PMID- 33037956
OWN - NLM
STAT- MEDLINE
DCOM- 20210531
LR  - 20210531
IS  - 1867-108X (Electronic)
IS  - 1867-1071 (Linking)
VI  - 39
IP  - 2
DP  - 2021 Feb
TI  - Deep learning-based and hybrid-type iterative reconstructions for CT: 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.
PG  - 186-197
LID - 10.1007/s11604-020-01045-w [doi]
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.
FAU - Matsukiyo, Ryo
AU  - Matsukiyo R
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
FAU - Ohno, Yoshiharu
AU  - Ohno Y
AUID- ORCID: 0000-0002-4431-1084
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. 
      yohno@fujita-hu.ac.jp.
AD  - 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.
FAU - Matsuyama, Takahiro
AU  - Matsuyama T
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
FAU - Nagata, Hiroyuki
AU  - Nagata H
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
FAU - Kimata, Hirona
AU  - Kimata H
AD  - Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara-shi, Tochigi, 
      324-8550, Japan.
FAU - Ito, Yuya
AU  - Ito Y
AD  - Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara-shi, Tochigi, 
      324-8550, Japan.
FAU - Ogawa, Yukihiro
AU  - Ogawa Y
AD  - Canon Medical Systems Corporation, 1385 Shimoishigami, Otawara-shi, Tochigi, 
      324-8550, Japan.
FAU - Murayama, Kazuhiro
AU  - Murayama K
AD  - Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University 
      School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, 
      Japan.
FAU - Kato, Ryoichi
AU  - Kato R
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
FAU - Toyama, Hiroshi
AU  - Toyama H
AD  - Department of Radiology, Fujita Health University School of Medicine, 1-98, 
      Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20201010
PL  - Japan
TA  - Jpn J Radiol
JT  - Japanese journal of radiology
JID - 101490689
SB  - IM
MH  - Abdomen/*diagnostic imaging
MH  - Algorithms
MH  - Arteries/diagnostic imaging
MH  - Carcinoma, Renal Cell/*diagnostic imaging
MH  - *Deep Learning
MH  - Female
MH  - Humans
MH  - In Vitro Techniques
MH  - Kidney/*diagnostic imaging
MH  - Kidney Neoplasms/*diagnostic imaging
MH  - Male
MH  - Middle Aged
MH  - *Quality Improvement
MH  - Radiographic Image Interpretation, Computer-Assisted
MH  - Tomography, X-Ray Computed/*methods
OTO - NOTNLM
OT  - Abdomen
OT  - CT
OT  - Deep learning
OT  - Reconstruction
OT  - Vasculature
EDAT- 2020/10/11 06:00
MHDA- 2021/06/01 06:00
CRDT- 2020/10/10 12:04
PHST- 2020/08/19 00:00 [received]
PHST- 2020/09/11 00:00 [accepted]
PHST- 2020/10/11 06:00 [pubmed]
PHST- 2021/06/01 06:00 [medline]
PHST- 2020/10/10 12:04 [entrez]
AID - 10.1007/s11604-020-01045-w [pii]
AID - 10.1007/s11604-020-01045-w [doi]
PST - ppublish
SO  - Jpn J Radiol. 2021 Feb;39(2):186-197. doi: 10.1007/s11604-020-01045-w. Epub 2020 
      Oct 10.