Improvement of Spatial Resolution on Coronary CT Angiography by Using Super-Resolution Deep Learning Reconstruction
Fuminari Tatsugami, Toru Higaki, Ikuo Kawashita, Wataru Fukumoto, Yuko Nakamura, Masakazu Matsuura, Tzu-Cheng Lee, Jian Zhou, Liang Cai, Toshiro Kitagawa, Yukiko Nakano, Kazuo Awai,

Acad Radiol. 2023 Jan 19;S1076-6332(22)00700-0. doi: 10.1016/j.acra.2022.12.044.

Conclusion: SR-DLR was superior to hybrid IR with respect to the image noise, the sharpness of coronary artery margins, and plaque detectability.


Impact of a Deep Learning-based Super-resolution Image Reconstruction Technique on High-contrast Computed Tomography: A Phantom Study
Hideyuki Sato, Shinichiro Fujimoto, Nobuo Tomizawa, Hidekazu Inage, Takuya Yokota, Hikaru Kudo, Ruiheng Fan, Keiichi Kawamoto, Yuri Honda, Takayuki Kobayashi, Tohru Minamino, Yosuke Kogure,

Acad Radiol. 2023 Jan 21;S1076-6332(22)00696-1. doi: 10.1016/j.acra.2022.12.040.

Conclusion: The present results suggest that DLSRR can achieve greater noise reduction and improved spatial resolution in the high-contrast region compared with conventional DLR and iterative reconstruction techniques.


Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: An initial experience
Makoto Orii, Misato Sone, Takeshi Osaki, Yuta Ueyama, Takuya Chiba, Tadashi Sasaki, Kunihiro Yoshioka

Research Square. DOI: https://doi.org/10.21203/rs.3.rs-1875541/v2

Conclusion: SR-DLR improves the image quality of the coronary arteries and in-stent lumen at CCTA. Datasets
reconstructed with SR-DLR empower the clinician with the high-contrast signal definition and reduce
noise, relative to conventional MBIR.


Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep-Learning Reconstruction and Other Reconstruction Algorithms
Yasunori Nagayama, Takafumi Emoto, Hidetaka Hayashi, Masafumi Kidoh, Seitaro Oda, Takeshi Nakaura, Daisuke Sakabe, Yoshinori Funama, Noriaki Tabata, Masanobu Ishii, Kenshi Yamanaga, Koichiro Fujisue, Seiji Takashio, Eiichiro Yamamoto, Kenichi Tsujita, and Toshinori Hirai,
AJR, 2023, https://doi.org/10.2214/AJR.23.29506

Conclusion: SR-DLR yielded improved delineation of the stent strut and in-stent lumen, with better image sharpness and less image noise and blooming artifacts, in comparison with HIR, MBIR, and NR-DLR.


Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography
Yasunori Nagayama, Takafumi Emoto, Yuki Kato, Masafumi Kidoh, Seitaro Oda, Daisuke Sakabe, Yoshinori Funama, Takeshi Nakaura, Hidetaka Hayashi, Sentaro Takada, Ryutaro Uchimura, Masahiro Hatemura, Kenichi Tsujita & Toshinori Hirai,

European Radiology 2023, https://doi.org/10.1007/s00330-023-09888-3

Conclusion: SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms.


Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study
Hideki Kawai, Sadako Motoyama, Masayoshi Sarai, Yoshihiro Sato, Takahiro Matsuyama, Ryota Matsumoto, Hiroshi Takahashi, Akio Katagata, Yumi Kataoka, Yoshihiro Ida, Takashi Muramatsu, Yoshiharu Ohno, Yukio Ozaki, Hiroshi Toyama, Jagat Narula, Hideo Izawa,

European Radiology 2023 Sep 6, https://doi.org/10.1007/s00330-023-10110-7

Conclusion: PIQE provides superior image quality and diagnostic accuracy for ISR, even with stents measuring < 3.0 mm in diameter.

Clinical relevance statement: With improvements in the diagnostic accuracy of in-stent stenosis, CT angiography could become a gatekeeper for ICA in post-stenting cases, obviating ICA in many patients after recent stenting with infrequent ISR and allowing non-invasive ISR detection in the late phase


Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography

Takafuji M, Kitagawa K, Mizutani S, Hamaguchi A, Kisou R, Iio K, Ichikawa K, Izumi D, Sakuma H. Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography.

Radiol Cardiothorac Imaging. 2023 Aug 17;5(4):e230085. doi: 10.1148/ryct.230085. PMID: 37693207; PMCID: PMC10485715.

Conclusion: SR-DLR improved vessel sharpness, image noise, and accuracy of coronary stenosis grading compared with the C-DLR technique.


Super-resolution deep learning reconstruction to improve image quality of coronary CT angiography

Nobuo Tomizawa, Yui Nozaki, Hideyuki Sato, Yuko Kawaguchi, Ayako Kudo, Daigo Takahashi, Kazuhisa Takamura, Makoto Hiki, Shinichiro Fujimoto, Iwao Okai, Seiji Koga, Shinya Okazaki, Kanako K Kumamaru, Tohru Minamino, Shigeki Aoki, Super-resolution deep learning reconstruction to improve image quality of coronary CT angiography,
Radiology Advances, 2024;, umae001, https://doi.org/10.1093/radadv/umae001

Conclusion: Our exploratory analysis suggests that super-resolution deep learning reconstruction could improve image quality with lower tube current settings than model-based iterative reconstruction with similar diagnostic performance to diagnose coronary stenosis in coronary CT angiography.


Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study

Greffier J, Pastor M, Si-Mohamed S, Goutain-Majorel C, Peudon-Balas A, Bensalah MZ, Frandon J, Beregi JP, Dabli D. Comparison of two deep-learning image reconstruction algorithms on cardiac CT images: A phantom study.

Diagn Interv Imaging. 2024 Mar;105(3):110-117. doi: 10.1016/j.diii.2023.10.004. Epub 2023 Nov 8. PMID: 37949769.

Conclusion: Compared to AiCE, PIQE reduced noise, improved spatial resolution, noise texture and detectability of simulated cardiac lesions. PIQE seems to have a greater potential for dose reduction in cardiac CT acquisition.


Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom
Sawamura S, Kato S, Funama Y, Oda S, Mochizuki H, Inagaki S, Takeuchi Y, Morioka T, Izumi T, Ota Y, Kawagoe H, Cheng S, Nakayama N, Fukui K, Tsutsumi T, Iwasawa T, Utsunomiya D. Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Quant Imaging Med Surg. 2024 Apr 3;14(4):2870-2883. doi: 10.21037/qims-23-1204. Epub 2024 Mar 27. PMID: 38617144; PMCID: PMC11007503.

Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.


Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography
Ryu JK, Kim KH, Otgonbaatar C, Kim DS, Shim H, Seo JW. Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography.

Br J Radiol. 2024 May 11:tqae094. doi: 10.1093/bjr/tqae094. Epub ahead of print. PMID: 38733576.

Conclusions: SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations.

Advanced in knowledge: The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.