Abstract No. 197 ▪ FEATURED ABSTRACT Feasibility of Deep Learning-enabled Quarter Dose Interventional Cone-Beam CT: Image Quality Evaluation

To assess the potential of deep learning (DL)-based denoising technique on the reduction of patient radiation dose and image quality improvement in low dose C-arm based interventional Cone-beam Computed Tomography (CBCT). This study collected low-dose (LD) C-arm CBCT datasets from a university hospital. The hospital conducted scans at a level one-fourth of the routine dose (RD) CBCT images for research purposes from December 1, 2019, to January 1, 2022. (RD DAP: s 5796.4 uGym2 vs LD DAP: 1794.8 uGym2). The data was acquired from 90 patients who underwent transarterial chemoembolization (TACE) using Axiom Artis C-arm system (Siemens Healthcare, Forchheim, Germany). All CBCT images were reconstructed via filtered back projection (FBP) and were additionally reconstructed using ClariCT.AI (ClariPi Inc., Seoul, Korea), a DL-based denoising technique that was previously studied for use in MDCT images. Image quality comparison between denoised LD (DLD) and LD images was performed based on image noise and signal-to-noise ratio (SNR). The regions of interest (ROI) for quantitative evaluation abdominal aorta with diameter 1 cm2. After LD and DLD CBCT images were reconstructed MIP images, a radiologist assessed subjective noise, liver vessel visibility. Image noises for DLD were 36.51 ±5.40 HU in liver parenchyma, 37.08 ±27.77 HU in vertebra erector spinae muscles and 53.20 ±11.33 HU in abdominal aorta, resulting in significantly lower noise level than those of LD which were 85.89 ±11.88 HU, 117.14 ±14.71 HU and 178.37 ±70.24 HU, respectively. SNR was 0.84 ±0.39, 0.65 ±0.40 and 0.59 ±0.16 for LD in liver parenchyma, vertebra erector spinae muscles and abdominal aorta, and was 1.97 ±0.89, 1.73 ±1.25 and 1.43 ±0.86 for DLD indicating superior level compared to LD. DLD MIP images showed reduced image noise and improved liver vessel visibility compared to LD MIP images. DL-based denoising can improve image quality in LD C-arm CT images during TACE. Implementing the DL-based denoising technique in C-arm CT can potentially lower patient radiation exposure during TACE procedures, a critical intervention for liver cancer treatment.