Literature DB >> 35014900

Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Corey T Jensen1, Shiva Gupta1, Mohammed M Saleh1, Xinming Liu1, Vincenzo K Wong1, Usama Salem1, Wei Qiao1, Ehsan Samei1, Nicolaus A Wagner-Bartak1.   

Abstract

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such effects. Purpose To evaluate liver metastases and image quality between reduced-dose deep learning image reconstruction (DLIR) and standard-dose filtered back projection (FBP) contrast-enhanced abdominal CT. Materials and Methods In this prospective Health Insurance Portability and Accountability Act-compliant study (September 2019 through April 2021), participants with biopsy-proven colorectal cancer and liver metastases at baseline CT underwent standard-dose and reduced-dose portal venous abdominal CT in the same breath hold. Three radiologists detected and characterized lesions at standard-dose FBP and reduced-dose DLIR, reported confidence, and scored image quality. Contrast-to-noise ratios for liver metastases were recorded. Summary statistics were reported, and a generalized linear mixed model was used. Results Fifty-one participants (mean age ± standard deviation, 57 years ± 13; 31 men) were evaluated. The mean volume CT dose index was 65.1% lower with reduced-dose CT (12.2 mGy) than with standard-dose CT (34.9 mGy). A total of 161 lesions (127 metastases, 34 benign lesions) with a mean size of 0.7 cm ± 0.3 were identified. Subjective image quality of reduced-dose DLIR was superior to that of standard-dose FBP (P < .001). The mean contrast-to-noise ratio for liver metastases of reduced-dose DLIR (3.9 ± 1.7) was higher than that of standard-dose FBP (3.5 ± 1.4) (P < .001). Differences in detection were identified only for lesions 0.5 cm or smaller: 63 of 65 lesions detected with standard-dose FBP (96.9%; 95% CI: 89.3, 99.6) and 47 lesions with reduced-dose DLIR (72.3%; 95% CI: 59.8, 82.7). Lesion accuracy with standard-dose FBP and reduced-dose DLIR was 80.1% (95% CI: 73.1, 86.0; 129 of 161 lesions) and 67.1% (95% CI: 59.3, 74.3; 108 of 161 lesions), respectively (P = .01). Lower lesion confidence was reported with a reduced dose (P < .001). Conclusion Deep learning image reconstruction (DLIR) improved CT image quality at 65% radiation dose reduction while preserving detection of liver lesions larger than 0.5 cm. Reduced-dose DLIR demonstrated overall inferior characterization of liver lesions and reader confidence. Clinical trial registration no. NCT03151564 © RSNA, 2022 Online supplemental material is available for this article.

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Year:  2022        PMID: 35014900      PMCID: PMC8962777          DOI: 10.1148/radiol.211838

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   29.146


  30 in total

1.  Comparison of Abdominal Computed Tomographic Enhancement and Organ Lesion Depiction Between Weight-Based Scanner Software Contrast Dosing and a Fixed-Dose Protocol in a Tertiary Care Oncologic Center.

Authors:  Corey T Jensen; Katherine J Blair; Nicolaus A Wagner-Bartak; Lan N Vu; Brett W Carter; Jia Sun; Tharakeswara K Bathala; Shiva Gupta
Journal:  J Comput Assist Tomogr       Date:  2019 Jan/Feb       Impact factor: 1.826

2.  Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.

Authors:  Damien Racine; Fabio Becce; Anais Viry; Pascal Monnin; Brian Thomsen; Francis R Verdun; David C Rotzinger
Journal:  Phys Med       Date:  2020-06-20       Impact factor: 2.685

3.  Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.

Authors:  Anushri Parakh; Jinjin Cao; Theodore T Pierce; Michael A Blake; Cristy A Savage; Avinash R Kambadakone
Journal:  Eur Radiol       Date:  2021-04-23       Impact factor: 5.315

4.  Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).

Authors:  Injoong Kim; Hyunkoo Kang; Hyun Jung Yoon; Bo Mi Chung; Na-Young Shin
Journal:  Neuroradiology       Date:  2020-10-10       Impact factor: 2.804

5.  Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.

Authors:  Joël Greffier; Aymeric Hamard; Fabricio Pereira; Corinne Barrau; Hugo Pasquier; Jean Paul Beregi; Julien Frandon
Journal:  Eur Radiol       Date:  2020-02-25       Impact factor: 5.315

Review 6.  Deep Learning: A Primer for Radiologists.

Authors:  Gabriel Chartrand; Phillip M Cheng; Eugene Vorontsov; Michal Drozdzal; Simon Turcotte; Christopher J Pal; Samuel Kadoury; An Tang
Journal:  Radiographics       Date:  2017 Nov-Dec       Impact factor: 5.333

7.  Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm.

Authors:  Justin Solomon; Peijei Lyu; Daniele Marin; Ehsan Samei
Journal:  Med Phys       Date:  2020-07-06       Impact factor: 4.071

8.  Image texture, low contrast liver lesion detectability and impact on dose: Deep learning algorithm compared to partial model-based iterative reconstruction.

Authors:  D Racine; H G Brat; B Dufour; J M Steity; M Hussenot; B Rizk; D Fournier; F Zanca
Journal:  Eur J Radiol       Date:  2021-06-03       Impact factor: 3.528

9.  Use of Water Equivalent Diameter for Calculating Patient Size and Size-Specific Dose Estimates (SSDE) in CT: The Report of AAPM Task Group 220.

Authors:  Cynthia McCollough; Donovan M Bakalyar; Maryam Bostani; Samuel Brady; Kristen Boedeker; John M Boone; H Heather Chen-Mayer; Olav I Christianson; Shuai Leng; Baojun Li; Michael F McNitt-Gray; Roy A Nilsen; Mark P Supanich; Jia Wang
Journal:  AAPM Rep       Date:  2014-09

10.  Radiation Dose Reduction by Using CT with Iterative Model Reconstruction in Patients with Pulmonary Invasive Fungal Infection.

Authors:  Chenggong Yan; Jun Xu; Chunyi Liang; Qi Wei; Yuankui Wu; Wei Xiong; Huan Zheng; Yikai Xu
Journal:  Radiology       Date:  2018-04-10       Impact factor: 11.105

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  3 in total

1.  Commentary On: Image Quality Evaluation in Dual Energy CT of the Chest, Abdomen and Pelvis in Obese Patients with Deep Learning Image Reconstruction.

Authors:  Corey T Jensen
Journal:  J Comput Assist Tomogr       Date:  2022-06-23       Impact factor: 2.081

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.  Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction.

Authors:  Tetsuro Kaga; Yoshifumi Noda; Takayuki Mori; Nobuyuki Kawai; Toshiharu Miyoshi; Fuminori Hyodo; Hiroki Kato; Masayuki Matsuo
Journal:  Jpn J Radiol       Date:  2022-03-14       Impact factor: 2.701

  3 in total

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