Journal article
Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT
American journal of roentgenology (1976), Vol.214(3), pp.566-573
03/01/2020
DOI: 10.2214/ajr.19.21809
PMID: 31967501
Abstract
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.
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 (
= 22; 16 women, six men) and abdominopelvic (
= 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.
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) (
< 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 (
= 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (
< 0.0001).
At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.
Details
- Title: Subtitle
- Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT
- Creators
- Ramandeep Singh - Massachusetts General HospitalSubba R Digumarthy - Massachusetts General HospitalVictorine V Muse - Massachusetts General HospitalAvinash R Kambadakone - Massachusetts General HospitalMichael A Blake - Massachusetts General HospitalAzadeh Tabari - Harvard UniversityYiemeng Hoi - Varian Medical SystemsNaruomi Akino - Varian Medical SystemsErin Angel - Varian Medical SystemsRachna Madan - Brigham and Women's HospitalMannudeep K Kalra - Massachusetts General Hospital
- Resource Type
- Journal article
- Publication Details
- American journal of roentgenology (1976), Vol.214(3), pp.566-573
- DOI
- 10.2214/ajr.19.21809
- PMID
- 31967501
- ISSN
- 0361-803X
- eISSN
- 1546-3141
- Language
- English
- Date published
- 03/01/2020
- Academic Unit
- Radiology
- Record Identifier
- 9984697727302771
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