A new method in diffraction tomography for medical image reconstruction

1993 ◽  
Author(s):  
Terence K. K Chan
Keyword(s):  
Image Reconstruction ◽  
Medical Image ◽  
New Method ◽  
Diffraction Tomography ◽  
Medical Image Reconstruction

scholarly journals A Web Simulation of Medical Image Reconstruction and Processing as an Educational Tool

2014 ◽  
Vol 28 (1) ◽  
pp. 24-31 ◽  
Author(s):  
Dimitrios Papamichail ◽  
Evaggelos Pantelis ◽  
Panagiotis Papagiannis ◽  
Pantelis Karaiskos ◽  
Evangelos Georgiou
Keyword(s):  
Image Reconstruction ◽  
Medical Image ◽  
Educational Tool ◽  
Medical Image Reconstruction

Research and Developments in Medical Image Reconstruction Methods and Its Applications

2017 ◽  
pp. 491-535
Author(s):  
Shailendra Tiwari ◽  
Rajeev Srivastava
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Medical Image ◽  
Ct Scanner ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Development Technology ◽  
Medical Image Reconstruction

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures. In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.

Learning deconvolutional deep neural network for high resolution medical image reconstruction

2018 ◽  
Vol 468 ◽  
pp. 142-154 ◽  
Author(s):  
Hui Liu ◽  
Jun Xu ◽  
Yan Wu ◽  
Qiang Guo ◽  
Bulat Ibragimov ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Image Reconstruction ◽  
Medical Image ◽  
Deep Neural Network ◽  
Medical Image Reconstruction

Medical Image Compressed Sensing Reconstruction

2014 ◽  
Vol 556-562 ◽  
pp. 4835-4838 ◽  
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu ◽  
Yu Ming Sun
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Medical Image ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Sparse Reconstruction ◽  
Image Reconstruction Algorithm ◽  
Gradient Direction ◽  
Medical Image Reconstruction ◽  
Negative Gradient

In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

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