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01-01-2022 | Computed Tomography | Computed Tomography

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

Authors: Yoshifumi Noda, Nobuyuki Kawai, Shoma Nagata, Fumihiko Nakamura, Takayuki Mori, Toshiharu Miyoshi, Ryosuke Suzuki, Fumiya Kitahara, Hiroki Kato, Fuminori Hyodo, Masayuki Matsuo

Published in: European Radiology | Issue 1/2022

Abstract

Objectives

To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) reconstructed using deep learning image reconstruction (DLIR) and compare them with those of images reconstructed using hybrid iterative reconstruction (IR).

Methods

The local institutional review board approved this prospective study. Written informed consent was obtained from all participants. Thirty consecutive participants with pancreatic cancer (PC) underwent pancreatic protocol DECT for initial evaluation. DECT data were reconstructed at 70 keV using 40% adaptive statistical iterative reconstruction–Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The diagnostic acceptability and conspicuity of PC were qualitatively assessed using a 5-point scale. IC values of the abdominal aorta, pancreas, PC, liver, and portal vein; standard deviation (SD); and coefficient of variation (CV) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups.

Results

The diagnostic acceptability and conspicuity of PC were significantly better in the DLIR-M group compared with those in the other groups (p < .001–.001). The IC values of the anatomical structures were almost comparable between the three groups (p = .001–.9). The SD of IC values was significantly lower in the DLIR-H group (p < .001) and resulted in the lowest CV (p < .001–.002) compared with those in the hybrid-IR and DLIR-M groups.

Conclusions

DLIR could significantly improve image quality and reduce the variability of IC values than could hybrid-IR.

Key Points

Image quality and conspicuity of pancreatic cancer were the best in DLIR-M.
DLIR significantly reduced background noise and improved SNR and CNR.
The variability of iodine concentration was reduced in DLIR.

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Title: Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration
Authors: Yoshifumi Noda
Nobuyuki Kawai
Shoma Nagata
Fumihiko Nakamura
Takayuki Mori
Toshiharu Miyoshi
Ryosuke Suzuki
Fumiya Kitahara
Hiroki Kato
Fuminori Hyodo
Masayuki Matsuo
Publication date: 01-01-2022
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Pancreatic Cancer
Pancreatic Cancer
Published in: European Radiology / Issue 1/2022
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-08121-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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