scholarly journals Signal Enhancement of Low Magnetic Field Magnetic Resonance Image Using a Conventional- and Cyclic-Generative Adversarial Network Models With Unpaired Image Sets

2021 ◽  
Vol 11 ◽  
Author(s):  
Denis Yoo ◽  
Yuni Annette Choi ◽  
C. J. Rah ◽  
Eric Lee ◽  
Jing Cai ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Magnetic Resonance ◽  
Correlation Factor ◽  
Signal Enhancement ◽  
Enhancement Ratio ◽  
Mr Images ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Low Magnetic Field

In this study, the signal enhancement ratio of low-field magnetic resonance (MR) images was investigated using a deep learning-based algorithm. Unpaired image sets (0.06 Tesla and 1.5 Tesla MR images for different patients) were used in this study following three steps workflow. In the first step, the deformable registration of a 1.5 Tesla MR image into a 0.06 Tesla MR image was performed to ensure that the shapes of the unpaired set matched. In the second step, a cyclic-generative adversarial network (GAN) was used to generate a synthetic MR image of the original 0.06 Tesla MR image based on the deformed or original 1.5 Tesla MR image. Finally, an enhanced 0.06 Tesla MR image could be generated using the conventional-GAN with the deformed or synthetic MR image. The results from the optimized flow and enhanced MR images showed significant signal enhancement of the anatomical view, especially in the nasal septum, inferior nasal choncha, nasopharyngeal fossa, and eye lens. The signal enhancement ratio, signal-to-noise ratio (SNR) and correlation factor between the original and enhanced MR images were analyzed for the evaluation of the image quality. A combined method using conventional- and cyclic-GANs is a promising approach for generating enhanced MR images from low-magnetic-field MR.

scholarly journals Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

2021 ◽  
Vol 7 (8) ◽  
pp. 133
Author(s):  
Jonas Denck ◽  
Jens Guehring ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Magnetic Resonance ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Mr Images ◽  
Generative Adversarial Network ◽  
Radiology Training ◽  
Repetition Time ◽  
Echo Time ◽  
Image Orientation

A magnetic resonance imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. While current generative approaches allow only the synthesis of specific sets of MR contrasts, we developed a method to generate synthetic MR images with adjustable image contrast. Therefore, we trained a generative adversarial network (GAN) with a separate auxiliary classifier (AC) network to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, and image orientation). The AC determined the repetition time with a mean absolute error (MAE) of 239.6 ms, the echo time with an MAE of 1.6 ms, and the image orientation with an accuracy of 100%. Therefore, it can properly condition the generator network during training. Moreover, in a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

scholarly journals Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yanqiu Zeng ◽  
Baocan Zhang ◽  
Wei Zhao ◽  
Shixiao Xiao ◽  
Guokai Zhang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Electronic Noise ◽  
Mr Images ◽  
Electronic Components ◽  
Mr Image ◽  
Residual Part ◽  
Soft Threshold

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

scholarly journals Comparison of radiographer interobserver image registration variability using cone beam CT and MR for cervix radiotherapy

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200169
Author(s):  
John Rodgers ◽  
Rosie Hales ◽  
Lee Whiteside ◽  
Jacqui Parker ◽  
Louise McHugh ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Image Guidance ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Mr Images ◽  
Mr Image ◽  
Image Guided Radiotherapy ◽  
Multimodality Image ◽  
Planning Ct

Objectives: The aim of this study was to assess the consistency of therapy radiographers performing image registration using cone beam computed tomography (CBCT)-CT, magnetic resonance (MR)-CT, and MR-MR image guidance for cervix cancer radiotherapy and to assess that MR-based image guidance is not inferior to CBCT standard practice. Methods: 10 patients receiving cervix radiation therapy underwent daily CBCT guidance and magnetic resonance (MR) imaging weekly during treatment. Offline registration of each MR image, and corresponding CBCT, to planning CT was performed by five radiographers. MR images were also registered to the earliest MR interobserver variation was assessed using modified Bland–Altman analysis with clinically acceptable 95% limits of agreement (LoA) defined as ±5.0 mm. Results: 30 CBCT-CT, 30 MR-CT and 20 MR–MR registrations were performed by each observer. Registration variations between CBCT-CT and MR-CT were minor and both strategies resulted in 95% LoA over the clinical threshold in the anteroposterior direction (CBCT-CT ±5.8 mm, MR-CT ±5.4 mm). MR–MR registrations achieved a significantly improved 95% LoA in the anteroposterior direction (±4.3 mm). All strategies demonstrated similar results in lateral and longitudinal directions. Conclusion: The magnitude of interobserver variations between CBCT-CT and MR-CT were similar, confirming that MR-CT radiotherapy workflows are comparable to CBCT-CT image-guided radiotherapy. Our results suggest MR–MR radiotherapy workflows may be a superior registration strategy. Advances in knowledge: This is the first publication quantifying interobserver registration of multimodality image registration strategies for cervix radical radiotherapy patients.

Detection of degradation of magnetic resonance (MR) images: Comparison of an automated MR image-quality analysis system with trained human observers

1995 ◽  
Vol 2 (4) ◽  
pp. 277-281 ◽  
Author(s):  
Edward A. Gardner ◽  
James H. Ellis ◽  
Rosemary J. Hyde ◽  
Alex M. Aisen ◽  
Douglas J. Quint ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Analysis ◽  
Mr Images ◽  
Mr Image ◽  
Image Quality Analysis ◽  
Analysis System
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