159. Evaluation of a user-guided deformable registration workflow for multi-modal (CT-MRI) prostate imaging

Lastrucci, A.; Barucci, Andrea; Pini, S.; Russo, S.; Alpi, P.; Barca, R.; Coppola, M.; Fondelli, S.; Paoletti, L.; Rossi, Francesca; Bastiani, P.; Esposito, Marco

doi:10.1016/j.ejmp.2018.04.170

Cited by 1 publication

(2 citation statements)

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“…As such, the dosimetric inaccuracies engendered by MR-CT registration defects would be of lower amplitude than those of the diagnostic inaccuracies. Nevertheless, the Reg Refine image registration tool has been validated previously for dosimetric applications in different clinical settings [ 52 ]–[ 55 ], including CT to T2-weighted MR images registration.…”

Section: Discussionmentioning

confidence: 99%

“…T2-weighted, TSE-SSH MR, and CT Images were de-identified and uploaded into MIM (MIM Software, Inc, Cleveland, OH). CT images were registered to the T2-weighted, TSE-SSH images using the MIM Reg Refine deformable image registration tool [ 52 ]–[ 55 ]. Images were then visually inspected and loaded into MATLAB 2020a (MathWorks, Inc.) using the COMKAT Image Tool [ 56 ], [ 57 ], and intensities were normalized to [0, 1] for training.…”

Section: Methodsmentioning

confidence: 99%

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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

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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.

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Section: Discussionmentioning

confidence: 99%