One of the major challenges of lungcancerradiation 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 radiationdose 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 lungcancer.
This work was supported in part by NIH grant CA140413, by National Science Foundation of China under Grant No. 61075010, and also by the National Basic Research Program of China (973 Program) Grant No. 2010CB732505.
II.A. Construction of super-resolution 4D-CT model on the planning day
II.B. Reconstruction of 4D-CT on the treatment day
II.B.1. Step 1: Bone alignment
II.B.2. Step 2: Phase deinterlace
II.B.3. Step 3: Correspondence detection by robust feature matching
II.B.4. Step 4: Correspondence propagation along respiratory motion
II.B.5. Step 5: Interpolate the dense deformation field
II.B.6. Step 6: Adjust the respiratory motion on the treatment day
II.C. Summary of our 4D-CT reconstruction algorithm
III. RESULTS AND DISCUSSION
III.A. Evaluation of 4D-CT model on the planning day
III.B. Evaluation of 4D-CT reconstruction on the treatment day
III.B.1. Simulated dataset
III.B.2. Real clinic data from lungcancertreatment
- Medical imaging
- Medical image reconstruction
- Radiation treatment
- Image reconstruction
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