Demonstrated Clinical Impact of PIQE Cardiac

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.

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.

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.

Conclusion: 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.

Conclusion: 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.