Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network

Abstract

Multi-modality imaging constitutes a foundation of precision medicine, especially in oncology where reliable and rapid imaging techniques are needed in order to insure adequate diagnosis and treatment. In cervical cancer, precision oncology requires the acquisition of 18 F-labeled 2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET), magnetic resonance (MR), and computed tomography (CT) images. Thereafter, images are co-registered to derive electron density attributes required for FDG-PET attenuation correction and radiation therapy planning. Nevertheless, this traditional approach is subject to MR-CT registration defects, expands treatment expenses, and increases the patient’s radiation exposure. To overcome these disadvantages, we propose a new framework for cross-modality image synthesis which we apply on MR-CT image translation for cervical cancer diagnosis and treatment. The framework is based on a conditional generative adversarial network (cGAN) and illustrates a novel tactic that addresses, simplistically but efficiently, the paradigm of vanishing gradient vs. feature extraction in deep learning. Its contributions are summarized as follows: 1) The approach –termed sU-cGAN-uses, for the first time, a shallow U-Net (sU-Net) with an encoder/decoder depth of 2 as generator; 2) sU-cGAN’s input is the same MR sequence that is used for radiological diagnosis, i.e. T2-weighted, Turbo Spin Echo Single Shot (TSE-SSH) MR images; 3) Despite limited training data and a single input channel approach, sU-cGAN outperforms other state of the art deep learning methods and enables accurate synthetic CT (sCT) generation. In conclusion, the suggested framework should be studied further in the clinical settings. Moreover, the sU-Net model is worth exploring in other computer vision tasks.

Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy

“…The trained model was applied to the remaining 30 MRI data to synthesize pseudo-CT images. The accuracy of MD-CycleGAN was verified by comparing the pseudo-CT images synthesized based on GAN with ResNet, shallow U-Net (sUnet), and FCN as the generators(Emami et al 2018, Baydoun et al 2021. The ground truth CT (CT gt ) was the real CT images obtained at the simulation stage.…”

Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network

“…Some of the traditional methods for generating a synthetic CT image involved bulk tissue density assignment as discussed in [6] and image registration between CT and MRI [8], [9]. Many recent works have proposed deep CNN learning methods for continuous value sCT generation using variants of U-Net or GAN based regression [13], [14]. Two of the recent surveys on the topic [11], [12] have extensively summarized related works.…”

Generative adversarial networks in ophthalmology: what are these and how can they be used?

“…There are many ways to incorporate GANs in medical imaging tasks, such as segmentation [18], classification [19], detection [20], registration [21], image reconstruction [22] and image synthesis (Table 1) [23]. GANs have been used in research studies for generating medical images of various image modalities, including breast ultrasound [24], mammograms [25], computed tomography (CT) [26][27][28][29], magnetic resonance images (MRI) [30], cancer pathology images [31], and contrast agent-free ischemic heart disease images [32]. Moreover, GANs have been shown to be capable of cross-modality image synthesis, such as generating MRI based on ultrasound [33] or CT [34,35].…”