Impact of iterative reconstructions on image quality in clinical CT images demonstrated by a novel noise power spectrum measurement tool

This poster is published under an open license. Please read the disclaimer for further details.

Congress:

ECR 2017

Poster Number:

C-3121

Type:

Scientific Exhibit

Keywords:

Radiation physics, Radioprotection / Radiation dose, Computer applications, CT, Image manipulation / Reconstruction, Computer Applications-General, Physics, Image verification

Authors:

H. Pasquier¹, F. Gardavaud², A. Rahmouni¹, A. Luciani¹; ¹Creteil/FR, ²Paris/FR

DOI:

10.1594/ecr2017/C-3121

DOI-Link:

https://dx.doi.org/10.1594/ecr2017/C-3121

Fig. 1: Example of a clinical abdominalimage

Fig. 2: Noise image obtained by subtracting two adjacent axial slices

Fig. 3: Identification of the remaining anatomical structures

Fig. 5: 1D global Noise Power Spectrum of abdomen CT images reconstructed using FBP,...

Aims and objectives

Currently, Image Quality (IQ) in Computed Tomography (CT) is generally assessed using uniform and structurally simple phantoms [1]. However, in clinical practice, complex anatomical structures and variable textures influence the resulting IQ of patients’ exams. Therefore, the reference IQ measured on phantom for a given protocol may differ from the actual IQ of clinical images [2-3]. The purpose of this study was to quantify noise texture and magnitude characteristics of clinical CT images obtained with Iterative Reconstructions (IR) techniques.

Methods and materials

An automated tool was developed to extract noise texture and magnitude metrics from 10 chest and abdomen clinical CT-scans (Discovery CT750 HD; GE Healthcare, Wisconsin) performed for oncology follow-up by measuring a global Noise Power Spectrum (NPS). First, a noise image was obtained by subtracting adjacent axial slices ( Fig. 1) from one another to remove the main anatomical structures that tended to be correlated between images (Fig. 2). Then, a segmentation algorithm was applied to pinpoint edges of remaining anatomical structures ( Fig. 3...

Results

Compared to FBP, ASIR30 and ASIR50, MBIR reduced noise magnitude by 31 %, 17 % and 13 % in chest and by 47 %, 34 % and 29 % in abdomen respectively (p < 0.01 each). These noise magnitude reductions were also associated to changes in noise texture: for chest and abdomen, fpeak were significantly lower for MBIR (0.08 and 0.09 mm-1 respectively), ASIR50 (0.13 and 0.14 mm-1 respectively) and ASIR30 (0,16 and 0.18 mm-1 respectively) compared to FBP (0.23 and 0.27 mm-1 respectively; p...

Conclusion

Assessing NPS of clinical CT examinations can demonstrate the reduction of noise magnitude and the changes of noise texture associated to IR. This method could be used to tailor CT protocols according to radiologists’ preferences regarding noise texture and magnitude.

Personal information

References

[1] C. H. McCollough, M. R. Bruesewitz, M. F. McNitt-Gray, K. Bush, T. Ruckdeschel, J. T. Payne, J. A. Brink, and R. K. Zeman, “The phantom portion of the American College of Radiology (ACR) computed tomography(CT) accreditation program: Practical tips, artifact examples, and pitfalls toavoid,” Medical Physics,vol.31, p. 2423–2442, 2004. [2] D. Gomez-Cardona, K. Li, J. Hsieh, M. G. Lubner, P. J. Pickhardt and G.-H. Chen, "Can conclusions drawn from phantom-based image noise assessments be generalized to in vivo studies for the nonlinear model-based iterative...