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Imaging technology

About: Imaging technology is a research topic. Over the lifetime, 1450 publications have been published within this topic receiving 26186 citations.


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Book
27 May 2014
TL;DR: This book describes recent developments, as well as the prospects and challenges in advances in imaging sciences and engineering such as 3D image sensing, 3D holographic imaging, imaging applications for bio-photonics and3D image recognition.
Abstract: Provides a broad overview of advanced multidimensional imaging systems with contributions from leading researchers in the fieldMulti-dimensional Imaging takes the reader from the introductory concepts through to the latest applications of these techniques. Split into 3 parts covering 3D image capture, processing, visualization and display, using 1) a Multi-View Approach and 2.) a Holographic Approach, followed by a 3rd part addressing other 3D systems approaches, applications and signal processing for advanced 3D imaging. This book describes recent developments, as well as the prospects and challenges in advances in imaging sciences and engineering such as 3D image sensing, 3D holographic imaging, imaging applications for bio-photonics and 3D image recognition. Advanced imaging systems incorporate knowledge from various fields. It is a complex technology that combines physics, optics, signal processing, and image capture techniques.Provides a broad overview of advanced multidimensional imaging systems with contributions from leading researchers in the field.Integrates the background, introductory material with new advances in 3D imaging and applications.Covers the most recent technologies such as high speed digital holography, compressive sensing, real-time 3D integral imaging, 3D TV, photon counting imaging.To be available as an enhanced ebook with added functionality of colour films showing the effects of advanced 3D applications such as 3D microscopy, 3D biomedical imaging and 3D for security and defense applications.Acts as a single source reference to the rapidly developing field of 3D imaging technology.Provides supplementary material on a companion website including video clips, examples, numerical simulations, and experimental results to show the theoretical concepts.With contributions from leading researchers from across these fields, Multi-dimensional Imaging is a comprehensive reference for the imaging technology research community.

11 citations

Proceedings ArticleDOI
18 Jun 2007
TL;DR: An array imaging system, dubbed PERIODIC, is presented, capable of exploiting diversities, including subpixel displacement, phase, polarization, and wavelength, to produce superresolution images.
Abstract: An array imaging system, dubbed PERIODIC, is presented, capable of exploiting diversities, including subpixel displacement, phase, polarization, and wavelength, to produce superresolution images. The hardware system and software interface described, and sample results are shown.

11 citations

Journal ArticleDOI
TL;DR: Track µUS enables real-time imaging of the surgical cavity, conferring significant qualitative improvement over conventional ultrasound, thus affecting clinical decision making.
Abstract: High frequency micro-ultrasound (µUS) transducers with central frequencies up to 50 MHz facilitate dynamic visualization of patient anatomy with minimal disruption of the surgical work flow. Micro-ultrasound improves spatial resolution over conventional ultrasound imaging from millimeter to micrometer, but compromises depth penetration. This trade-off is sufficient during an open surgery in which the bone is removed and theultrasound probe can be placed into the surgical cavity. By fusing µUS with pre-operative imaging and tracking the ultrasound probe intra-operatively using our optical topographic imaging technology, we can provide dynamic feedback during surgery, thus affecting clinical decision making. We present our initial experience using high-frequency µUS imaging during spinal procedures. Micro-ultrasound images were obtained in five spinal procedures. Medical rationale for use of µUS was provided for each patient. Surgical procedures were performed using the standard clinical practice with bone removal to facilitate real-time ultrasound imaging of the soft tissue. During surgery, the µUS probe was registered to the pre-operative computed tomography and magnetic resonance images. Images obtained comprised five spinal decompression surgeries (four tumor resections, one cystic synovial mass). Micro-ultrasound images obtained during spine surgery delineated exquisite detailing of the spinal anatomy including white matter and gray matter tracts and nerve roots and allowed accurate assessment of the extent of decompression/tumor resection. In conclusion, tracked µUS enables real-time imaging of the surgical cavity, conferring significant qualitative improvement over conventional ultrasound.

11 citations

Journal ArticleDOI
TL;DR: Combining the high soft-tissue contrast of MRI and the metabolic information derived from PET, PET/MRI bears the potential to be utilized as an accurate and efficient diagnostic tool for primary tumor staging, therapy monitoring and restaging of tumors of the female pelvis and plays a valuable role in the management of targeted tumor therapies in the future.

11 citations

Journal ArticleDOI
TL;DR: It is shown from the experimental results that the residual-in-residual dense block network (RRDBNet) trained with different loss functions performs the best super-resolution for OCT images, and it is demonstrated from the preliminary results that deep learning methods have good generalization and robustness between OCT systems.
Abstract: Optical coherence tomography (OCT) is a noninvasive, high resolution, and real-time imaging technology that has been used in ophthalmology and other medical fields. Limited by the point spread function of OCT system, it is difficult to optimize its spatial resolution only based on hardware. Digital image processing methods, especially deep learning, provide great potential in super-resolving images. In this paper, the matched axial low resolution (LR) and high resolution OCT image pairs from actual OCT imaging are collected to generate the dataset by our home-made spectral domain OCT (SD-OCT) system. Several methods are selected to super-resolve LR OCT images. It is shown from the experimental results that the residual-in-residual dense block network (RRDBNet) trained with different loss functions performs the best super-resolution for OCT images, and it is demonstrated from the preliminary results that deep learning methods have good generalization and robustness between OCT systems. We believe deep learning methods have broad prospects in improving the quality of OCT images.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202312
202224
202190
202091
201984
201879