Improving image‐guided radiation therapy of lung cancer by reconstructing 4D‐CT from a single free‐breathing 3D‐CT on the treatment day

Abstract

Purpose: One of the major challenges of lung cancer radiation therapy is how to reduce the margin of treatment field but also manage geometric uncertainty from respiratory motion. To this end, 4D-CT imaging has been widely used for treatment planning by providing a full range of respiratory motion for both tumor and normal structures. However, due to the considerable radiation dose and the limit of resource and time, typically only a free-breathing 3D-CT image is acquired on the treatment day for image-guided patient setup, which is often determined by the image fusion of the free-breathing treatment and planning day 3D-CT images. Since individual slices of two free breathing 3D-CTs are possibly acquired at different phases, two 3D-CTs often look different, which makes the image registration very challenging. This uncertainty of pretreatment patient setup requires a generous margin of radiation field in order to cover the tumor sufficiently during the treatment. In order to solve this problem, our main idea is to reconstruct the 4D-CT (with full range of tumor motion) from a single free-breathing 3D-CT acquired on the treatment day. Methods: We first build a super-resolution 4D-CT model from a low-resolution 4D-CT on the planning day, with the temporal correspondences also established across respiratory phases. Next, we propose a 4D-to-3D image registration method to warp the 4D-CT model to the treatment day 3D-CT while also accommodating the new motion detected on the treatment day 3D-CT. In this way, we can more precisely localize the moving tumor on the treatment day. Specifically, since the free-breathing 3D-CT is actually the mixed-phase image where different slices are often acquired at different respiratory phases, we first determine the optimal phase for each local image patch in the free-breathing 3D-CT to obtain a sequence of partial 3D-CT images (with incomplete image data at each phase) for the treatment day. Then we reconstruct a new 4D-CT for the treatment day by registering the 4D-CT of the planning day (with complete information) to the sequence of partial 3D-CT images of the treatment day, under the guidance of the 4D-CT model built on the planning day. Results: We first evaluated the accuracy of our 4D-CT model on a set of lung 4D-CT images with manually labeled landmarks, where the maximum error in respiratory motion estimation can be reduced from 6.08 mm by diffeomorphic Demons to 3.67 mm by our method. Next, we evaluated our proposed 4D-CT reconstruction algorithm on both simulated and real free-breathing images. The reconstructed 4D-CT using our algorithm shows clinically acceptable accuracy and could be used to guide a more accurate patient setup than the conventional method. Conclusions: We have proposed a novel two-step method to reconstruct a new 4D-CT from a single free-breathing 3D-CT on the treatment day. Promising reconstruction results imply the possible application of this new algorithm in the image guided radiation therapy of lung cancer.

A Novel Fast Helical 4D-CT Acquisition Technique to Generate Low-Noise Sorting Artifact–Free Images at User-Selected Breathing Phases

“…Images are free from sorting artifacts and were generated at a patient dose similar to or less than those used for current 4D-CT techniques. Although techniques to deal with the sorting artifacts present in images created using conventional slow-pitch helical and cine 4D-CT acquisition techniques (2) have been previously proposed using deformable registration (27, 28), the use of fast-helical scans here avoids the resorting of reconstructed images entirely. The technique is robust in the presence of irregular breathing, and the use of a motion model allows images to be generated at any user-specified breathing phase.…”

Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

“…Miguel, et al studied a patient-specific respiratory model based on combined images from various respiratory cine and static images using B-Spline registration (21). Wu, et al applied super-resolution to estimate lung motion using spatiotemporal images and produce 4DCT using the 3DCT of the day (22). Super-resolution has become an effective approach to overcome imaging detection limits and produce superior image quality.…”

Reconstruction of four-dimensional computed tomography lung images by applying spatial and temporal anatomical constraints using a Bayesian model

“…1,2 Recently, 4-D cone beam CT has also been used for radiotherapy planning. It has been widely used in diagnosis and image-guided treatments (such as image-guided biopsy and radiation therapy) for lung cancer.…”

“…In radiation therapy, 4-D CT helps precisely plan and optimize the orientation, shape, and dose of radiation beams and guides the delivery of radiation beams toward the tumor in the presence of respiratory motion. 1,2 Successful implementation of 4-D CT scanning not only provides breathing dynamics, but also minimizes motion artifacts for imaging diagnosis and image-guided procedures. [3][4][5] Because 4-D CT data are acquired prior to the treatment, they may not reflect the real-time anatomy and updated tumor position; thus, motion compensation methods can be used for motion compensation during treatment.…”