Ryo Matsukiyo1, Yoshiharu Ohno2,3, Takahiro Matsuyama1, Hiroyuki Nagata1, Hirona Kimata4, Yuya Ito4, Yukihiro Ogawa4, Kazuhiro Murayama5, Ryoichi Kato1, Hiroshi Toyama1. 1. Department of Radiology, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. 2. Department of Radiology, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. yohno@fujita-hu.ac.jp. 3. Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. yohno@fujita-hu.ac.jp. 4. Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara-shi, Tochigi, 324-8550, Japan. 5. Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
Abstract
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.
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.
Entities:
Keywords:
Abdomen; CT; Deep learning; Reconstruction; Vasculature
Authors: Pedro Augusto Gondim Teixeira; Nicolas Villani; Malik Ait Idir; Edouard Germain; Charles Lombard; Romain Gillet; Alain Blum Journal: Quant Imaging Med Surg Date: 2021-10