Quantitative optimization of X-ray image acquisition with respect to object thickness and anode voltage—A comparison using different converter screens

Nezhad, Nazila Saeid; Ullherr, Maximilian; Zabler, Simon

doi:10.1016/j.nima.2022.166472

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Numerous factors influence the quality of images produced by digital X-ray radiography techniques. Notable amongst these factors are the setup used, the energy level (kilovoltage kV) [27,28], the tube current (mA), the exposure time [29], and the focal spot size of the X-ray tube used for acquisition [30]. Additionally, the detector properties, the calibration procedure, and the material properties of the inspected component could also affect the quality of acquired images.…”

Section: Radiographic Image Qualitymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

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In response to the growing inspection demand exerted by process automation in component manufacturing, non-destructive testing (NDT) continues to explore automated approaches that utilize deep-learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep-learning models. This study investigated the influence of two image-quality parameters, namely signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), on the performance of a U-net deep-learning semantic segmentation model. Input images were acquired with varying combinations of exposure factors, such as kilovoltage, milli-ampere, and exposure time, which altered the resultant radiographic image quality. The data were sorted into five different datasets according to their measured SNR and CNR values. The deep-learning model was trained five distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection-over-union (IoU) metric of 0.9594 on test data of the same category but dropped to 0.5875 when tested on lower CNR test data. The result of this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters in order to enhance the performance of deep-learning segmentation models for NDT digital X-ray radiography applications.

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Section: Radiographic Image Qualitymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Hena

Wei

Ibarra-Castanedo

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Numerous factors influence the quality of images produced by digital X-ray radiography techniques. Notable amongst these factors are the setup used, energy level (kilovoltage kV) [28,29], the tube current (mA), the exposure time [30], and focal spot size of the X-ray tube used for acquisition [31]. Additionally, detector properties, calibration procedure and the material properties of the inspected component could also affect the quality of acquired images.…”

Section: Radiographic Image Qualitymentioning

confidence: 99%

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

Hena¹,

Wei²,

Ibarra-Castanedo³

et al. 2023

Preprint

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In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

show abstract

Quantitative optimization of X-ray image acquisition with respect to object thickness and anode voltage—A comparison using different converter screens

Cited by 2 publications

References 12 publications

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters—An Experimental Study

Deep Learning Neural Network Performance on NDT Digital X-ray Radiography Images: Analyzing the Impact of Image Quality Parameters - An Experimental Study

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