2023
DOI: 10.1002/mp.16405
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Mitigation of motion‐induced artifacts in cone beam computed tomography using deep convolutional neural networks

Abstract: BackgroundCone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image‐guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto‐segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep‐learning based appro… Show more

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Cited by 3 publications
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“…Today, DL has found its way from research into our daily lives in a multitude of applications [4,5], such as Internet searches, translation apps, face recognition and augmentation on social media, speech interfaces, digital art generation, and chatbots. It can achieve enormous good, e.g., by preventing secondary cancer through improved medical imaging [6]. Other recent advances have further demonstrated the astonishing capacities of DL: generative AI models caught public attention by producing striking images from text prompts (e.g., 'DALL-E 2' and its openaccess brother 'Stable Diffusion', as well as 'Midjourney' [7][8][9]), while generalist models (e.g., 'GATO' [10]), and the unprecedented utility of multimodal 'large language models' (LLMs), create the impression that we are getting closer to building so-called 'artificial general intelligence' (AGI): an engineered human-like or even superhuman intelligence [11,12].…”
Section: Introductionmentioning
confidence: 99%
“…Today, DL has found its way from research into our daily lives in a multitude of applications [4,5], such as Internet searches, translation apps, face recognition and augmentation on social media, speech interfaces, digital art generation, and chatbots. It can achieve enormous good, e.g., by preventing secondary cancer through improved medical imaging [6]. Other recent advances have further demonstrated the astonishing capacities of DL: generative AI models caught public attention by producing striking images from text prompts (e.g., 'DALL-E 2' and its openaccess brother 'Stable Diffusion', as well as 'Midjourney' [7][8][9]), while generalist models (e.g., 'GATO' [10]), and the unprecedented utility of multimodal 'large language models' (LLMs), create the impression that we are getting closer to building so-called 'artificial general intelligence' (AGI): an engineered human-like or even superhuman intelligence [11,12].…”
Section: Introductionmentioning
confidence: 99%