Optimization of CT protocols using cause-and-effect analysis of outliers

Serna, A.; Ramos, David Martínez; Garcia-Angosto, E.A.; García-Sánchez, Antonio-Javier; Chans, Maria A.; Benedicto-Orovitg, Jose M.; Puchades-Puchades, V.; Mata-Colodro, J.F.

doi:10.1016/j.ejmp.2018.10.010

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Although the SSDE parameter adjusts, a priori, the dose level better than BMI, the unavailability of collecting it in some of the CTs (see Table 1), together with the possibility of discerning gender, have motivated this twofold study. Finally, it is notable that a powerful tool for dose optimization during CT examinations is the analysis of outliers, as we have shown in a previous work [36].…”

Section: Resultsmentioning

confidence: 86%

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

García-Sánchez

Angosto

Llor

et al. 2019

Sensors

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Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.

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Section: Resultsmentioning

confidence: 86%

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

García-Sánchez

Angosto

Llor

et al. 2019

Sensors

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“…Based on these challenges, a lot of work was done during the CT examination to reduce the radiation dose and maintain the quality of the captured images. In [18][19][20], the author proposed a news method to optimize the dose during CT scans. However, sometimes the reduction of radiation leads to a reduction in image quality.…”

Section: State Of the Artmentioning

confidence: 99%

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Takam¹,

Kouanou²,

Samba³

et al. 2021

Artificial Intelligence

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Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.

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“…Se han publicado 4 artículos científicos en diferentes revistas con un índice de impacto JCR de primer cuartil [29] y de segundo cuartil [28,38,39] en los que se ha expuesto: (i) cómo se debe de abordar la implantación de cualquier sistemas de gestión de dosis en una institución sanitaria simple o compleja, (ii) la obtención de valores medios de medición de dosis en la modalidad de Tomografía Computarizada (TC) en distintos hospitales para su mejora y optimización. (iii) identificación de los valores anómalos extraídos de la media y que deberían de ser evaluados para evitar futuras radiaciones innecesarios al paciente y, (iv) control de las radiaciones recibidas, en tiempo real, por los profesionales sanitarios que realizan estas pruebas médicas radiológicas mediante la creación de un dispositivo que ha sido calibrado, certificado y testeado para su uso hospitalario para así ofrecer una completa supervisión y control de las radiaciones ionizantes a cualquier usuario del hospital, sea paciente o profesional sanitario.…”

Section: Resumen En Castellanounclassified

“…This thesis provides a good framework for the prevention of ionizing radiation. This doctoral thesis presents a methodology for the implementation, improvement and optimisation of a system for managing patient doses in radiological medical tests at a corporate level, as well as the control and measurement of the doses received by the healthcare professionals who carry out these tests by creating a low-cost, flexible measuring device that can be connected to the corporate dose management systems of any institution that implements it.Four scientific articles have been published in different journals with a JCR impact index of first quartile [29] and second quartile [28,38,39] in which it has been exposed: (i) how to approach the introduction of any dose management system in a simple or complex healthcare institution, (ii) the collection of average dose measurement values in the modality of Computed Tomography (CT) in different hospitals for its improvement and optimization. (iii) identification of the outliers extracted from the media and which should be evaluated in order to avoid future unnecessary radiation to the patient and, (iv) monitoring of the radiation received, in real time, by healthcare professionals who perform these radiological medical tests by creating a device that has been calibrated, certified and tested for hospital use in order to offer complete monitoring and control of ionizing radiation to any hospital user, patient or healthcare professional.…”

mentioning

confidence: 99%

“…Four scientific articles have been published in different journals with a JCR impact index of first quartile [29] and second quartile [28,38,39] in which it has been exposed: (i) how to approach the introduction of any dose management system in a simple or complex healthcare institution, (ii) the collection of average dose measurement values in the modality of Computed Tomography (CT) in different hospitals for its improvement and optimization. (iii) identification of the outliers extracted from the media and which should be evaluated in order to avoid future unnecessary radiation to the patient and, (iv) monitoring of the radiation received, in real time, by healthcare professionals who perform these radiological medical tests by creating a device that has been calibrated, certified and tested for hospital use in order to offer complete monitoring and control of ionizing radiation to any hospital user, patient or healthcare professional.…”

mentioning

confidence: 99%

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Contribución a la dosimetría personal en pruebas médicas radiológicas para entornos regionales

Angosto¹,

Ángel²

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The damage caused by ionizing radiation to organs and tissues due to radiological medical tests is a risk factor to the population that must be measured and evaluated by any health institution. This thesis provides a good framework for the prevention of ionizing radiation. This doctoral thesis presents a methodology for the implementation, improvement and optimisation of a system for managing patient doses in radiological medical tests at a corporate level, as well as the control and measurement of the doses received by the healthcare professionals who carry out these tests by creating a low-cost, flexible measuring device that can be connected to the corporate dose management systems of any institution that implements it.Four scientific articles have been published in different journals with a JCR impact index of first quartile [29] and second quartile [28,38,39] in which it has been exposed: (i) how to approach the introduction of any dose management system in a simple or complex healthcare institution, (ii) the collection of average dose measurement values in the modality of Computed Tomography (CT) in different hospitals for its improvement and optimization. (iii) identification of the outliers extracted from the media and which should be evaluated in order to avoid future unnecessary radiation to the patient and, (iv) monitoring of the radiation received, in real time, by healthcare professionals who perform these radiological medical tests by creating a device that has been calibrated, certified and tested for hospital use in order to offer complete monitoring and control of ionizing radiation to any hospital user, patient or healthcare professional.

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Optimization of CT protocols using cause-and-effect analysis of outliers

Cited by 10 publications

References 13 publications

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Contribución a la dosimetría personal en pruebas médicas radiológicas para entornos regionales

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