Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016:2016:170-178.
doi: 10.1007/978-3-319-46976-8_18. Epub 2016 Sep 27.

Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks

Dong Nie  1   2 , Xiaohuan Cao  1   3 , Yaozong Gao  1   2 , Li Wang  1 , Dinggang Shen  1
Affiliations

Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks

Dong Nie et al. Deep Learn Data Label Med Appl (2016). 2016.

Abstract

Computed tomography (CT) is critical for various clinical applications, e.g., radiotherapy treatment planning and also PET attenuation correction. However, CT exposes radiation during CT imaging, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve any radiation. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiotherapy planning. In this paper, we propose a 3D deep learning based method to address this challenging problem. Specifically, a 3D fully convolutional neural network (FCN) is adopted to learn an end-to-end nonlinear mapping from MR image to CT image. Compared to the conventional convolutional neural network (CNN), FCN generates structured output and can better preserve the neighborhood information in the predicted CT image. We have validated our method in a real pelvic CT/MRI dataset. Experimental results show that our method is accurate and robust for predicting CT image from MRI image, and also outperforms three state-of-the-art methods under comparison. In addition, the parameters, such as network depth and activation function, are extensively studied to give an insight for deep learning based regression tasks in our application.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
A pair of corresponding pelvic MR (left) and CT (right) images from the same subject.
Fig. 2
Fig. 2
Illustration of difference between FCN and CNN. The left column shows MR slices, and the right one shows corresponding CT slices.
Fig. 3
Fig. 3
The 3D FCN architecture for estimating CT image from MRI image.
Fig. 4
Fig. 4
Sensitivity analysis of 3 activation functions with respect to network depth.
Fig. 5
Fig. 5
Visual comparison of original MR images, the estimated CT images by our method and the ground truth CT images on 2 subjects.
See this image and copyright information in PMC

References

    1. Brenner DJ, Hall EJ. Computed tomographyałan increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277–2284. - PubMed
    1. Burgos N, et al. Robust CT synthesis for radiotherapy planning: application to the head and neck region. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. MICCAI 2015. LNCS. Vol. 9350. Springer; Heidelberg: 2015. pp. 476–484.
    1. Catana C, et al. Toward implementing an MRI-based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med. 2010;51(9):1431–1438. - PMC - PubMed
    1. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. International Conference on Artificial Intelligence and Statistics; 2010. pp. 249–256.
    1. Huynh T, et al. Estimating CT image from MRI data using structured random forest and auto-context model. 2015 - PMC - PubMed