Literature DB >> 31967501

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

Ramandeep Singh1,2, Subba R Digumarthy1,2, Victorine V Muse1,2, Avinash R Kambadakone1,2, Michael A Blake1,2, Azadeh Tabari2,3, Yiemeng Hoi4, Naruomi Akino4, Erin Angel4, Rachna Madan2,3, Mannudeep K Kalra1,2.   

Abstract

OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. MATERIALS AND METHODS. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest (n = 22; 16 women, six men) and abdominopelvic (n = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. RESULTS. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (p < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (p = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (p < 0.0001). CONCLUSION. At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.

Entities:  

Keywords:  abdomen CT; chest CT; deep learning; image reconstruction; radiation dose

Mesh:

Substances:

Year:  2020        PMID: 31967501     DOI: 10.2214/AJR.19.21809

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  34 in total

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Authors:  Hiroyuki Okamoto; Satoshi Kito; Naoki Tohyama; Shunsuke Yonai; Ryu Kawamorita; Masaru Nakamura; Takahiro Fujimoto; Syoji Tani; Akihiro Yomoda; Toru Isobe; Hiroshi Furukawa; Kikuo Kotaka; Jun Itami; Hitoshi Ikushima; Takushi Dokiya; Yoshiyuki Shioyama
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Review 3.  Advanced CT techniques for assessing hepatocellular carcinoma.

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4.  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.

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Journal:  Quant Imaging Med Surg       Date:  2022-05

5.  Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study.

Authors:  Joël Greffier; Djamel Dabli; Aymeric Hamard; Asmaa Belaouni; Philippe Akessoul; Julien Frandon; Jean-Paul Beregi
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6.  Observer Performance for Detection of Pulmonary Nodules at Chest CT over a Large Range of Radiation Dose Levels.

Authors:  Joel G Fletcher; David L Levin; Anne-Marie G Sykes; Rebecca M Lindell; Darin B White; Ronald S Kuzo; Vighnesh Suresh; Lifeng Yu; Shuai Leng; David R Holmes; Akitoshi Inoue; Matthew P Johnson; Rickey E Carter; Cynthia H McCollough
Journal:  Radiology       Date:  2020-09-29       Impact factor: 11.105

7.  Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography.

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Journal:  Br J Radiol       Date:  2021-02-24       Impact factor: 3.039

8.  Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection.

Authors:  Yoshifumi Noda; Tetsuro Kaga; Nobuyuki Kawai; Toshiharu Miyoshi; Hiroshi Kawada; Fuminori Hyodo; Avinash Kambadakone; Masayuki Matsuo
Journal:  Br J Radiol       Date:  2021-02-22       Impact factor: 3.039

9.  Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction.

Authors:  Joël Greffier; Salim Si-Mohamed; Boris Guiu; Julien Frandon; Maeliss Loisy; Fabien de Oliveira; Philippe Douek; Jean-Paul Beregi; Djamel Dabli
Journal:  Quant Imaging Med Surg       Date:  2022-02

10.  Advantages and disadvantages of single-source dual-energy whole-body CT angiography with 50% reduced iodine dose at 40 keV reconstruction.

Authors:  Yoshifumi Noda; Fumihiko Nakamura; Noriyuki Yasuda; Toshiharu Miyoshi; Nobuyuki Kawai; Hiroshi Kawada; Fuminori Hyodo; Masayuki Matsuo
Journal:  Br J Radiol       Date:  2021-02-22       Impact factor: 3.039

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