Three-dimensional constructive interference in steady state (3D CISS) is a steady-state gradient-echo sequence in magnetic resonance imaging (MRI) that has been used in an increasing number of applications in the study of brain disease in recent years. Owing to the very high spatial resolution, the strong hyperintensity of the cerebrospinal fluid signal and the high contrast-to-noise ratio, 3D CISS can be employed in a wide range of scenarios, ranging from the traditional study of cranial nerves, the ventricular system, the subarachnoid cisterns and related pathology to more recently discussed applications, such as the fundamental role it can assume in the setting of acute ischemic stroke, vascular malformations, infections and several brain tumors. In this review, after briefly summarizing its fundamental physical principles, we examine in detail the various applications of 3D CISS in brain imaging, providing numerous representative cases, so as to help radiologists improve its use in imaging protocols in daily clinical practice.
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http://dx.doi.org/10.3390/biomedicines10112997 | DOI Listing |
Sensors (Basel)
January 2025
Shanghai Film Academy, Shanghai University, Shanghai 200072, China.
The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images.
View Article and Find Full Text PDFMolecules
January 2025
State Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China.
Niobium pentoxide (T-NbO) is a promising anode material for dual-ion batteries due to its high lithium capacity and fast ion storage and release mechanism. However, T-NbO suffers from the disadvantages of poor electrical conductivity and fast cycling capacity decay. Herein, a nitrogen-doped three-dimensional porous carbon (RMF) was prepared for loading niobium pentoxide to construct a composite system with excellent electrochemical performance.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Division of Molecular & Regenerative Prosthodontics, Tohoku University Graduate School of Dentistry, Sendai 980-8575, Japan.
Tooth/skeletal dysplasia, such as hypophosphatasia (HPP), has been extensively studied. However, there are few definitive treatments for these diseases owing to the lack of an in vitro disease model. Cells differentiated from patient-derived induced pluripotent stem cells (iPSCs) demonstrate a pathological phenotype.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.
Due to advances in big data technology, deep learning, and knowledge engineering, biological sequence visualization has been extensively explored. In the post-genome era, biological sequence visualization enables the visual representation of both structured and unstructured biological sequence data. However, a universal visualization method for all types of sequences has not been reported.
View Article and Find Full Text PDFBeijing Da Xue Xue Bao Yi Xue Ban
February 2025
Center for Digital Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digi-tal Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Research Center of Engineering and Technology for Computerized Dentistry, Beijing 100081, China.
Objective: To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data, by utilizing a dynamic graph-based registration network model (maxillofacial dynamic graph registration network, MDGR-Net), and to provide a valuable reference for digital design and analysis in clinical dental applications.
Methods: Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022. Through data augmentation, a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.
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