PMID- 35502368
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220506
IS  - 2223-4292 (Print)
IS  - 2223-4306 (Electronic)
IS  - 2223-4306 (Linking)
VI  - 12
IP  - 5
DP  - 2022 May
TI  - Deep learning reconstruction allows low-dose imaging while maintaining image 
      quality: comparison of deep learning reconstruction and hybrid iterative 
      reconstruction in contrast-enhanced abdominal CT.
PG  - 2977-2984
LID - 10.21037/qims-21-1216 [doi]
AB  - We aimed to compare the radiation dose and image quality of a low-dose abdominal 
      computed tomography (CT) protocol reconstructed with deep learning reconstruction 
      (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative 
      reconstruction. This retrospective study enrolled 71 patients [61 men; average 
      age, 71.9 years; mean body mass index (BMI), 24.3 kg/m(2)] who underwent both 
      low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and 
      routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive 
      iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume 
      CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate 
      (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image 
      noise was measured in the hepatic parenchyma and bilateral erector spinae 
      muscles. Moreover, subjective assessment of perceived image quality and 
      diagnostic acceptability was performed. The low-dose protocol helped reduce the 
      CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was 
      significantly lower and CNR significantly higher with the low-dose protocol than 
      with the normal-dose protocol (P<0.001). In the subjective assessment of image 
      quality, there was no significant difference between the protocols with regard to 
      image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic 
      acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by 
      more than 40% without loss of image quality compared to routine-dose abdominal CT 
      with AIDR 3D.
CI  - 2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.
FAU - Tamura, Akio
AU  - Tamura A
AD  - Department of Radiology, Iwate Medical University School of Medicine, Iwate, 
      Japan.
FAU - Mukaida, Eisuke
AU  - Mukaida E
AD  - Department of Radiology, Iwate Medical University School of Medicine, Iwate, 
      Japan.
FAU - Ota, Yoshitaka
AU  - Ota Y
AD  - Division of Central Radiology, Iwate Medical University Hospital, Iwate, Japan.
FAU - Nakamura, Iku
AU  - Nakamura I
AD  - Iwate Medical University School of Medicine, Iwate, Japan.
FAU - Arakita, Kazumasa
AU  - Arakita K
AD  - Healthcare IT Development Center, Canon Medical Systems Corporation, Otawara, 
      Japan.
FAU - Yoshioka, Kunihiro
AU  - Yoshioka K
AD  - Department of Radiology, Iwate Medical University School of Medicine, Iwate, 
      Japan.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Quant Imaging Med Surg
JT  - Quantitative imaging in medicine and surgery
JID - 101577942
PMC - PMC9014148
OTO - NOTNLM
OT  - Computed tomography (CT)
OT  - advanced intelligent clear-IQ engine (AiCE)
OT  - contrast-to-noise ratio (CNR)
OT  - deep learning reconstruction (DLR)
OT  - noise reduction
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://qims.amegroups.com/article/view/10.21037/qims-21-1216/coif). KA is an 
      employee of Canon Medical Systems Corporation. The other authors have no 
      conflicts of interest to declare.
EDAT- 2022/05/04 06:00
MHDA- 2022/05/04 06:01
PMCR- 2022/05/01
CRDT- 2022/05/03 02:10
PHST- 2021/12/17 00:00 [received]
PHST- 2022/02/09 00:00 [accepted]
PHST- 2022/05/03 02:10 [entrez]
PHST- 2022/05/04 06:00 [pubmed]
PHST- 2022/05/04 06:01 [medline]
PHST- 2022/05/01 00:00 [pmc-release]
AID - qims-12-05-2977 [pii]
AID - 10.21037/qims-21-1216 [doi]
PST - ppublish
SO  - Quant Imaging Med Surg. 2022 May;12(5):2977-2984. doi: 10.21037/qims-21-1216.