Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN

Liu, Yunbi; Liu, Mingxia; Xi, Yuhua; Qin, Genggeng; Shen, Dinggang; Yang, Wei

doi:10.1007/978-3-030-59713-9_65

Cited by 4 publications

(8 citation statements)

References 18 publications

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“…The most widely studied subject in the image generation lit-erature is image enhancement. Several researchers investigated bone suppression (Liu et al, 2020a;Matsubara et al, 2020;Zarshenas et al, 2019;Gozes and Greenspan, 2020;Lin et al, 2020;Zhou et al, 2020b) and lung enhancement Gozes and Greenspan, 2020) techniques to improve image interpretability. A number of works (Liu et al, 2020a;Zhou et al, 2020b) employed GANs to generate bone-suppressed images.…”

Section: Image Generationmentioning

confidence: 99%

“…Several researchers investigated bone suppression (Liu et al, 2020a;Matsubara et al, 2020;Zarshenas et al, 2019;Gozes and Greenspan, 2020;Lin et al, 2020;Zhou et al, 2020b) and lung enhancement Gozes and Greenspan, 2020) techniques to improve image interpretability. A number of works (Liu et al, 2020a;Zhou et al, 2020b) employed GANs to generate bone-suppressed images. For example, Liu et al (2020a) employed GANs and leveraged additional input to the generator to guide the dual-energy subtraction (DES) soft-tissue image generation process.…”

Section: Image Generationmentioning

confidence: 99%

“…A number of works (Liu et al, 2020a;Zhou et al, 2020b) employed GANs to generate bone-suppressed images. For example, Liu et al (2020a) employed GANs and leveraged additional input to the generator to guide the dual-energy subtraction (DES) soft-tissue image generation process. In this study, bones, edges and clavicles were first segmented by a CNN model, and the resulting edge maps were fed to the generator with the original CXR image as prior knowledge.…”

Section: Image Generationmentioning

confidence: 99%

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Deep Learning for Chest X-ray Analysis: A Survey

Sogancioglu,

Çallı,

van Ginneken

et al. 2021

Preprint

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Recent advances in deep learning have led to a promising performance in many medical image analysis tasks. As the most commonly performed radiological exam, chest radiographs are a particularly important modality for which a variety of applications have been researched. The release of multiple, large, publicly available chest X-ray datasets in recent years has encouraged research interest and boosted the number of publications. In this paper, we review all studies using deep learning on chest radiographs, categorizing works by task: image-level prediction (classification and regression), segmentation, localization, image generation and domain adaptation. Commercially available applications are detailed, and a comprehensive discussion of the current state of the art and potential future directions are provided.

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Section: Image Generationmentioning

confidence: 99%

Section: Image Generationmentioning

confidence: 99%

Section: Image Generationmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Chest X-ray Analysis: A Survey

Sogancioglu,

Çallı,

van Ginneken

et al. 2021

Preprint

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“…Moreover, the AI-DES can arbitrarily select the enhanced tissue by adjusting the weight of the image subtraction process. Consequently, our AI-DES has the potential to provide more enriched information than existing methods, where bone-suppressed images are directly produced [23][24][25][26][27][28][29][30][31]. For instance, Liu et al, developed an AI model to generate DES-like soft tissue images, but their approach primarily focused on suppressing bone tissues, making it difficult to selectively enhance specific tissues [25].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, our AI-DES has the potential to provide more enriched information than existing methods, where bone-suppressed images are directly produced [23][24][25][26][27][28][29][30][31]. For instance, Liu et al, developed an AI model to generate DES-like soft tissue images, but their approach primarily focused on suppressing bone tissues, making it difficult to selectively enhance specific tissues [25]. Similarly, Bae et al, developed a generative adversarial network (GAN)-based bone suppression model for chest radiography, and demonstrated that its ability to detect pulmonary lesions is comparable to that of a DES technique [26].…”

Section: Introductionmentioning

confidence: 99%

Development of Artificial Intelligence-Based Dual-Energy Subtraction for Chest Radiography

Yamazaki,

Koshida,

Tanaka

et al. 2023

Applied Sciences

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Recently, some facilities have utilized the dual-energy subtraction (DES) technique for chest radiography to increase pulmonary lesion detectability. However, the availability of the technique is limited to certain facilities, in addition to other limitations, such as increased noise in high-energy images and motion artifacts with the one-shot and two-shot methods, respectively. The aim of this study was to develop artificial intelligence-based DES (AI–DES) technology for chest radiography to overcome these limitations. Using a trained pix2pix model on clinically acquired chest radiograph pairs, we successfully converted 130 kV images into virtual 60 kV images that closely resemble the real images. The averaged peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) between virtual and real 60 kV images were 33.8 dB and 0.984, respectively. We also achieved the production of soft-tissue- and bone-enhanced images using a weighted image subtraction process with the virtual 60 kV images. The soft-tissue-enhanced images exhibited sufficient bone suppression, particularly within lung fields. Although the bone-enhanced images contained artifacts on and around the lower thoracic and lumbar spines, superior sharpness and noise characteristics were presented. The main contribution of our development is its ability to provide selectively enhanced images for specific tissues using only high-energy images obtained via routine chest radiography. This suggests the potential to improve the detectability of pulmonary lesions while addressing challenges associated with the existing DES technique. However, further improvements are necessary to improve the image quality.

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