Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)

2006 ◽  
Author(s):  
Cynthia McCollough ◽  
Jie Zhang ◽  
Michael Bruesewitz ◽  
Brian Bartholmai
Keyword(s):  
Image Reconstruction ◽  
Lung Tissue ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Ct Image Reconstruction ◽  
Tissue Research ◽  
Research Consortium ◽  
Image Reconstruction Algorithms

scholarly journals A biological phantom for evaluation of CT image reconstruction algorithms

2014 ◽  
Author(s):  
J. Cammin ◽  
G. S. K. Fung ◽  
E. K. Fishman ◽  
J. H. Siewerdsen ◽  
J. W. Stayman ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Ct Image Reconstruction ◽  
Image Reconstruction Algorithms

scholarly journals Deep Learning CT Image Reconstruction in Clinical Practice

2020 ◽  
Author(s):  
Clemens Arndt ◽  
Felix Güttler ◽  
Andreas Heinrich ◽  
Florian Bürckenmeyer ◽  
Ioannis Diamantis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Emergency Services ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Key Points ◽  
Ct Image Reconstruction ◽  
Medical Subspecialty ◽  
Image Reconstruction Algorithms

Background Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL). Method This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed. Results and Conclusion DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches. Key Points:  Citation Format

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