Improving respiratory signal prediction with a deep neural network and simple changes to the input and output data format.

To improve respiratory gating accuracy and radiation treatment throughput, we developed a generalized model based on a deep neural network (DNN) for predicting any given patient's respiratory motion.Our model uses long short-term memory (LSTM) based on a recurrent neural network (RNN), and improves upon common techniques. The first improvement is that the data input is not a one-dimensional sequence, but two-dimensional block data.

Real-time nonstandard-shaped gold fiducial marker tracking on x-ray fluoroscopic images for prostate radiotherapy.

The prostate moves in accordance with the movement of surrounding organs. Tumor position can change by ≥3 mm during radiotherapy. Given the difficulties of visualizing the prostate fluoroscopically, fiducial markers are generally implanted into the prostate to monitor its motion during treatment.

Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.

We sought to accelerate 2D/3D image registration computation time using image synthesis with a deep neural network (DNN) to generate digitally reconstructed radiographic (DRR) images from X-ray flat panel detector (FPD) images. And we explored the feasibility of using our DNN in the patient setup verification application. Images of the prostate and of the head and neck (H&N) regions were acquired by two oblique X-ray fluoroscopic units and the treatment planning CT.

Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.

We developed a deep neural network (DNN) to generate X-ray flat panel detector (FPD) images from digitally reconstructed radiographic (DRR) images. FPD and treatment planning CT images were acquired from patients with prostate and head and neck (H&N) malignancies. The DNN parameters were optimized for FPD image synthesis.

Optimizing 3DCT image registration for interfractional changes in carbon-ion prostate radiotherapy.

To perform setup procedures including both positional and dosimetric information, we developed a CT-CT rigid image registration algorithm utilizing water equivalent pathlength (WEPL)-based image registration and compared the resulting dose distribution with those of two other algorithms, intensity-based image registration and target-based image registration, in prostate cancer radiotherapy using the carbon-ion pencil beam scanning technique. We used the data of the carbon ion therapy planning CT and the four-weekly treatment CTs of 19 prostate cancer cases. Three CT-CT registration algorithms were used to register the treatment CTs to the planning CT.

Real-time deep neural network-based automatic bowel gas segmentation on X-ray images for particle beam treatment.

Since particle beam distribution is vulnerable to change in bowel gas because of its low density, we developed a deep neural network (DNN) for bowel gas segmentation on X-ray images. We used 6688 image datasets from 209 cases as training data, 736 image datasets from 23 cases as validation data and 102 image datasets from 51 cases as test data (total 283 cases). For the training data, we prepared three types of digitally reconstructed radiographic (DRR) images (all-density, bone and gas) by projecting the treatment planning CT image data.

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

Introduction: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (DNN) to generate 4DCT from 3DCT data.

A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy.

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 planning 4DCT data. Tumor positions were selected on respective DRR images to place the GTV center of gravity in the center of each DRR.

Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma.

Purpose: We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.

Methods: Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data.

Using a deep neural network for four-dimensional CT artifact reduction in image-guided radiotherapy.

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.

Methods: We used 857 thoracoabdominal 4DCT data sets scanned with 320-section CT with no 4DCT artifact within any volume (ground-truth image).

Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.

Purpose: To improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN).

Methods: In the learning stage, target positions were projected onto digitally reconstructed radiography (DRR) images from four-dimensional computed tomography (4DCT). DRR images were cropped into subimages of the target or surrounding regions to build a network that takes input of the image pattern of subimages and produces a target probability map (TPM) for estimating the target position.

Commissioning of a fluoroscopic-based real-time markerless tumor tracking system in a superconducting rotating gantry for carbon-ion pencil beam scanning treatment.

Purpose: To perform the final quality assurance of our fluoroscopic-based markerless tumor tracking for gated carbon-ion pencil beam scanning (C-PBS) radiotherapy using a rotating gantry system, we evaluated the geometrical accuracy and tumor tracking accuracy using a moving chest phantom with simulated respiration.

Methods: The positions of the dynamic flat panel detector (DFPD) and x-ray tube are subject to changes due to gantry sag. To compensate for this, we generated a geometrical calibration table (gantry flex map) in 15° gantry angle steps by the bundle adjustment method.