A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging

Abstract

A novel method was developed to track lung tumor motion in real time during radiation therapy with the purpose to allow target radiation dose escalation while simultaneously reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. This method analyzes beam’s eye view radiation therapy treatment megavoltage (MV) images with simulated digitally reconstructed radiographs (DRRs) as references. Instead of comparing global DRRs with projection images, this method incorporates a technique that divides the global composite DRR and the corresponding MV projection into sub-images called tiles. Registration is performed independently on tile pairs in order to reduce the effects of global discrepancies due to scattering or imaging modality differences. This algorithm was evaluated by phantom studies while simulated tumors were controlled to move with various patterns in a complex humanoid torso. Approximately 15,000 phantom MV images were acquired at nine gantry angles, with different tumors moving within ranges between 10 and 20 mm. Tumors were successfully identified on every projection with a total maximum/average error of 1.84/0.98 mm. This algorithm was also applied to over 5,000 frames of MV projections acquired during radiation therapy of five lung cancer patients. This tumor-tracking methodology is capable of accurately locating lung tumors during treatment without implanting any internal fiducial markers nor delivering extra imaging radiation doses.

Simultaneous object detection and segmentation for patient‐specific markerless lung tumor tracking in simulated radiographs with deep learning

“…Besides DL-based approaches, numerous other studies have investigated non-DL-based image tracking techniques for markerless lung tumor tracking, including well-established methods such as image registration, 40 template matching, 41 and optical flow 42,43 . Other less common methods like short arc tumor tracking 44 and hidden Markov model 45 have also been proposed.Some studies were exclusively conducted on digital or experimental phantoms with tumor motions that were mechanically controlled following either simulated breathing patterns 42 or patient-measured motion traces.…”

Tennis Rehabilitation Training-Assisted Paclitaxel Nanoparticles in Treatment of Lung Tumor

“…In this paper, Camel was used as a template to design and screen new high-performance paclitaxel nanoparticles with anticancer activity. Based on this, we will do the following work on peptide carriers: (1) develop targeted anticancer drugs through receptor-mediated targeting molecules and chimera 18-Camel; (2) coordinate anticancer drugs by combining targeted drugs and anticancer drugs with siRNA carrying tumor suppressor genes and antirejection genes; (3) to study the anticancer effect in mice, the synergistic anticancer effect in vivo as a gene carrier, the distribution of paclitaxel in mice, and the evaluation of toxicity.…”

“…e goal is to gradually increase the target radiation dose while reducing the dose to sensitive structures, thereby increasing local control without increasing toxicity. is method uses the analog and digital reconstructed radiographs as a reference to analyze the megavolt image of the eye diagram of the beam for radiotherapy treatment, but the technology of tracking motion is too complicated and not so easy to implement [3]. e innovation of this article lies in the application of nanotechnology in photodynamic tumor therapy, which can improve the stability of photosensitizers, volume targeting, the depth of action of photodynamic therapy, and simple oxygen production in various ways.…”

Technical Note: Synthetic treatment beam imaging for motion monitoring during spine SBRT treatments — a phantom study

“…There have been several publications related to using MV images for monitoring lung and prostate tumor motion in real-time during treatment. [14][15][16][17][18][19][20] However, these techniques are mostly applied to three-dimensional (3D) static or 3D conformal/arc plans, which have relatively large field apertures in MV images to facilitate image matching between the MV image and digitally reconstructed radiography (DRR) from planning CT. On the contrary, for spine SBRT treatment, since highly intensity-modulated beams are used to create a sharp dose falloff to spare the radiosensitive spinal cord or cauda while maintaining adequate target coverage, the beam apertures are often small, which renders a very challenging task of tracking spine motion with the treatment beam images. In this paper, we present a method to enlarge the field of view (FOV) of MV treatment beam images, called the enhanced synthetic treatment beam (ESTB) imaging, and illustrate its feasibility in detecting motion in spine SBRT treatment delivered with sliding window IMRT.…”

“…Since the MV image axis is orthogonal to the kV counterpart, if acquired simultaneously and combined with the kV images, on‐treatment MV images can be a natural solution to these problems. There have been several publications related to using MV images for monitoring lung and prostate tumor motion in real‐time during treatment 14–20 . However, these techniques are mostly applied to three‐dimensional (3D) static or 3D conformal/arc plans, which have relatively large field apertures in MV images to facilitate image matching between the MV image and digitally reconstructed radiography (DRR) from planning CT. On the contrary, for spine SBRT treatment, since highly intensity‐modulated beams are used to create a sharp dose falloff to spare the radiosensitive spinal cord or cauda while maintaining adequate target coverage, the beam apertures are often small, which renders a very challenging task of tracking spine motion with the treatment beam images.…”