Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning–Based Reconstruction: A Clinical and Phantom Study

Nagayama, Yasunori; Goto, Makoto; Sakabe, Daisuke; Emoto, Takafumi; Shigematsu, Shinsuke; Oda, Shigeto; Tanoue, Shota; Kidoh, Masafumi; Nakaura, Takeshi; Funama, Yoshinori; Uchimura, Ryutaro; Takada, Sentaro; Hayashi, Hidetaka; Hatemura, Masahiro; Hirai, Toshinori

doi:10.2214/ajr.21.27255

Cited by 29 publications

(31 citation statements)

References 36 publications

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“…Sixteen articles met the selection criteria and were included in this review. Table 1 shows these study characteristics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…Table 1 shows these study characteristics [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Nearly all (14) studies were determined high quality [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], and the lower quality ones were “pure” phantom studies [ 2 , 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…All but one article were published in the last two years, representing that the AI for dose optimization in pediatric radiology has only just become popular [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Nearly all (14) studies were determined high quality [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], and the lower quality ones were “pure” phantom studies [ 2 , 3 ]. All studies used the DL technique [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] and were conducted by 12 groups of researchers from USA ( n = 8) [ 1 , 8 , 9 , 10 , 11 , 12 ...…”

Section: Resultsmentioning

confidence: 99%

“…Radiology is an indispensable part of modern healthcare. However, most of the medical imaging modalities, such as computed tomography (CT), positron emission tomography (PET), and general radiography, use ionizing radiation for image production [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Although the radiation dose involved in these imaging modalities is low (<100 mSv), and their real risk is unclear, some epidemiologic and biologic studies have demonstrated that these radiological examinations can cause cancers [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…For the last few years, artificial intelligence (AI) has been introduced into radiology for radiation dose optimization. Studies have demonstrated its ability in pushing the limit, i.e., able to further reduce the radiation dose but without sacrificing image quality, such as noise and spatial resolution [ 1 , 6 , 9 , 10 , 11 , 12 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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Pediatric evaluations for deep learning CT denoising

Nelson,

Kc,

Badal

et al. 2023

Medical Physics

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BackgroundDeep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup.PurposeTo assess DL CT denoising in pediatric and adult‐sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology.MethodsThe computer simulated IQ phantoms in the framework featured pediatric‐sized versions of standard CatPhan 600 and MITA‐LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard‐sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric‐sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance.ResultsUsing the proposed pediatric‐sized IQ phantom framework, size differences between adult and pediatric‐sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric‐sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms.ConclusionWe developed a framework of using pediatric‐sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.

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The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to Nagy et al.

Mese¹

2023

Pediatr Radiol

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Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning–Based Reconstruction: A Clinical and Phantom Study

Cited by 29 publications

References 36 publications

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Pediatric evaluations for deep learning CT denoising

The potential for photon-counting computed tomography and deep learning to reduce radiation dose in paediatric radiology: reply to Nagy et al.

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