Correction of computed tomography motion artifacts using pixel-specific back-projection

1996 ◽  
Vol 15 (3) ◽  
pp. 333-342 ◽  
Author(s):  
C.J. Ritchie ◽  
C.R. Crawford ◽  
J.D. Godwin ◽  
K.F. King ◽  
Yongmin Kim
Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1122
Author(s):  
Jessica Graef ◽  
Bernd A. Leidel ◽  
Keno K. Bressem ◽  
Janis L. Vahldiek ◽  
Bernd Hamm ◽  
...  

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.


Author(s):  
Xiaoyu Sun ◽  
Feng Huang ◽  
Guanjun Lai ◽  
Dan Yu ◽  
Bin Zhang ◽  
...  

2014 ◽  
Vol 4 ◽  
pp. 38 ◽  
Author(s):  
Lukas Ebner ◽  
Felix Knobloch ◽  
Adrian Huber ◽  
Julia Landau ◽  
Daniel Ott ◽  
...  

Objective: The aim of the present study was to evaluate a dose reduction in contrast-enhanced chest computed tomography (CT) by comparing the three latest generations of Siemens CT scanners used in clinical practice. We analyzed the amount of radiation used with filtered back projection (FBP) and an iterative reconstruction (IR) algorithm to yield the same image quality. Furthermore, the influence on the radiation dose of the most recent integrated circuit detector (ICD; Stellar detector, Siemens Healthcare, Erlangen, Germany) was investigated. Materials and Methods: 136 Patients were included. Scan parameters were set to a thorax routine: SOMATOM Sensation 64 (FBP), SOMATOM Definition Flash (IR), and SOMATOM Definition Edge (ICD and IR). Tube current was set constantly to the reference level of 100 mA automated tube current modulation using reference milliamperes. Care kV was used on the Flash and Edge scanner, while tube potential was individually selected between 100 and 140 kVp by the medical technologists at the SOMATOM Sensation. Quality assessment was performed on soft-tissue kernel reconstruction. Dose was represented by the dose length product. Results: Dose-length product (DLP) with FBP for the average chest CT was 308 mGy*cm ± 99.6. In contrast, the DLP for the chest CT with IR algorithm was 196.8 mGy*cm ± 68.8 (P = 0.0001). Further decline in dose can be noted with IR and the ICD: DLP: 166.4 mGy*cm ± 54.5 (P = 0.033). The dose reduction compared to FBP was 36.1% with IR and 45.6% with IR/ICD. Signal-to-noise ratio (SNR) was favorable in the aorta, bone, and soft tissue for IR/ICD in combination compared to FBP (the P values ranged from 0.003 to 0.048). Overall contrast-to-noise ratio (CNR) improved with declining DLP. Conclusion: The most recent technical developments, namely IR in combination with integrated circuit detectors, can significantly lower radiation dose in chest CT examinations.


2003 ◽  
Vol 30 (9) ◽  
pp. 2465-2474 ◽  
Author(s):  
U. van Stevendaal ◽  
J.-P. Schlomka ◽  
A. Harding ◽  
M. Grass

Author(s):  
Genwei Ma ◽  
Xing Zhao ◽  
Yining Zhu ◽  
Huitao Zhang

Abstract To solve the problem of learning based computed tomography (CT) reconstruction, several reconstruction networks were invented. However, applying neural network to tomographic reconstruction still remains challenging due to unacceptable memory space requirement. In this study, we presents a novel lightweight block reconstruction network (LBRN), which transforms the reconstruction operator into a deep neural network by unrolling the filter back-projection (FBP) method. Specifically, the proposed network contains two main modules, which, respectively, correspond to the filter and back-projection of FBP method. The first module of LBRN decouples the relationship of Radon transform between the reconstructed image and the projection data. Therefore, the following module, block back-projection module, can use the block reconstruction strategy. Due to each image block is only connected with part filtered projection data, the network structure is greatly simplified and the parameters of the whole network is dramatically reduced. Moreover, this approach is trained end-to-end, working directly from raw projection data and does not depend on any initial images. Five reconstruction experiments are conducted to evaluate the performance of the proposed LBRN: full angle, low-dose CT, region of interest (ROI), metal artifacts reduction and real data experiment. The results of the experiments show that the LBRN can be effectively introduced into the reconstruction process and has outstanding advantages in terms of different reconstruction problems.


2020 ◽  
Vol 20 ◽  
pp. 100213
Author(s):  
Shuai Na ◽  
Xiaoyun Yuan ◽  
Li Lin ◽  
Julio Isla ◽  
David Garrett ◽  
...  

2015 ◽  
Vol 770 ◽  
pp. 491-494
Author(s):  
Andrey E. Kovtanyuk

A computed tomography problem as a 3D reconstruction of density distribution is considered. The input data are obtained as a result of irradiations. The solution of the computed tomography problem is presented as a set of cross-section images. The reconstruction in a single cross-section is performed by algorithm of convolution and back projection. The parallelization is fulfilled over a set of cross-sections by use of the MPI technology.


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