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Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance

  • Gautier LaurentEmail author
  • Nicolas Villani
  • Gabriela Hossu
  • Aymeric Rauch
  • Alain Noël
  • Alain Blum
  • Pedro Augusto Gondim Teixeira
Physics
  • 38 Downloads

Abstract

Objective

Evaluate and compare the image quality and acceptance of a full MBIR algorithm to that of an earlier full IR hybrid algorithm and filtered back projection (FBP).

Methods

Acquisitions were performed with a 320 detector-row CT scanner with seven different dose levels. Images were reconstructed with three algorithms: FBP, full hybrid iterative reconstruction (HIR), and a full model-based iterative reconstruction algorithm (full MBIR). The sensitometry, spatial resolution, image texture, and low-contrast detectability of these algorithms were compared. Subjective analysis of low-contrast detectability was performed. Ten radiologists answered a questionnaire on image quality and confidence in full MBIR images in clinical practice.

Results

The contrast-to-noise ratio of full MBIR was significantly higher than in the other algorithms (p < 0.0015). The spatial resolution was also higher with full MBIR at high frequencies (> 0.3 lp/mm). Full MBIR at low dose levels led to better low-contrast detectability and more inserts being identified with a higher confidence (p < 0.0001). Full MBIR was associated with a change in image texture compared to HIR and FBP. Eighty percent of radiologists judged general appearance and texture of full MBIR images worse than HIR. Moreover, compared with HIR, for 50% of radiologists, the diagnostic confidence on full MBIR images was worse. Questionnaire reliability was considered acceptable (Cronbach alpha 0.7).

Conclusion

Compared to conventional iterative reconstruction algorithms, full MBMIR presented a higher image quality and low-contrast detectability and a worse acceptance among radiologists.

Key Points

• Full MBIR used led to an overall improvement in image quality compared with FBP and HIR.

• Full MBIR leads to image texture change which reduces the confidence in these images among radiologists.

• Awareness of the image texture change and improved quality of full MBIR reconstructed images could improve the acceptance of this technique in clinical practice.

Keywords

Computed tomography Image reconstruction Image quality Phantom imaging Abdomen 

Abbreviations

AIDR

Adaptative statistical iterative reconstruction

CNR

Contrast-to-noise-ratio

CT

Computed tomography

CTP

Catphan phantom

FBP

Filtered back projection

HIR

Hybrid iterative reconstruction

HU

Hounsfield units

IR

Iterative reconstruction

LDPE

Low density polyethylene

MBIR

Model-based iterative reconstruction

MTF

Modulation transfer function

NPS

Noise power spectrum

ROI

Region of interest

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Professor Pedro Augusto Gondim Teixeira.

Conflict of interest

Two authors involved in this work (Pedro Augusto Gondim Teixeira and Alain Blum) participate on a non-remunerated research contract with CANON medical Systems for the development and clinical testing of post processing tools for CT. The others authors have non-potential conflicts of interest to disclose.

Statistics and biometry

One of the authors, Gabriela Hossu, PhD, is a statistician.

Ethical approval

Institutional Review Board approval was not required because this was a phantom based study.

Methodology

• prospective

• experimental

• performed at one institution

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Guilloz Imaging Department, Central HospitalUniversity Hospital Center of NancyNancy CedexFrance
  2. 2.Medical Radiophysical UnitCentre Alexis-VautrinVandoeuvre-lès-NancyFrance
  3. 3.Université de Lorraine, Inserm, IADINancyFrance
  4. 4.CRAN UMR 7039 Université de Lorraine-CNRSVillers-lès-NancyFrance

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