Prediction of the position of external markers using a recurrent neural network trained with unbiased online recurrent optimization for safe lung cancer radiotherapy.

Background And Objective: During lung cancer radiotherapy, the position of infrared reflective objects on the chest can be recorded to estimate the tumor location. However, radiotherapy systems have a latency inherent to robot control limitations that impedes the radiation delivery precision. Prediction with online learning of recurrent neural networks (RNN) allows for adaptation to non-stationary respiratory signals, but classical methods such as real-time recurrent learning (RTRL) and truncated backpropagation through time are respectively slow and biased.

A Real-Time Four-Dimensional Reconstruction Algorithm of Cine-Magnetic Resonance Imaging (Cine-MRI) Using Deep Learning.

Purpose The purpose of this study is to propose algorithms and methods for achieving high accuracy in tracking and interception irradiation technology for tumors that move by respiration using MR-linac (MRIdian®, ViewRay Inc.) and to use deep learning to predict the movement of moving tumors in real time during radiation therapy and reconstruct cine magnetic resonance imaging (cine-MRI) into four-dimensional (4D) movies. Methods In this study, we propose a reconstruction algorithm using 4DCT for treatment planning taken before irradiation as training data in consideration of the actual treatment flow.

Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator (LINAC) systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow algorithm to perform deformable image registration (DIR) of chest computed tomography (CT) scan images of four patients with lung cancer. We then track three internal points close to the lung tumor based on the previously computed deformation field and predict their position with a recurrent neural network (RNN) trained using real-time recurrent learning (RTRL) and gradient clipping.

Development of a markerless tumor-tracking algorithm using prior four-dimensional cone-beam computed tomography.

Respiratory motion management is a huge challenge in radiation therapy. Respiratory motion induces temporal anatomic changes that distort the tumor volume and its position. In this study, a markerless tumor-tracking algorithm was investigated by performing phase recognition during stereotactic body radiation therapy (SBRT) using four-dimensional cone-beam computer tomography (4D-CBCT) obtained at patient registration, and in-treatment cone-beam projection images.

Quantification of susceptibility change at high-concentrated SPIO-labeled target by characteristic phase gradient recognition.

Phase map cross-correlation detection and quantification may produce highlighted signal at superparamagnetic iron oxide nanoparticles, and distinguish them from other hypointensities. The method may quantify susceptibility change by performing least squares analysis between a theoretically generated magnetic field template and an experimentally scanned phase image. Because characteristic phase recognition requires the removal of phase wrap and phase background, additional steps of phase unwrapping and filtering may increase the chance of computing error and enlarge the inconsistence among algorithms.

Tumor growth suppression by gadolinium-neutron capture therapy using gadolinium-entrapped liposome as gadolinium delivery agent.

Neutron capture therapy (NCT) is a promising non-invasive cancer therapy approach and some recent NCT research has focused on using compounds containing gadolinium as an alternative to currently used boron-10 considering several advantages that gadolinium offers compared to those of boron. In this study, we evaluated gadolinium-entrapped liposome compound as neutron capture therapy agent by in vivo experiment on colon-26 tumor-bearing mice. Gadolinium compound were injected intravenously via tail vein and allowed to accumulate into tumor site.

A study of internal defect testing with the laser-EMAT ultrasonic method.

This paper studies the ultrasonic detection and evaluation of internal volume defects in metals using laser generation and electromagnetic acoustic transducer (EMAT) detection. A finite element model is developed to simulate the interaction of laser-generated ultrasonic waves with the defect in the material. Not only have the directly scattered shear waves been observed, but also the mode-converted creeping waves on the defect surface.

Correction of chemical shift misregistration by images from two different bandwidths.

One major effect caused by the different chemical shift frequencies of water and fat is the misregistration between the two components in MR images. Methods to correct misregistration are required in clinical MRI for accurate localization and artifact reduction. One of the methods uses the images scanned at opposite readout gradients to separate water and fat signal in the k-space.

Phase gradient imaging for positive contrast generation to superparamagnetic iron oxide nanoparticle-labeled targets in magnetic resonance imaging.

Positive contrast imaging methods produce enhanced signal at large magnetic field gradient in magnetic resonance imaging. Several postprocessing algorithms, such as susceptibility gradient mapping and phase gradient mapping methods, have been applied for positive contrast generation to detect the cells targeted by superparamagnetic iron oxide nanoparticles. In the phase gradient mapping methods, smoothness condition has to be satisfied to keep the phase gradient unwrapped.