Dixon-based thorax synthetic CT generation using Generative Adversarial Network

Baydoun, Atallah; Xu, Ke; Yang, Huan; Zhou, Feifei; Heo, Jin Uk; Jones, Robert S.; Avril, Norbert; Traughber, Melanie; Traughber, Bryan; Qian, Pengjiang; Muzic, Raymond F.

doi:10.1016/j.ibmed.2020.100010

Cited by 9 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Baydoun et al 139 . tried different network configurations (VGG16, 153 VGG19, 153 and ResNet 154 ) as a benchmark with a 2D conditional GAN receiving either two Dixon input (water and fat) or four (water, fat, in‐phase, and opposed‐phase).…”

Section: Resultsmentioning

confidence: 99%

“…Results showed that DL prediction reduced the RMSE in corrected PET SUV by a factor of 4 for bone lesions and 1.5 for soft tissue lesions. Following this first work, other authors showed the improvement of DL-based AC over the traditional atlasbased MRAC proposed by the vendors 70,139,140,141,142,143,144 , also comparing several network configurations145,146 . Torrado et al142 pre-trained their U-net on 19 healthy brains acquired with T 1 GRE MRI and, subsequently, they trained the network using Dixon images of colorectal and prostate cancer patients.…”

mentioning

confidence: 90%

See 1 more Smart Citation

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

View full text Add to dashboard Cite

Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy, and III) derive attenuation maps for the correction of positron emission tomography (PET).Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated, assessing the clinical readiness of the presented methods.

show abstract

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 90%

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the first case, most methods have been tested with paired data in H&N (9 papers) and the pelvic region (4 papers) except Baydoun et al 131 64,131,132,133,134,135,136 , also comparing several network configurations 137,138 .…”

Section: Iiic Pet Attenuation Correctionmentioning

confidence: 99%

“…However, it requires more input data to improve output. Baydoun et al 131 tried different network configurations (VGG16 145 , VGG19 145 , and ResNet 146 ) as a benchmark with a 2D conditional GAN receiving either two Dixon input (water and fat) or four (water, fat, in-phase and opposed-phase). The GAN always performed better than VGG19 and ResNet, with more accurate results obtained with four inputs.…”

Section: Iiic Pet Attenuation Correctionmentioning

confidence: 99%

Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

Spadea,

Maspero,

Zaffino

et al. 2021

Preprint

View full text Add to dashboard Cite

Recently, deep learning (DL)-based methods for the generation of synthetic Computed Tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) to replace CT in magnetic resonance (MR)-based treatment planning, II) facilitate Cone-Beam Computed Tomography (CBCT)-based image guided adaptive radiotherapy, and III) derive attenuation maps for the correction of Positron Emission Tomography (PET). Appropriate database searching was performed on journal articles published between January 2014 and December 2020.The key characteristics of the DL methods were extracted from each eligible study and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges and also summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DLbased sCT generation was evaluated assessing the clinical readiness of the presented methods.

show abstract

“…The usual choice of conventional T1-, T2-, or Dixon-derived sequences as input for sCT generation algorithms derives essentially from two factors: 1) data availability, as conventional T1 and T2 sequences are the most widely used in clinical radiology [ 22 ], and Dixon sequences are used in the currently available PET/MR systems for attenuation correction [ 24 ]. 2) Dixon sequences allow the use of up to 4 channels as input, which empowers the features extraction capacity of the sCT generation methods and potentially improves the overall accuracy [ 25 ]. However, conventional T1 and T2 sequences usually require longer acquisition time than CT, which is a source of discomfort to the patient and leads to geometric distortions in MR images [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

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

Baydoun

Heo

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Dixon-based thorax synthetic CT generation using Generative Adversarial Network

Cited by 9 publications

References 28 publications

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Deep learning based synthetic‐CT generation in radiotherapy and PET: A review

Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

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

Contact Info

Product

Resources

About