Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model
TLDR
Experimental results show that the learning-based method proposed can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.Abstract:
Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.read more
Citations
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Book ChapterDOI
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
TL;DR: Wang et al. as mentioned in this paper trained a fully convolutional network (FCN) to generate CT given the MR image, and applied Auto-Context Model (ACM) to implement a context-aware generative adversarial network.
Journal ArticleDOI
Medical Image Synthesis with Deep Convolutional Adversarial Networks
TL;DR: This paper trains a fully convolutional network to generate a target image given a source image and proposes to use the adversarial learning strategy to better model the FCN, designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images.
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MedGAN: Medical image translation using GANs
Karim Armanious,Karim Armanious,Chenming Jiang,Marc Fischer,Thomas Küstner,Tobias Hepp,Konstantin Nikolaou,Sergios Gatidis,Bin Yang +8 more
TL;DR: A new framework, named MedGAN, is proposed for medical image-to-image translation which operates on the image level in an end- to-end manner and outperforms other existing translation approaches.
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A review of substitute CT generation for MRI-only radiation therapy
TL;DR: A variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences, and a consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.
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Multimodal MR Synthesis via Modality-Invariant Latent Representation
TL;DR: A multi-input multi-output fully convolutional neural network model for MRI synthesis that avoids the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated.
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