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A visibility overshoot index for interventional x-ray image quality assessment

Asli Kumcu (UGent) , Ljiljana Platisa (UGent) , Bart Goossens (UGent) , Amber J. Gislason-Lee, Andrew G. Davies, Gerard Schouten, Dimitri Buytaert (UGent) , Klaus Bacher (UGent) and Wilfried Philips (UGent)
(2023) Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment. In Proceedings of SPIE 12467.
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Abstract
Dose reduction remains an important goal in interventional x-ray. We propose an image quality (IQ) measure called the visibility overshoot index. Given a patient image and a specified clinical task, the index quantifies the maximum acceptable dose reduction. The dose control system can then use this information to deliver the minimum dose necessary for detection of clinical signals, reducing unnecessary radiation exposure. We developed an experimental visual model to estimate signal detectability as a function of image features such as noise and signal contrast. The model is used to find a feature’s threshold–the maximum change in noise or signal contrast where signal detectability remains possible. An automated algorithm measures the magnitudes of these features on a frame. Visibility overshoot is expressed in terms of the image features: the noise overshoot and contrast overshoot indices are the ratio of the threshold to measured noise/contrast. The indices demonstrate good agreement with detector dose, channelized hotelling observer results, and clinicians’ judgments. In our study of a cylindrical object phantom acquired at seven dose levels, we found that the noise overshoot index is linearly related to the square root of detector dose and the CHO detectability index, with Pearson correlation 0.995–1.0 for signals 1-4 mm diameter. For interventional cardiology and neurology sequences acquired at standard and 25–30% dose, the index and clinicians rank IQ similarly. Results on the phantom suggest at least 15% dose reduction could be achieved in fluoroscopy mode. Our patient-specific IQ approach could bring additional dose savings to clinical practice.

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MLA
Kumcu, Asli, et al. “A Visibility Overshoot Index for Interventional X-Ray Image Quality Assessment.” Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment, edited by Yan Chen and Claudia R. Mello-Thoms, vol. 12467, SPIE, 2023, doi:10.1117/12.2652548.
APA
Kumcu, A., Platisa, L., Goossens, B., Gislason-Lee, A. J., Davies, A. G., Schouten, G., … Philips, W. (2023). A visibility overshoot index for interventional x-ray image quality assessment. In Y. Chen & C. R. Mello-Thoms (Eds.), Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment (Vol. 12467). https://doi.org/10.1117/12.2652548
Chicago author-date
Kumcu, Asli, Ljiljana Platisa, Bart Goossens, Amber J. Gislason-Lee, Andrew G. Davies, Gerard Schouten, Dimitri Buytaert, Klaus Bacher, and Wilfried Philips. 2023. “A Visibility Overshoot Index for Interventional X-Ray Image Quality Assessment.” In Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment, edited by Yan Chen and Claudia R. Mello-Thoms. Vol. 12467. SPIE. https://doi.org/10.1117/12.2652548.
Chicago author-date (all authors)
Kumcu, Asli, Ljiljana Platisa, Bart Goossens, Amber J. Gislason-Lee, Andrew G. Davies, Gerard Schouten, Dimitri Buytaert, Klaus Bacher, and Wilfried Philips. 2023. “A Visibility Overshoot Index for Interventional X-Ray Image Quality Assessment.” In Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment, ed by. Yan Chen and Claudia R. Mello-Thoms. Vol. 12467. SPIE. doi:10.1117/12.2652548.
Vancouver
1.
Kumcu A, Platisa L, Goossens B, Gislason-Lee AJ, Davies AG, Schouten G, et al. A visibility overshoot index for interventional x-ray image quality assessment. In: Chen Y, Mello-Thoms CR, editors. Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment. SPIE; 2023.
IEEE
[1]
A. Kumcu et al., “A visibility overshoot index for interventional x-ray image quality assessment,” in Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment, San Diego, CA, 2023, vol. 12467.
@inproceedings{01GY7BHJKFPX1VHB2W15XY1DEA,
  abstract     = {{Dose reduction remains an important goal in interventional x-ray. We propose an image quality (IQ) measure called the visibility overshoot index. Given a patient image and a specified clinical task, the index quantifies the maximum acceptable dose reduction. The dose control system can then use this information to deliver the minimum dose necessary for detection of clinical signals, reducing unnecessary radiation exposure. We developed an experimental visual model to estimate signal detectability as a function of image features such as noise and signal contrast. The model is used to find a feature’s threshold–the maximum change in noise or signal contrast where signal detectability remains possible. An automated algorithm measures the magnitudes of these features on a frame. Visibility overshoot is expressed in terms of the image features: the noise overshoot and contrast overshoot indices are the ratio of the threshold to measured noise/contrast. The indices demonstrate good agreement with detector dose, channelized hotelling observer results, and clinicians’ judgments. In our study of a cylindrical object phantom acquired at seven dose levels, we found that the noise overshoot index is linearly related to the square root of detector dose and the CHO detectability index, with Pearson correlation 0.995–1.0 for signals 1-4 mm diameter. For interventional cardiology and neurology sequences acquired at standard and 25–30% dose, the index and clinicians rank IQ similarly. Results on the phantom suggest at least 15% dose reduction could be achieved in fluoroscopy mode. Our patient-specific IQ approach could bring additional dose savings to clinical practice.}},
  articleno    = {{124670T}},
  author       = {{Kumcu, Asli and Platisa, Ljiljana and Goossens, Bart and Gislason-Lee, Amber J. and Davies, Andrew G. and Schouten, Gerard and Buytaert, Dimitri and Bacher, Klaus and Philips, Wilfried}},
  booktitle    = {{Medical Imaging 2023 : Image Perception, Observer Performance, and Technology Assessment}},
  editor       = {{Chen, Yan and Mello-Thoms, Claudia R.}},
  isbn         = {{9781510660403}},
  issn         = {{2410-9045}},
  language     = {{eng}},
  location     = {{San Diego, CA}},
  pages        = {{16}},
  publisher    = {{SPIE}},
  title        = {{A visibility overshoot index for interventional x-ray image quality assessment}},
  url          = {{http://doi.org/10.1117/12.2652548}},
  volume       = {{12467}},
  year         = {{2023}},
}
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