# Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image

arXiv: https://arxiv.org/abs/1709.02073 · HTML: https://mlsanity.com/paper/1709.02073/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2017.

## Built on by (most cited first, 9 of 9)

- [Deep learning-based synthetic-CT generation in radiotherapy and PET: a review](https://mlsanity.com/paper/2102.02734/) (2021)
- [Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites](https://mlsanity.com/paper/2206.06813/) (2022)
- [Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis](https://mlsanity.com/paper/2105.02835/) (2021)
- [Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets](https://mlsanity.com/paper/2104.01592/) (2021)
- [Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images](https://mlsanity.com/paper/2102.07225/) (2021)
- [CNNs and GANs in MRI-based cross-modality medical image estimation](https://mlsanity.com/paper/2106.02198/) (2021)
- [Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis](https://mlsanity.com/paper/2303.10202/) (2023)
- [TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation](https://mlsanity.com/paper/2208.06099/) (2022)
- [Structure-Guided MR-to-CT Synthesis with Spatial and Semantic Alignments for Attenuation Correction of Whole-Body PET/MR Imaging](https://mlsanity.com/paper/2411.17488/) (2024)

## Abstract (arXiv)

Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapping between them is highly complex due to large gaps of appearances of the two modalities. In this work, we aim to tackle this MR-to-CT synthesis by a novel deep embedding convolutional neural network (DECNN). Specifically, we generate the feature maps from MR images, and then transform these feature maps forward through convolutional layers in the network. We can further compute a tentative CT synthesis from the midway of the flow of feature maps, and then embed this tentative CT synthesis back to the feature maps. This embedding operation results in better feature maps, which are further transformed forward in DECNN. After repeat-ing this embedding procedure for several times in the network, we can eventually synthesize a final CT image in the end of the DECNN. We have validated our proposed method on both brain and prostate datasets, by also compar-ing with the state-of-the-art methods. Experimental results suggest that our DECNN (with repeated embedding op-erations) demonstrates its superior performances, in terms of both the perceptive quality of the synthesized CT image and the run-time cost for synthesizing a CT image.