CT dose management of adult patients with unknown body weight using an effective diameter

Fukunaga, Masao; Matsubara, Kosuke; Ichikawa, Shota; Matsui, Hideki; Yamamoto, Hiroyuki; Miyati, Tosiaki

doi:10.1016/j.ejrad.2020.109483

Cited by 13 publications

(10 citation statements)

References 7 publications

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“…Generally, the accuracy of a deep-learning neural network model is largely dependent on the size of high-quality initial training datasets 22 – 24 . Conversely, a previous study by Fukunaga et al has shown a similar tendency to the present study, in which a better correlation between body weight and effective diameter was found in chest CT compared to abdominal CT 2 . Boos et al also revealed that the effective diameter had a significantly better correlation with weight than with body mass index in chest CT scans, whereas it had a significantly better correlation with body mass index than with weight in abdominal CT scans 3 .…”

Section: Discussionsupporting

confidence: 89%

“…Body weight is an important parameter in the field of radiology as the dose of contrast medium 1 , management of radiation dose 2 , 3 , or selection of reduced tube voltage 4 is strongly related to body weight. However, we cannot always determine the accurate body weight at the time of computed tomography (CT) scanning, especially in emergency care.…”

Section: Introductionmentioning

confidence: 99%

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A deep-learning method using computed tomography scout images for estimating patient body weight

Ichikawa

Hamada

Sugimori

2021

Sci Rep

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Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scanning, especially in emergency care. Time-efficient methods to estimate body weight with high accuracy before diagnostic CT scans currently do not exist. In this study, on the basis of 1831 chest and 519 abdominal CT scout images with the corresponding body weights, we developed and evaluated deep-learning models capable of automatically predicting body weight from CT scout images. In the model performance assessment, there were strong correlations between the actual and predicted body weights in both chest (ρ = 0.947, p < 0.001) and abdominal datasets (ρ = 0.869, p < 0.001). The mean absolute errors were 2.75 kg and 4.77 kg for the chest and abdominal datasets, respectively. Our proposed method with deep learning is useful for estimating body weights from CT scout images with clinically acceptable accuracy and potentially could be useful for determining the contrast medium dose and CT dose management in adult patients with unknown body weight.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

A deep-learning method using computed tomography scout images for estimating patient body weight

Ichikawa

Hamada

Sugimori

2021

Sci Rep

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“…One possible reason was that the dataset size of abdominal scout images was less compared to that of chest scout images. Conversely, a previous study by Fukunaga et al has shown a similar tendency to the present study, in which a better correlation between body weight and effective diameter was found in chest CT compared to abdominal CT [2] . Surprisingly, it seems that body weight should be therefore estimated not from the abdominal region but from the chest region, if the scan range includes the chest region.…”

Section: Discussionsupporting

confidence: 89%

“…Body weight is an important parameter in the eld of radiology as the dose of contrast media [1] or the management of radiation dose [2] is strongly related to body weight. However, we cannot always determine the accurate body weight at the time of computed tomography (CT) scan, especially in emergency care.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Estimating Patient Body Weight Using Computed Tomography Scout Images

Ichikawa

Hamada

Sugimori

2021

Preprint

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Body weight is an indispensable parameter for determination of the dose of contrast media, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT) scan, especially in emergency care. In this study, based on 1831 chest and 519 abdominal CT scout images with the corresponding body weight, we developed deep learning models capable of automatically predicting body weight from CT scout images. In the assessment of model performance, there were strong correlations between the actual and predicted body weight both in chest (r = 0.955, p < 0.001) and abdominal datasets (r = 0.901, p < 0.001). Mean absolute errors were 2.75 kg and 4.77 kg for chest and abdominal datasets, respectively. The proposed method was able to predict body weight from chest and abdominal CT scout images with high accuracy. Body weight derived from our method may show potential use in determination of the dose of contrast media and management of radiation dose in adult patients with unknown body weight.

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“…In addition, patient size and attenuation information extracted from the localizer image can be used to optimize the irradiation parameters according to the desired image quality and radiation dose. Knowledge of the patient's body attenuation or shape characteristics prior to spiral CT scanning is helpful in effectively optimizing spiral CT scanning to achieve automatic tunning of size-adapted exposure parameters, such as automatic tube current modulation (TCM) and automatic kVp selection [14][15][16][17]. Ichikawa et al trained a deep neural network to correlate the scout image and patient's weight with an error close to 3 kg [18].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

Salimi

Shiri

Akhavanallaf

et al. 2022

European Journal of Radiology

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CT dose management of adult patients with unknown body weight using an effective diameter

Cited by 13 publications

References 7 publications

A deep-learning method using computed tomography scout images for estimating patient body weight

A deep-learning method using computed tomography scout images for estimating patient body weight

A Deep Learning Method for Estimating Patient Body Weight Using Computed Tomography Scout Images

Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization

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