Feasibility of similarity coefficient map in improving quality of magnetic resonance images of spleen

Author(s):  
Haoyu Wang ◽  
Yaoqin Xie ◽  
Shanglian Bao ◽  
Jiani Hu
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rafal Obuchowicz ◽  
Mariusz Oszust ◽  
Adam Piorkowski

Abstract Background The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. Methods For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss’ kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. Results The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. Conclusions The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.


2003 ◽  
Vol 18 (4) ◽  
pp. 442-448 ◽  
Author(s):  
Milica Medved ◽  
Weiliang Du ◽  
Marta A. Zamora ◽  
Xiaobing Fan ◽  
Olufunmilayo I. Olopade ◽  
...  

2016 ◽  
Vol 49 (3) ◽  
pp. 158-164
Author(s):  
Tiago da Silva Jornada ◽  
Camila Hitomi Murata ◽  
Regina Bitelli Medeiros

Abstract Objective: To study the influence that the scan percentage tool used in partial k-space acquisition has on the quality of images obtained with magnetic resonance imaging equipment. Materials and Methods: A Philips 1.5 T magnetic resonance imaging scanner was used in order to obtain phantom images for quality control tests and images of the knee of an adult male. Results: There were no significant variations in the uniformity and signal-to-noise ratios with the phantom images. However, analysis of the high-contrast spatial resolution revealed significant degradation when scan percentages of 70% and 85% were used in the acquisition of T1- and T2-weighted images, respectively. There was significant degradation when a scan percentage of 25% was used in T1- and T2-weighted in vivo images (p ≤ 0.01 for both). Conclusion: The use of tools that limit the k-space is not recommended without knowledge of their effect on image quality.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Lixia Chen ◽  
Bin Yang ◽  
Xuewen Wang

The quality of dynamic magnetic resonance imaging reconstruction has heavy impact on clinical diagnosis. In this paper, we propose a new reconstructive algorithm based on the L+S model. In the algorithm, the l1 norm is substituted by the lp norm to approximate the l0 norm; thus the accuracy of the solution is improved. We apply an alternate iteration method to solve the resulting problem of the proposed method. Experiments on nine data sets show that the proposed algorithm can effectively reconstruct dynamic magnetic resonance images.


2021 ◽  
Vol 15 ◽  
Author(s):  
Evan Fletcher ◽  
Charles DeCarli ◽  
Audrey P. Fan ◽  
Alexander Knaack

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.


2021 ◽  
Vol 8 ◽  
pp. 100327
Author(s):  
Daniel Stocker ◽  
Andrei Manoliu ◽  
Anton S. Becker ◽  
Borna K. Barth ◽  
Daniel Nanz ◽  
...  

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