Interventional X-ray quality measure based on a psychovisual detectability model

Transcription

1 Interventional X-ray quality measure based on a psychovisual detectability model Asli Kumcu, Benhur Ortiz-Jaramillo, Ljiljana Platisa, Bart Goossens, Wilfried Philips iminds-telin-ipi, Ghent University, Belgium CHO in Multi-Slice Images

2 Outline Interventional X-ray quality measure based on a psychovisual detectability model Background Design of interventional X-ray quality measure Results Conclusion & Future work 2

3 Clinical purpose of interventional X-ray Blockage of artery Stent opens artery Angiography procedure [1] [2] [3] [1] [2] [3] 3

4 Interventional X-ray dose Clinical goal: Reduce dose to patient/staff (increases noise, affects contrast) Keep sufficient image quality State of the art: Assess dose to detector and use pre-programmed curves to modify X-ray output [4] Goal of this work: Assess perceived task-based image quality per acquisition (patient / anatomy / view) in real-time [4] AJ Gislason, et al., Allura Xper Cardiac System Implementation of Automatic Dose Rate Control, Philips Technical report,

5 Interventional X-ray quality measure Task: Visibility (detectability) of vessels Metric estimates: Detection probability Quality Figure of merit: Ratio of # pixels with partial detectability to # all detectable pixels Quality FOM: 86% Clinical images acquired on Philips Allura with 100% dose and 50% dose with denoising 5

6 Detection probability Aim for dose which results in image parameters estimated to have 99.5% detectability P(det) =ƒ(contrast ratio, noise, background intensity) Probability of detecting object P(det) (%) Quality too low (increase dose) X Parameter (e.g. contrast ratio, CR) Quality too high (reduce dose) Target: minimum contrast ratio (lowest dose) resulting in 99.5% detectability 6

7 Design of measure Interventional sequence acquisition Estimate image quality attributes Estimate detectability of clinical targets Psychovisual target detectability model Acquisition Dose feedback loop Quality model Quality target 7

8 Psychovisual target detectability model human experiments 1 up / 1 down staircase procedure Target Noise σ Noise types Local Background (cd/m 2 ) S loan σ=0 Static noise 59 L etters σ 1 = σ 2 =0.087 Dynamic noise

9 Psychovisual target detectability model results [5] Detectability reduced in higher noise and darker backgrounds Local background luminance (L LB ) σ 1, L LB = 59 cd/m 2 σ 2, L LB = 254 cd/m 2 [5] A. Kumcu, et al., Effects of static and dynamic image noise and background luminance on letter contrast threshold, QoMEX

10 Estimate image quality attributes Interventional sequence Luminance domain Detectability Contrast ratio Noise (σ) Background luminance Psychovisual target detectability model 10

11 Estimate image quality attributes Contrast & background intensity Weber contrast computed from mean foreground and background intensity using local content informationbased contrast ratio [6] or shearlet-based [7] contrast ratio Noise variance Spatial noise estimator, extension of [8]: incorporates noise model which takes into account relationship between pixel intensity and noise [6] B. Ortiz, et al, Computing contrast ratio in medical images using local content information, MIPS XVI conference 2015 [7] B. Goossens, et al., "Efficient Design of a Low Redundant Discrete Shearlet Transform, " in Proc International Workshop on Local and Non-Local Approximation in Image Processing (LNLA2009), August 19-21, 2009, Tuusula, Finland, p [8] V. Zlokolica, et al, "Noise estimation for video processing based on spatial-temporal gradient histograms," IEEE Signal Processing Letters, 2006, 13,

12 Results interventional neurology (DSA) 100% dose 50% dose + denoising Frame Contrast ratio Frame Contrast ratio Noise Detectability Noise Detectability Quality FOM: 82.5% 83.7% Human scores from VGA experiment: 100% for both sequences 12

13 Results interventional cardiology 100% dose 50% dose + denoising Frame Contrast ratio Frame Contrast ratio Noise Detectability Noise Detectability Quality FOM: 86% 87% 13

14 Needed contrast DECREASE (%) Needed contrast DECREASE (%) needed needed Needed contrast INCREASE (%) Needed contrast INCREASE (%) Results alternative quality FOM Contrast too high: contrast decrease needed Contrast too low: contrast increase needed High dose sequence Lower dose sequence 14

15 Limitations Signal model 1 (complex) frequency Consider evaluating additional signal frequencies with vessel-like objects or characterize entire CSF White noise Consider extending psychovisual experiments to complex backgrounds 2 observers Follow-up psychovisual study planned with additional observers and parameters 15

16 Conclusion & Future work Task-based measure for real-time quality assessment in interventional X-ray Currently index is pixel-based go to object-based index in the future Include target motion in model Extended comparison to existing vision models for dynamic noise Extended validation with observers: effect of dose 16

17 Acknowledgments This work was supported by the Eniac PANORAMA project Thanks to project partners Philips and University of Leeds, and cardiologists at UZGent 17

18 Thank you! Questions? Interventional X-ray quality measure based on a psychovisual detectability model Asli Kumcu, Benhur Ortiz-Jaramillo, Ljiljana Platisa, Bart Goossens, Wilfried Philips iminds-telin-ipi, Ghent University, Belgium CHO in Multi-Slice Images