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Eur Radiol. 2019 Nov;29(11):6163-6171. doi: 10.1007/s00330-019-06170-3. Epub 2019 Apr 11.

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

Author information

1
Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
2
Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan. yukon@hiroshima-u.ac.jp.
3
Canon Medical Research USA, Inc., Vernon Hills, IL, USA.
4
Canon Medical Systems Co. Ltd., Otawara, Japan.

Abstract

OBJECTIVES:

Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).

METHODS:

Our retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.

RESULTS:

The image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (pā€‰<ā€‰0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.

CONCLUSIONS:

DLR improved the quality of abdominal U-HRCT images.

KEY POINTS:

ā€¢ The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. ā€¢ Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

KEYWORDS:

Artificial intelligence; Liver; Machine learning; Neural networks (computer); X-ray computed tomography

PMID:
30976831
DOI:
10.1007/s00330-019-06170-3
[Indexed for MEDLINE]

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