CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts

2018 ◽  
Vol 29 (8) ◽  
pp. 4008-4015 ◽  
Author(s):  
Su Joa Ahn ◽  
Jung Hoon Kim ◽  
Sang Min Lee ◽  
Sang Joon Park ◽  
Joon Koo Han
Keyword(s):  
Texture Analysis ◽  
Renal Cysts ◽  
Liver Parenchyma ◽  
Liver Lesions ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms

Convergence and stability analysis of the half thresholding based few-view CT reconstruction

2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang
Keyword(s):  
Noise Suppression ◽  
Reconstructed Image ◽  
Reference Image ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Ill Posed ◽  
Thresholding Algorithm ◽  
The Stability ◽  
Well Posed

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

scholarly journals Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhifang Wu ◽  
Binwei Guo ◽  
Bin Huang ◽  
Xinzhong Hao ◽  
Ping Wu ◽  
...  
Keyword(s):  
Time Of Flight ◽  
Pulmonary Nodules ◽  
Medium Size ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Silicon Photomultiplier ◽  
Standardized Uptake Values ◽  
Pet Ct ◽  
Quantification Accuracy ◽  
Small Pulmonary Nodules

AbstractTo evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10–25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

SU-C-141-05: Impact of Respiratory-Correlated CT Reconstruction Algorithms in the Choice of Margin Definition for Free Breathing Lung Treatment

2013 ◽  
Vol 40 (6Part2) ◽  
pp. 92-92
Author(s):  
S Thengumpallil ◽  
J-F Germond ◽  
O Matzinger ◽  
J Bourhis ◽  
F Bochud ◽  
...  
Keyword(s):  
Free Breathing ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms

scholarly journals Patterns of enhancement in the hepatobiliary phase of gadoxetic acid-enhanced MRI

2020 ◽  
Vol 93 (1112) ◽  
pp. 20190989
Author(s):  
Cathryn L Hui ◽  
Marcela Mautone
Keyword(s):  
Liver Parenchyma ◽  
Gadoxetic Acid ◽  
Hepatobiliary Phase ◽  
Liver Lesions ◽  
Enhanced Mri

A variety of patterns of enhancement of liver lesions and liver parenchyma is observed in the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI. It is becoming increasingly apparent that many lesions may exhibit HBP enhancement. Much of the literature regarding the role of gadoxetic acid-enhanced MRI in characterising liver lesions is dichotomous, focusing on whether lesions are enhancing or non-enhancing in the HBP, rather than examining the patterns of enhancement. We provide a pattern-based description of HBP enhancement of liver parenchyma and of liver lesions. The role of OATP1B3 transporters, hepatocyte function and lesion composition in influencing patterns of HBP hyperintensity are discussed.

scholarly journals Liver haematoma as a presentation of peliosis hepatis

2019 ◽  
Vol 12 (2) ◽  
pp. e226737 ◽  
Author(s):  
Yatee A Dave ◽  
Alisha Gupta ◽  
Mihir M Shah ◽  
Darren Carpizo
Keyword(s):  
Liver Parenchyma ◽  
Peliosis Hepatis ◽  
Liver Lesions ◽  
Postoperative Course ◽  
Intracystic Haemorrhage ◽  
Hepatic Haematoma ◽  
Liver Haematoma

Peliosis hepatis (PH) is an uncommon, benign hepatic vascular condition involving liver lesions consisting of dilated sinusoidal spaces that ultimately lead to numerous blood-filled cavities within the liver parenchyma. We detail the case of a 62-year-old woman who presented with a large hepatic haematoma resulting from intracystic haemorrhage. She was subsequently diagnosed with PH via arteriogram. Her symptoms were successfully treated surgically with open fenestration. Patient’s postoperative course was unremarkable, with no recurrence of symptoms to date.

scholarly journals Penalized PET/CT Reconstruction Algorithms with Automatic Realignment for Anatomical Priors

2020 ◽  
pp. 1-1
Author(s):  
Yu-Jung Tsai ◽  
Alexandre Bousse ◽  
Simon Arridge ◽  
Charles W. Stearns ◽  
Brian F. Hutton ◽  
...  
Keyword(s):  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Pet Ct ◽  
Anatomical Priors

scholarly journals Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3941 ◽  
Author(s):  
Li ◽  
Cai ◽  
Wang ◽  
Zhang ◽  
Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Image Domain ◽  
Adversarial Network ◽  
Domain Loss ◽  
Limited Angle

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

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