Hye Joo Park1, Seo-Youn Choi2, Ji Eun Lee1, Sanghyeok Lim1, Min Hee Lee1, Boem Ha Yi1, Jang Gyu Cha1, Ji Hye Min3, Bora Lee4,5, Yunsub Jung6. 1. Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea. 2. Department of Radiology, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, 170 Jomaru-ro, Bucheon, 14584, Republic of Korea. sychoi@schmc.ac.kr. 3. Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro Gangnam-gu, Seoul,, 06351, Republic of Korea. 4. Institue of Public Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea. 5. Department of Statistics, Chung-Ang University, Seoul, Republic of Korea. 6. GE Healthcare Co., Ltd, 416 Hangang-daero, Jung-gu, Seoul,, 04637, Republic of Korea.
Abstract
OBJECTIVES: To compare the image quality and radiation dose of a deep learning image reconstruction (DLIR) algorithm compared with iterative reconstruction (IR) and filtered back projection (FBP) at different tube voltages and tube currents. MATERIALS AND METHODS: A customized body phantom was scanned at different tube voltages (120, 100, and 80 kVp) with different tube currents (200, 100, and 60 mA). The CT datasets were reconstructed with FBP, hybrid IR (30% and 50%), and DLIR (low, medium, and high levels). The reference image was set as an image taken with FBP at 120 kVp/200 mA. The image noise, contrast-to-noise ratio (CNR), sharpness, artifacts, and overall image quality were assessed in each scan both qualitatively and quantitatively. The radiation dose was also evaluated with the volume CT dose index (CTDIvol) for each dose scan. RESULTS: In qualitative and quantitative analyses, compared with reference images, low-dose CT with DLIR significantly reduced the noise and artifacts and improved the overall image quality, even with decreased sharpness (p < 0.05). Despite the reduction of image sharpness, low-dose CT with DLIR could maintain the image quality comparable to routine-dose CT with FBP, especially when using the medium strength level. CONCLUSION: The new DLIR algorithm reduced noise and artifacts and improved overall image quality, compared to FBP and hybrid IR. Despite reduced image sharpness in CT images of DLIR algorithms, low-dose CT with DLIR seems to have an overall greater potential for dose optimization. KEY POINTS: • Using deep learning image reconstruction (DLIR) algorithms, image quality was maintained even with a radiation dose reduced by approximately 70%. • DLIR algorithms yielded lower image noise, higher contrast-to-noise ratios, and higher overall image quality than FBP and hybrid IR, both subjectively and objectively. • DLIR algorithms can provide a better image quality, much better than FBP and even better than hybrid IR, while facilitating a reduction in radiation dose.
OBJECTIVES: To compare the image quality and radiation dose of a deep learning image reconstruction (DLIR) algorithm compared with iterative reconstruction (IR) and filtered back projection (FBP) at different tube voltages and tube currents. MATERIALS AND METHODS: A customized body phantom was scanned at different tube voltages (120, 100, and 80 kVp) with different tube currents (200, 100, and 60 mA). The CT datasets were reconstructed with FBP, hybrid IR (30% and 50%), and DLIR (low, medium, and high levels). The reference image was set as an image taken with FBP at 120 kVp/200 mA. The image noise, contrast-to-noise ratio (CNR), sharpness, artifacts, and overall image quality were assessed in each scan both qualitatively and quantitatively. The radiation dose was also evaluated with the volume CT dose index (CTDIvol) for each dose scan. RESULTS: In qualitative and quantitative analyses, compared with reference images, low-dose CT with DLIR significantly reduced the noise and artifacts and improved the overall image quality, even with decreased sharpness (p < 0.05). Despite the reduction of image sharpness, low-dose CT with DLIR could maintain the image quality comparable to routine-dose CT with FBP, especially when using the medium strength level. CONCLUSION: The new DLIR algorithm reduced noise and artifacts and improved overall image quality, compared to FBP and hybrid IR. Despite reduced image sharpness in CT images of DLIR algorithms, low-dose CT with DLIR seems to have an overall greater potential for dose optimization. KEY POINTS: • Using deep learning image reconstruction (DLIR) algorithms, image quality was maintained even with a radiation dose reduced by approximately 70%. • DLIR algorithms yielded lower image noise, higher contrast-to-noise ratios, and higher overall image quality than FBP and hybrid IR, both subjectively and objectively. • DLIR algorithms can provide a better image quality, much better than FBP and even better than hybrid IR, while facilitating a reduction in radiation dose.