Faculty Collaboration Database

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction. J Comput Assist Tomogr 2022 Jul-Aug 01;46(4):604-611

Date

04/29/2022

Pubmed ID

35483100

DOI

10.1097/RCT.0000000000001316

Scopus ID

2-s2.0-85134296786 (requires institutional sign-in at Scopus site)   5 Citations

Abstract

OBJECTIVE: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30.

METHODS: Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods.

RESULTS: Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria ( P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V ( P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V ( P < 0.05).

CONCLUSIONS: TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.

Author List

Fair E, Profio M, Kulkarni N, Laviolette PS, Barnes B, Bobholz S, Levenhagen M, Ausman R, Griffin MO, Duvnjak P, Zorn AP, Foley WD

Authors

Eric Fair MD Assistant Professor in the Radiology department at Medical College of Wisconsin
Michael O. Griffin MD, PhD Associate Professor in the Radiology department at Medical College of Wisconsin
Naveen Kulkarni MD Assistant Professor in the Radiology department at Medical College of Wisconsin
Peter LaViolette PhD Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Abdomen
Algorithms
Humans
Image Processing, Computer-Assisted
Obesity
Pelvis
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed