3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy.

Background And Purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy.

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system.

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review.

Purpose: In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the electron density of tissue necessary for dose calculation. Several methods of synthetic-CT (sCT) generation from MRI data have been developed for radiotherapy dose calculation.

Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.

Purpose: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) from CBCT to perform dose calculation.

Comparison of Deep Learning-Based and Patch-Based Methods for Pseudo-CT Generation in MRI-Based Prostate Dose Planning.

Purpose: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM).

Methods And Materials: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy).

Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods.

Purpose: Methods have been recently developed to generate pseudo-computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)-only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy.

Materials And Methods: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer.

Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol.

Selective internal radiation therapy (SIRT) using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of liver cancer. A personalized intervention can lead to high concentration dose in the tumor, while sparing the surrounding parenchyma. We propose a computational model for patient-specific simulation of entire hepatic arterial tree, based on liver, tumors, and arteries segmentation on patient's tomography.

Inexact coronary tree matching algorithm with artificial nodes.

Coronary tree matching is applied to plan percutaneous vascular procedures. This work, which allows following each segment of non-isomorphic coronary trees over time, precedes the determination of the best 2D angiography view from C-arm acquisition system for angioplasty procedure. To match two 3D coronary trees which represent two successive cardiac phases, we adapted a reference inexact tree matching algorithm based on association graph and maximum clique.

Turning Tangent Empirical Mode Decomposition: A Framework for Mono- and Multivariate Signals.

A novel Empirical Mode Decomposition (EMD) algorithm, called 2T-EMD, for both mono- and multivariate signals is proposed in this paper. It differs from the other approaches by its computational lightness and its algorithmic simplicity. The method is essentially based on a redefinition of the signal mean envelope, computed thanks to new characteristic points, which offers the possibility to decompose multivariate signals without any projection.

L0 constrained sparse reconstruction for multi-slice helical CT reconstruction.

In this paper, we present a Bayesian maximum a posteriori method for multi-slice helical CT reconstruction based on an L0-norm prior. It makes use of a very low number of projections. A set of surrogate potential functions is used to successively approximate the L0-norm function while generating the prior and to accelerate the convergence speed.

ECG gated tomographic reconstruction for 3-D rotational coronary angiography.

A method is proposed for 3-D reconstruction of coronary from a limited number of projections in rotational angiography. A Bayesian maximum a posteriori (MAP) estimation is applied with a Poisson distributed projection to reconstruct the 3D coronary tree at a given instant of the cardiac cycle. Several regularizers are investigated L0-norm, L1 and L2 -norm in order to take into account the sparsity of the data.

A model-based reconstruction method for 3-D rotational coronary angiography.

This paper presents a model-based reconstruction method of the coronary tree from a few number of projections in rotational angiography imaging. The reconstruction relies on projections acquired at a same cardiac phase and an energy function minimization that aims to lead the deformation of the 3D model to fit projection data whereas preserving coherence both in time and space. Some preliminary results are provided on simulated rotational angiograms.

Robust rigid registration of retinal angiograms through optimization.

Retinal fundus photographs are employed as standard diagnostic tools in ophthalmology. Serial photographs of the flow of fluorescein and indocyanine green (ICG) dye are used to determine the areas of the retinal lesions. For objective measurements of features, the registration of the images is a necessity.

ECG compression by modelling the instantaneous module/phase of its DCT.

Recent developments in compression methods on the non-linear and non-stationary data, such as electrocardiograms (ECG), have received large attention by the time-frequency analysts. The technique presented in this paper is based on parametrical modeling the instantaneous module as well as the instantaneous phase, estimated directly from the Discrete Cosine Transform (DCT) of each ECG beat. The estimated parameters are then used to reconstruct each recorded beat.