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Observer performance for adaptive, image-based denoising and filtered back projection compared to scanner-based iterative reconstruction for lower dose CT enterography

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Abstract

Purpose

The purpose of this study was to compare observer performance for detection of intestinal inflammation for low-dose CT enterography (LD-CTE) using scanner-based iterative reconstruction (IR) vs. vendor-independent, adaptive image-based noise reduction (ANLM) or filtered back projection (FBP).

Methods

Sixty-two LD-CTE exams were performed. LD-CTE images were reconstructed using IR, ANLM, and FBP. Three readers, blinded to image type, marked intestinal inflammation directly on patient images using a specialized workstation over three sessions, interpreting one image type/patient/session. Reference standard was created by a gastroenterologist and radiologist, who reviewed all available data including dismissal Gastroenterology records, and who marked all inflamed bowel segments on the same workstation. Reader and reference localizations were then compared. Non-inferiority was tested using Jackknife free-response ROC (JAFROC) figures of merit (FOM) for ANLM and FBP compared to IR. Patient-level analyses for the presence or absence of inflammation were also conducted.

Results

There were 46 inflamed bowel segments in 24/62 patients (CTDIvol interquartile range 6.9–10.1 mGy). JAFROC FOM for ANLM and FBP were 0.84 (95% CI 0.75–0.92) and 0.84 (95% CI 0.75–0.92), and were statistically non-inferior to IR (FOM 0.84; 95% CI 0.76–0.93). Patient-level pooled confidence intervals for sensitivity widely overlapped, as did specificities. Image quality was rated as better with IR and AMLM compared to FBP (p < 0.0001), with no difference in reading times (p = 0.89).

Conclusions

Vendor-independent adaptive image-based noise reduction and FBP provided observer performance that was non-inferior to scanner-based IR methods. Adaptive image-based noise reduction maintained or improved upon image quality ratings compared to FBP when performing CTE at lower dose levels.

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Acknowledgments

Authors wish to acknowledge several key individuals for their contribution to this work. We thank Zhoubo Li and Dr. Armando Manduca for their development of the adaptive, image-based denoising method studied in this work, along with Drs. Dan Blezek and Brad Erickson, who were instrumental in adapting this method for implementation on a computer server that could be used in clinical practice. David Lake was extremely helpful in pilot work designed to refine the ANLM algorithm for clinical use. Kurt Augustine largely wrote the software, which the specialized computer workstations used for recording and matching of reader and reference markings. Sally Reinhart was invaluable in her assistance with manuscript preparation. This grant was supported by a Mayo Clinic Discovery and Translation Award.

Conflict of interest

A provisional patent application explaining the adaptive, image-based denoising method examined in this manuscript has been filed. Drs. Fletcher and McCollough are co-authors of the patent application. The other authors have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA

    Joel G. Fletcher, Jeff L. Fidler, John M. Barlow, Maria Shiung, Lifeng Yu & Cynthia H. McCollough

  2. Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA

    Amy K. Hara & Alvin C. Silva

  3. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Rickey E. Carter & Adam Bartley

  4. Department of Biomedical Imaging Resources, Mayo Clinic, Rochester, MN, USA

    David R. Holmes III

  5. Department of Gastroenterology, Mayo Clinic, Rochester, MN, USA

    Nicolas K. Weber & David H. Bruining

Authors
  1. Joel G. Fletcher
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  2. Amy K. Hara
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Corresponding author

Correspondence to Joel G. Fletcher.

Appendix: image quality assessment performed by radiologist readers

Appendix: image quality assessment performed by radiologist readers

Image quality criteria were performed after observer interrogation of each dataset. Readers could scroll through the data while evaluating image quality. Criteria and rankings are described below:

Overall image quality (5-point scale):

1 = non-diagnostic due to excessive noise artifacts

2 = diagnosis questionable due to excessive noise/artifacts; moderate decrease in diagnostic confidence

3 = diagnostic with moderate but acceptable noise/artifacts

4 = mild noise, no change in diagnostic confidence

5 = routine diagnostic image quality

Image sharpness (5-point scale):

1 = very sharp

2 = mildly unsharp edges, no diagnostic difference

3 = moderately unsharp, questionable diagnostic difference

4 = noticeable blur with poorly defined edges

5 = non-diagnostic

Image noise (4-point scale):

1 = less than usual

2 = optimal ‘routine’ noise

3 = increased noise, does not affect interpretation

4 = increased noise affecting interpretation

Noise texture (4-point scale):

0 = no noticeable change

1 = no noticeable change after window settings changed

2 = perceptible change

3 = blotchiness or change affecting confidence

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Fletcher, J.G., Hara, A.K., Fidler, J.L. et al. Observer performance for adaptive, image-based denoising and filtered back projection compared to scanner-based iterative reconstruction for lower dose CT enterography. Abdom Imaging 40, 1050–1059 (2015). https://doi.org/10.1007/s00261-015-0384-1

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