Literature DB >> 32248261

Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography.

Keigo Narita1, Yuko Nakamura2, Toru Higaki1, Motonori Akagi1, Yukiko Honda1, Kazuo Awai1.   

Abstract

PURPOSE: Deep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).
METHODS: This retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared.
RESULTS: The image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001).
CONCLUSION: DLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.

Entities:  

Keywords:  Artificial intelligence; Cholangiography; Neural networks (computer); Tomography, X-ray computed

Year:  2020        PMID: 32248261     DOI: 10.1007/s00261-020-02508-4

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  5 in total

1.  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.

Authors:  Akio Tamura; Eisuke Mukaida; Yoshitaka Ota; Iku Nakamura; Kazumasa Arakita; Kunihiro Yoshioka
Journal:  Quant Imaging Med Surg       Date:  2022-05

2.  Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi.

Authors:  Xiaoxiao Zhang; Gumuyang Zhang; Lili Xu; Xin Bai; Jiahui Zhang; Min Xu; Jing Yan; Daming Zhang; Zhengyu Jin; Hao Sun
Journal:  Insights Imaging       Date:  2022-10-08

3.  Artificial Intelligence Algorithm-Based High-Resolution Computed Tomography Image in the Treatment of Children with Bronchiolitis Obliterans by Traditional Chinese Medicine Method of Resolving Phlegm and Removing Blood Stasis.

Authors:  Xiaoning Shi; Qing Zhou
Journal:  Contrast Media Mol Imaging       Date:  2022-05-27       Impact factor: 3.009

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

Authors:  Yannan Cheng; Yangyang Han; Jianying Li; Ganglian Fan; Le Cao; Junjun Li; Xiaoqian Jia; Jian Yang; Jianxin Guo
Journal:  Br J Radiol       Date:  2021-02-24       Impact factor: 3.039

5.  Global illumination rendering versus volume rendering for the forensic evaluation of stab wounds using computed tomography.

Authors:  Wataru Fukumoto; Nobuo Kitera; Hidenori Mitani; Takahiro Sueoka; Shota Kondo; Ikuo Kawashita; Yuko Nakamura; Masataka Nagao; Kazuo Awai
Journal:  Sci Rep       Date:  2022-02-14       Impact factor: 4.379

  5 in total

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