Multi‐sequence MR image‐based synthetic CT generation using a generative adversarial network for head and neck MRI‐only radiotherapy

2020 ◽  
Vol 47 (4) ◽  
pp. 1880-1894 ◽  
Author(s):  
Mengke Qi ◽  
Yongbao Li ◽  
Aiqian Wu ◽  
Qiyuan Jia ◽  
Bin Li ◽  
...  
Keyword(s):  
Head And Neck ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct

scholarly journals OC-0294: MR-based synthetic CT with conditional Generative Adversarial Network for prostate RT planning

2018 ◽  
Vol 127 ◽  
pp. S151-S152 ◽  
Author(s):  
M.H.F. Savenije ◽  
M. Maspero ◽  
A.M. Dinkla ◽  
P.R. Seevinck ◽  
C.A.T. Van den Berg
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct

scholarly journals Deep CT to MR Synthesis Using Paired and Unpaired Data

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2361 ◽  
Author(s):  
Cheng-Bin Jin ◽  
Hakil Kim ◽  
Mingjie Liu ◽  
Wonmo Jung ◽  
Seongsu Joo ◽  
...  
Keyword(s):  
Screening Method ◽  
Training Data ◽  
Synthetic Methods ◽  
Training Methods ◽  
Ct Image ◽  
Mr Image ◽  
Generative Adversarial Network ◽  
Cardiac Pacemakers ◽  
Metal Implants ◽  
Adversarial Network

Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.

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 Dixon-based thorax synthetic CT generation using Generative Adversarial Network

2020 ◽  
Vol 3-4 ◽  
pp. 100010
Author(s):  
Atallah Baydoun ◽  
Ke Xu ◽  
Huan Yang ◽  
Feifei Zhou ◽  
Jin Uk Heo ◽  
...  
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Synthetic Ct
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