Ju Gang Nam1, Jung Hee Hong1, Da Som Kim2, Jiseon Oh1, Jin Mo Goo3,4. 1. Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea. 2. Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, 47392, Republic of Korea. 3. Department of Radiology, Seoul National University Hospital and College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea. jmgoo@plaza.snu.ac.kr. 4. Cancer Research Institute, Seoul National University College of Medicine, 03080, Seoul, Republic of Korea. jmgoo@plaza.snu.ac.kr.
Abstract
OBJECTIVE: To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. METHODS: One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. RESULTS: The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. CONCLUSION: With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. KEY POINTS: • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.
OBJECTIVE: To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. METHODS: One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. RESULTS: The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. CONCLUSION: With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. KEY POINTS: • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.
Authors: Corey T Jensen; Shiva Gupta; Mohammed M Saleh; Xinming Liu; Vincenzo K Wong; Usama Salem; Wei Qiao; Ehsan Samei; Nicolaus A Wagner-Bartak Journal: Radiology Date: 2022-01-11 Impact factor: 29.146