Japanese journal of radiology · 2025
Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.
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Ma Yongping, Nakajima Satoshi, Fushimi Yasutaka, Funaki Takeshi, Otani Sayo, Takiya Miyuki, Matsuda Akira, Kozawa Satoshi, Fukushima Yasuhiro, Okuchi Sachi, Sakata Akihiko, Yamamoto Takayuki, Sakamoto Ryo, Chihara Hideo, Mineharu Yohei, Arakawa Yoshiki, Nakamoto Yuji
Аннотация
PURPOSE: To investigate vessel delineation and image quality of ultra-high-resolution (UHR) CT angiography (CTA) reconstructed using deep learning reconstruction (DLR) optimised for brain CTA (DLR-brain) in moyamoya disease (MMD), compared with DLR optimised for body CT (DLR-body) and hybrid iterative reconstruction (Hybrid-IR).
MATERIALS AND METHODS: This retrospective study included 50 patients with suspected or diagnosed MMD who underwent UHR brain CTA. All images were reconstructed using DLR-brain, DLR-body, and Hybrid-IR. Quantitative analysis focussed on moyamoya perforator vessels in the basal ganglia and periventricular anastomosis. For these small vessels, edge sharpness, peak CT number, vessel contrast, full width at half maximum (FWHM), and image noise were measured and compared. Qualitative analysis was performed by visual assessment to compare vessel delineation and image quality.
RESULTS: DLR-brain significantly improved edge sharpness, peak CT number, vessel contrast, and FWHM, and significantly reduced image noise compared with DLR-body and Hybrid-IR (P < 0.05). DLR-brain significantly outperformed the other algorithms in the visual assessment (P < 0.001).
CONCLUSION: DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.
MATERIALS AND METHODS: This retrospective study included 50 patients with suspected or diagnosed MMD who underwent UHR brain CTA. All images were reconstructed using DLR-brain, DLR-body, and Hybrid-IR. Quantitative analysis focussed on moyamoya perforator vessels in the basal ganglia and periventricular anastomosis. For these small vessels, edge sharpness, peak CT number, vessel contrast, full width at half maximum (FWHM), and image noise were measured and compared. Qualitative analysis was performed by visual assessment to compare vessel delineation and image quality.
RESULTS: DLR-brain significantly improved edge sharpness, peak CT number, vessel contrast, and FWHM, and significantly reduced image noise compared with DLR-body and Hybrid-IR (P < 0.05). DLR-brain significantly outperformed the other algorithms in the visual assessment (P < 0.001).
CONCLUSION: DLR-brain provided superior visualisation of small intracranial vessels compared with DLR-body and Hybrid-IR in UHR brain CTA.