Combined signal averaging and compressed sensing: impact on quality of contrast-enhanced fat-suppressed 3D turbo field-echo imaging for pharyngolaryngeal squamous cell carcinoma

Takumi, Koji; Nagano, Hiroaki; Nakanosono, Ryota; Kumagae, Yuichi; Fukukura, Yoshihiko; Yamada, Takashi

doi:10.1007/s00234-020-02480-2

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…[53][54][55] Takumi et al performed a comparative study of the quality of contrast-enhanced (CE) fat-suppressed 3D gradient-echo imaging with both quantitative and qualitative assessments between images with and without compressed sensing, using a dataset of patients with pharyngolaryngeal squamous cell carcinoma (SCC). 53 They reported that the images obtained with compressed sensing demonstrated better image quality compared to those without, and they concluded that compressed sensing improves the image quality of 3D-based CE-T1WI for the evaluation of pharyngolaryngeal SCC, without requiring additional acquisition time. Kami et al also observed improved image quality of compressed sensingbased CE-3D-T1WI compared to the 2D multi-slice spin echo sequence in patients with maxillofacial lesions.…”

Section: Image Acquisition and Reconstructionmentioning

confidence: 99%

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Fujima,

Kamagata,

Ueda

et al. 2023

MRMS

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Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

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