, South Korea. kimjhyo@snu.ac.kr. 6. Department of Radiology, Seoul National University College of Medicine, Seoul, 110-744, South Korea. kimjhyo@snu.ac.kr. 7. Center for Medical-IT Convergence Technology Research, Advanced Institute of Convergence Technology, Suwon, 433-270, South Korea. kimjhyo@snu.ac.kr. 8. Department of Radiology, Seoul National University Hospital, 101 Daehak-Ro, Chongno-gu, Seoul, 110-744, South Korea. kimjhyo@snu.ac.kr.
Abstract
OBJECTIVES: To examine the impact of denoising on ultra-low-dose volume perfusion CT (ULD-VPCT) imaging in acute stroke. METHODS: Simulated ULD-VPCT data sets at 20 % dose rate were generated from perfusion data sets of 20 patients with suspected ischemic stroke acquired at 80 kVp/180 mAs. Four data sets were generated from each ULD-VPCT data set: not-denoised (ND); denoised using spatiotemporal filter (D1); denoised using quanta-stream diffusion technique (D2); combination of both methods (D1 + D2). Signal-to-noise ratio (SNR) was measured in the resulting 100 data sets. Image quality, presence/absence of ischemic lesions, CBV and CBF scores according to a modified ASPECTS score were assessed by two blinded readers. RESULTS: SNR and qualitative scores were highest for D1 + D2 and lowest for ND (all p ≤ 0.001). In 25 % of the patients, ND maps were not assessable and therefore excluded from further analyses. Compared to original data sets, in D2 and D1 + D2, readers correctly identified all patients with ischemic lesions (sensitivity 1.0, kappa 1.0). Lesion size was most accurately estimated for D1 + D2 with a sensitivity of 1.0 (CBV) and 0.94 (CBF) and an inter-rater agreement of 1.0 and 0.92, respectively. CONCLUSION: An appropriate combination of denoising techniques applied in ULD-VPCT produces diagnostically sufficient perfusion maps at substantially reduced dose rates as low as 20 % of the normal scan. KEY POINTS: Perfusion-CT is an accurate tool for the detection of brain ischemias. The high associated radiation doses are a major drawback of brain perfusion CT. Decreasing tube current in perfusion CT increases image noise and deteriorates image quality. Combination of different image-denoising techniques produces sufficient image quality from ultra-low-dose perfusion CT.
OBJECTIVES: To examine the impact of denoising on ultra-low-dose volume perfusion CT (ULD-VPCT) imaging in acute stroke. METHODS: Simulated ULD-VPCT data sets at 20 % dose rate were generated from perfusion data sets of 20 patients with suspected ischemic stroke acquired at 80 kVp/180 mAs. Four data sets were generated from each ULD-VPCT data set: not-denoised (ND); denoised using spatiotemporal filter (D1); denoised using quanta-stream diffusion technique (D2); combination of both methods (D1 + D2). Signal-to-noise ratio (SNR) was measured in the resulting 100 data sets. Image quality, presence/absence of ischemic lesions, CBV and CBF scores according to a modified ASPECTS score were assessed by two blinded readers. RESULTS: SNR and qualitative scores were highest for D1 + D2 and lowest for ND (all p ≤ 0.001). In 25 % of the patients, ND maps were not assessable and therefore excluded from further analyses. Compared to original data sets, in D2 and D1 + D2, readers correctly identified all patients with ischemic lesions (sensitivity 1.0, kappa 1.0). Lesion size was most accurately estimated for D1 + D2 with a sensitivity of 1.0 (CBV) and 0.94 (CBF) and an inter-rater agreement of 1.0 and 0.92, respectively. CONCLUSION: An appropriate combination of denoising techniques applied in ULD-VPCT produces diagnostically sufficient perfusion maps at substantially reduced dose rates as low as 20 % of the normal scan. KEY POINTS: Perfusion-CT is an accurate tool for the detection of brain ischemias. The high associated radiation doses are a major drawback of brain perfusion CT. Decreasing tube current in perfusion CT increases image noise and deteriorates image quality. Combination of different image-denoising techniques produces sufficient image quality from ultra-low-dose perfusion CT.
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