Abdominal motion tracking with free-breathing XD-GRASP acquisitions using spatio-temporal geodesic trajectories.

Free-breathing external beam radiotherapy remains challenging due to the complex elastic or irregular motion of abdominal organs, as imaging moving organs leads to the creation of motion blurring artifacts. In this paper, we propose a radial-based MRI reconstruction method from 3D free-breathing abdominal data using spatio-temporal geodesic trajectories, to quantify motion during radiotherapy. The prospective study was approved by the institutional review board and consent was obtained from all participants.

Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks.

Magnetic resonance imaging (MRI) plays a critical role in the planning and monitoring of hepatocellular carcinomas (HCC) treated with locoregional therapies, in order to assess disease progression or recurrence. Dynamic contrast-enhanced (DCE)-MRI sequences offer temporal data on tumor enhancement characteristics which has strong prognostic value. Yet, predicting follow-up DCE-MR images from which tumor enhancement and viability can be measured, before treatment of HCC actually begins, remains an unsolved problem given the complexity of spatial and temporal information.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy.

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets.

Predictive online 3D target tracking with population-based generative networks for image-guided radiotherapy.

Purpose: Respiratory motion of thoracic organs poses a severe challenge for the administration of image-guided radiotherapy treatments. Providing online and up-to-date volumetric information during free breathing can improve target tracking, ultimately increasing treatment efficiency and reducing toxicity to surrounding healthy tissue. In this work, a novel population-based generative network is proposed to address the problem of 3D target location prediction from 2D image-based surrogates during radiotherapy, thus enabling out-of-plane tracking of treatment targets using images acquired in real time.

Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence.

This paper presents a prospective study evaluating the impact on image quality and quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters when varying the number of respiratory motion states when using an eXtra-Dimensional Golden-Angle Radial Sparse Parallel (XD-GRASP) MRI sequence. DCE acquisition was performed using a 3D stack-of-stars gradient-echo golden-angle radial acquisition in free-breathing with 100 spokes per motion state and temporal resolution of 6 s/volume, and using a non-rigid motion compensation to align different motion states. Parametric analysis was conducted using a dual-input single-compartment model.