A patient-independent CT intensity matching method using conditional generative adversarial networks (cGAN) for single x-ray projection-based tumor localization.

A convolutional neural network (CNN)-based tumor localization method with a single x-ray projection was previously developed by us. One finding is that the discrepancy in the discrepancy in the intensity between a digitally reconstructed radiograph (DRR) of a three-dimensional computed tomography (3D-CT) and the measured x-ray projection has an impact on the performance. To address this issue, a patient-dependent intensity matching process for 3D-CT was performed using 3D-cone-beam computed tomography (3D-CBCT) from the same patient, which was sometimes inefficient and could adversely affect the clinical implementation of the framework.

Real-time tumor localization with single x-ray projection at arbitrary gantry angles using a convolutional neural network (CNN).

For tumor tracking therapy, precise knowledge of tumor position in real-time is very important. A technique using single x-ray projection based on a convolutional neural network (CNN) was recently developed which can achieve accurate tumor localization in real-time. However, this method was only validated at fixed gantry angles.

Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network.

Organ localization is an essential preprocessing step for many medical image analysis tasks such as image registration, organ segmentation and lesion detection. In this work, we propose an efficient method for multiple organ localization in CT image using 3D region proposal network. Compared with other convolutional neural network based methods that successively detect the target organs in all slices to assemble the final 3D bounding box, our method is fully implemented in 3D manner, thus can take full advantages of the spatial context information in CT image to perform efficient organ localization with only one prediction.

A probabilistic framework based on hidden markov model for fiducial identification in image-guided radiation treatments.

Fiducial tracking is a common target tracking method widely used in image-guided procedures such as radiotherapy and radiosurgery. In this paper, we present a multifiducial identification method that incorporates context information in the process. We first convert the problem into a state sequence problem by establishing a probabilistic framework based on a hidden Markov model (HMM), where prior probability represents an individual candidate's resemblance to a fiducial; transition probability quantifies the similarity of a candidate set to the fiducials' geometrical configuration; and the Viterbi algorithm provides an efficient solution.

A fast, accurate, and automatic 2D-3D image registration for image-guided cranial radiosurgery.

The authors developed a fast and accurate two-dimensional (2D)-three-dimensional (3D) image registration method to perform precise initial patient setup and frequent detection and correction for patient movement during image-guided cranial radiosurgery treatment. In this method, an approximate geometric relationship is first established to decompose a 3D rigid transformation in the 3D patient coordinate into in-plane transformations and out-of-plane rotations in two orthogonal 2D projections. Digitally reconstructed radiographs are generated offline from a preoperative computed tomography volume prior to treatment and used as the reference for patient position.

A study of the accuracy of cyberknife spinal radiosurgery using skeletal structure tracking.

Objective: New technology has enabled the increasing use of radiosurgery to ablate spinal lesions. The first generation of the CyberKnife (Accuray, Inc., Sunnyvale, CA) image-guided radiosurgery system required implanted radiopaque markers (fiducials) to localize spinal targets.