ResViT: residual vision transformers for multimodal medical image synthesis

O Dalmaz, M Yurt, T Çukur - IEEE Transactions on Medical …, 2022 - ieeexplore.ieee.org
Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Attenuation correction of PET/MR imaging

Y Chen, H An - Magnetic Resonance Imaging Clinics, 2017 - mri.theclinics.com
Simultaneous PET and MR imaging offers unprecedented opportunities to synergize the
physiologic and molecular imaging capability of PET and the excellent anatomic and …

Hi-net: hybrid-fusion network for multi-modal MR image synthesis

T Zhou, H Fu, G Chen, J Shen… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Magnetic resonance imaging (MRI) is a widely used neuroimaging technique that can
provide images of different contrasts (ie, modalities). Fusing this multi-modal data has …

MR‐based synthetic CT generation using a deep convolutional neural network method

X Han - Medical physics, 2017 - Wiley Online Library
Purpose Interests have been rapidly growing in the field of radiotherapy to replace CT with
magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and …

MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks

Y Lei, J Harms, T Wang, Y Liu, HK Shu, AB Jani… - Medical …, 2019 - Wiley Online Library
Purpose Automated synthetic computed tomography (sCT) generation based on magnetic
resonance imaging (MRI) images would allow for MRI‐only based treatment planning in …

A review of GPU-based medical image reconstruction

P Després, X Jia - Physica Medica, 2017 - Elsevier
Tomographic image reconstruction is a computationally demanding task, even more so
when advanced models are used to describe a more complete and accurate picture of the …

Cross-modality image synthesis from unpaired data using cyclegan: Effects of gradient consistency loss and training data size

Y Hiasa, Y Otake, M Takao, T Matsuoka… - … and Synthesis in …, 2018 - Springer
CT is commonly used in orthopedic procedures. MRI is used along with CT to identify
muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast …

[HTML][HTML] A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients

CN Ladefoged, I Law, U Anazodo, KS Lawrence… - Neuroimage, 2017 - Elsevier
Aim To accurately quantify the radioactivity concentration measured by PET, emission data
need to be corrected for photon attenuation; however, the MRI signal cannot easily be …

[HTML][HTML] DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

C Wang, G Yang, G Papanastasiou, SA Tsaftaris… - Information …, 2021 - Elsevier
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-
domain medical image synthesis tasks particularly due to its ability to deal with unpaired …

Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction

A Torrado-Carvajal, J Vera-Olmos… - Journal of nuclear …, 2019 - Soc Nuclear Med
Whole-body attenuation correction (AC) is still challenging in combined PET/MR scanners.
We describe Dixon-VIBE Deep Learning (DIVIDE), a deep-learning network that allows …