The Influence of X-Ray Tube Current-Time Variations Toward Signal to Noise Ratio (SNR) in Digital Radiography: A Phantom Study

Utami, Asih Puji; Istiqomah, Anisa Nur

doi:10.4028/p-ldt93t

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…X-ray tube current (mA): The quantity of electrons generated by the cathode filament in the cathode assembly of the X-ray tube is determined by the tube current [44]. During X-ray generation, these electrons collide with the anode target of the X-ray tube to generate X-rays photons.…”

Section: How Exposure Factors Affect Image Qualitymentioning

confidence: 99%

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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: How Exposure Factors Affect Image Qualitymentioning

confidence: 99%

“…2. X-ray tube current (mA): The quantity of electrons generated by the cathode filament in the cathode assembly of the X-ray tube is determined by the tube current [44].…”

Section: Phantom Aluminum Platesmentioning

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

“…2. X-ray Tube Curent (mA): The quantity of electrons generated by the cathode filament in the cathode assembly of the X-ray tube is determined by the tube current [43]. During X-ray generation, these electrons collide with the anode target of the Xray tube to generate X-rays photons.…”

Section: How Exposure Factors Affect 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

The Influence of X-Ray Tube Current-Time Variations Toward Signal to Noise Ratio (SNR) in Digital Radiography: A Phantom Study

Cited by 2 publications

References 31 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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