PURPOSE: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.
Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately a...
Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth r...
BACKGROUND: Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resectio...
To improve respiratory-gated radiotherapy accuracy, we developed a machine learning approach for markerless tumor tracking and evaluated it using lung cancer patient data. Digitally reconstructed radiography (DRR) datasets were generated using planni...
PURPOSE: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.
PURPOSE: To quickly and automatically propagate organ contours from pretreatment to fraction images in magnetic resonance (MR)-guided prostate external-beam radiotherapy.
An important aspect of robotic radiation therapy is active compensation of target motion. Recently, ultrasound has been proposed to obtain real-time volumetric images of abdominal organ motion. One approach to realize flexible probe placement through...
We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. T...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Aug 17, 2019
INTRODUCTION: Breathing artifact may affect the quality of four-dimensional computed tomography (4DCT) images. We developed a deep neural network (DNN)-based artifact reduction method.