Image Quality
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2021 ◽  
Vol 96 ◽  
pp. 107508
Author(s):  
Tian Yuan ◽  
Chen Li ◽  
Lihua Tian ◽  
Guo Li

2021 ◽  
Vol 30 (05) ◽  
Author(s):  
Juncai Yao ◽  
Jing Shen

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Liugang Gao ◽  
Kai Xie ◽  
Xiaojin Wu ◽  
Zhengda Lu ◽  
Chunying Li ◽  
...  

Abstract Objective To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy. Methods The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution. Results The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy. Conclusions High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.


2021 ◽  
pp. 028418512110418
Author(s):  
Vasiliki Chatzaraki ◽  
Rahel A Kubik-Huch ◽  
Michael Thali ◽  
Tilo Niemann

Background Contrast-to-noise ratio is used to objectively evaluate image quality in chest computed tomography angiography (CTA). Different authors define and measure contrast-to-noise ratio using different methods. Purpose To summarize and evaluate the different contrast-to-noise ratio calculation formulas in the current literature. Material and Methods A systematic review of the recent literature for studies using contrast-to-noise ratio was performed. Contrast-to-noise ratio measurement methods reported by the different authors were recorded and reproduced in three patients who underwent chest CTA in our department for exploring variations among the different measurement methods. Results The search resulted in 109 articles, of which 26 were included. The studies involved 69 different measurements and overall, three different formula patterns. In all three, aorta and pulmonary arteries comprised the objects of interest in the numerator. In the denominator, standard deviation of the attenuation of the object of interest itself or of another background were used to reflect image noise. Some authors averaged the ratio values at different levels to obtain global ratio values. Using the object of interest itself for image noise calculation in the denominator compared to the usage of another background caused the most prominent variances of contrast-to-noise ratio between the two different protocols used for the reproduction of the measurements. Conclusion We recommend using the standard deviation of the attenuation of a background indicator as image noise rather than the object of interest itself for more reliable and comparative values. Global contrast-to-noise ratios based on averaging the values of different measurement levels should be avoided.


2021 ◽  
Author(s):  
Joshua P. Seguin

<div>The study of neurodegenerative diseases have found promise through white matter lesions best visualized in FLAIR MRI; however, algorithms experience difficulty in generalizing to large multicenter datasets due to the variance of image quality and characteristics. This thesis presents a quality control tool that combines image quality assessment with outlier rejection algorithms; this tool is unique as it is specifically designed for large multicenter FLAIR MRI datasets. An image processing approach evaluates each volume by: intensity-based features, sharpness/blur-based features, signal- and contrast-to-noise ratios, noise field characteristics, motion artifact prevalence</div><div>and a total IQ score. The performance of this tool was evaluated on labelled ADNI and CCNA data reporting F1 scores of 0.82, and 0.85, respectively. Applications for this tool include potential rescan or longitudinal scanner study alongside the immediate application of outlier removal for</div><div>large FLAIR datasets.</div>


2021 ◽  
Author(s):  
Joshua P. Seguin

<div>The study of neurodegenerative diseases have found promise through white matter lesions best visualized in FLAIR MRI; however, algorithms experience difficulty in generalizing to large multicenter datasets due to the variance of image quality and characteristics. This thesis presents a quality control tool that combines image quality assessment with outlier rejection algorithms; this tool is unique as it is specifically designed for large multicenter FLAIR MRI datasets. An image processing approach evaluates each volume by: intensity-based features, sharpness/blur-based features, signal- and contrast-to-noise ratios, noise field characteristics, motion artifact prevalence</div><div>and a total IQ score. The performance of this tool was evaluated on labelled ADNI and CCNA data reporting F1 scores of 0.82, and 0.85, respectively. Applications for this tool include potential rescan or longitudinal scanner study alongside the immediate application of outlier removal for</div><div>large FLAIR datasets.</div>


Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2706
Author(s):  
Haotian Wen ◽  
José María Luna-Romera ◽  
José C. Riquelme ◽  
Christian Dwyer ◽  
Shery L. Y. Chang

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.


Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1886
Author(s):  
Olivier Chevallier ◽  
Hélène Escande ◽  
Khalid Ambarki ◽  
Elisabeth Weiland ◽  
Bernd Kuehn ◽  
...  

To compare two magnetic resonance cholangiopancreatography (MRCP) sequences at 3 Tesla (3T): the conventional 3D Respiratory-Triggered SPACE sequence (RT-MRCP) and a prototype 3D Compressed-Sensing Breath-Hold SPACE sequence (CS-BH-MRCP), in terms of qualitative and quantitative image quality and radiologist’s diagnostic confidence for detecting common bile duct (CBD) lithiasis, biliary anastomosis stenosis in liver-transplant recipients, and communication of pancreatic cyst with the main pancreatic duct (MPD). Sixty-eight patients with suspicion of choledocholithiasis or biliary anastomosis stenosis after liver transplant, or branch-duct intraductal papillary mucinous neoplasm of the pancreas (BD-IPMN), were included. The relative CBD to peri-biliary tissues (PBT) contrast ratio (CR) was assessed. Overall image quality, presence of artefacts, background noise suppression and the visualization of 12 separated segments of the pancreatic and bile ducts were evaluated by two observers working independently on a five-point scale. Diagnostic confidence was scored on a 1–3 scale. The CS-BH-MRCP presented significantly better CRs (p < 0.0001), image quality (p = 0.004), background noise suppression (p = 0.011), fewer artefacts (p = 0.004) and better visualization of pancreatic and bile ducts segments with the exception of the proximal CBD (p = 0.054), cystic duct confluence (p = 0.459), the four secondary intrahepatic bile ducts, and central part of the MPD (p = 0.885) for which no significant differences were found. Overall, diagnostic confidence was significantly better with the CS-BH-MRCP sequence for both readers (p = 0.038 and p = 0.038, respectively). This study shows that the CS-BH-MRCP sequence presents overall better image quality and bile and pancreatic ducts visualization compared to the conventional RT-MRCP sequence at 3T.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karsten Sebastian Luetkens ◽  
Süleyman Ergün ◽  
Henner Huflage ◽  
Andreas Steven Kunz ◽  
Carsten Herbert Gietzen ◽  
...  

AbstractCone-beam computed tomography is a powerful tool for 3D imaging of the appendicular skeleton, facilitating detailed visualization of bone microarchitecture. This study evaluated various combinations of acquisition and reconstruction parameters for the cone-beam CT mode of a twin robotic x-ray system in cadaveric wrist and elbow scans, aiming to define the best possible trade-off between image quality and radiation dose. Images were acquired with different combinations of tube voltage and tube current–time product, resulting in five scan protocols with varying volume CT dose indices: full-dose (FD; 17.4 mGy), low-dose (LD; 4.5 mGy), ultra-low-dose (ULD; 1.15 mGy), modulated low-dose (mLD; 0.6 mGy) and modulated ultra-low-dose (mULD; 0.29 mGy). Each set of projection data was reconstructed with three convolution kernels (very sharp [Ur77], sharp [Br69], intermediate [Br62]). Five radiologists subjectively assessed the image quality of cortical bone, cancellous bone and soft tissue using seven-point scales. Irrespective of the reconstruction kernel, overall image quality of every FD, LD and ULD scan was deemed suitable for diagnostic use in contrast to mLD (very sharp/sharp/intermediate: 60/55/70%) and mULD (0/3/5%). Superior depiction of cortical and cancellous bone was achieved in FDUr77 and LDUr77 examinations (p < 0.001) with LDUr77 scans also providing favorable bone visualization compared to FDBr69 and FDBr62 (p < 0.001). Fleiss’ kappa was 0.618 (0.594–0.641; p < 0.001), indicating substantial interrater reliability. In this study, we demonstrate that considerable dose reduction can be realized while maintaining diagnostic image quality in upper extremity joint scans with the cone-beam CT mode of a twin robotic x-ray system. Application of sharper convolution kernels for image reconstruction facilitates superior display of bone microarchitecture.


Author(s):  
Heng Yao ◽  
Ben Ma ◽  
Mian Zou ◽  
Dong Xu ◽  
Jincao Yao

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