TY - JOUR
T1 - Deep learning-based and hybrid-type iterative reconstructions for CT
T2 - comparison of capability for quantitative and qualitative image quality improvements and small vessel evaluation at dynamic CE-abdominal CT with ultra-high and standard resolutions
AU - Matsukiyo, Ryo
AU - Ohno, Yoshiharu
AU - Matsuyama, Takahiro
AU - Nagata, Hiroyuki
AU - Kimata, Hirona
AU - Ito, Yuya
AU - Ogawa, Yukihiro
AU - Murayama, Kazuhiro
AU - Kato, Ryoichi
AU - Toyama, Hiroshi
N1 - Funding Information:
This study consisted of an in vivo and in vitro component. This retrospective study was approved by institutional review board of (Fujita Health University Hospital), and written informed consent was waved from each subject. This study was financially and technically supported by Canon Medical Systems Corporation. Three of the authors are employees of Canon Medical Systems (HK, YI, and YO), but did not have control over any of the data used in this study.
Funding Information:
Authors wish to thank Ryota Hanaoka, MD, PhD, Takashi Ichihara, PhD (Department of Radiology, Fujita Health University School of Medicine), Yujiro Doi, RT, Ryota Matsumoto RT, Akio Katagata, RT, Yumi Kataoka RT (Department of Radiology, Fujita Health University Hospital), Ryoichi Shiroki, MD, PhD (Department of Urology, Fujita Health University School of Medicine), Naruomi Akino, RT, and Kenji Fujii, RT (Canon Medical Systems Corporation) for their excellent contributions to this work. This work was financially supported by Canon Medical Systems Corporation.
PY - 2021/2
Y1 - 2021/2
N2 - Purpose: To determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method. Materials and method: Thirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test. Results: For all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05). Conclusion: DLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.
AB - Purpose: To determine the image quality improvement including vascular structures using deep learning reconstruction (DLR) for ultra-high-resolution CT (UHR-CT) and area-detector CT (ADCT) compared to a commercially available hybrid-iterative reconstruction (IR) method. Materials and method: Thirty-two patients suspected of renal cell carcinoma underwent dynamic contrast-enhanced (CE) CT using UHR-CT or ADCT systems. CT value and contrast-to-noise ratio (CNR) on each CT dataset were assessed with region of interest (ROI) measurements. For qualitative assessment of improvement for vascular structure visualization, each artery was assessed using a 5-point scale. To determine the utility of DLR, CT values and CNRs were compared among all UHR-CT data by means of ANOVA followed by Bonferroni post hoc test, and same values on ADCT data were also compared between hybrid IR and DLR methods by paired t test. Results: For all arteries except the aorta, the CT value and CNR of the DLR method were significantly higher compared to those of the hybrid-type IR method in both CT systems reconstructed as 512 or 1024 matrixes (p < 0.05). Conclusion: DLR has a higher potential to improve the image quality resulting in a more accurate evaluation for vascular structures than hybrid IR for both UHR-CT and ADCT.
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U2 - 10.1007/s11604-020-01045-w
DO - 10.1007/s11604-020-01045-w
M3 - Article
AN - SCOPUS:85092401300
VL - 39
SP - 186
EP - 197
JO - Japanese Journal of Radiology
JF - Japanese Journal of Radiology
SN - 1867-1071
IS - 2
ER -