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Model-based deformable organ registration techniques using the finite element method (FEM) have recently been investigated intensively and applied to image-guided adaptive radiotherapy (IGART). These techniques assume that human organs are linearly elastic material, and their mechanical properties are predetermined. Unfortunately, the accurate measurement of the tissue material properties is challenging and the properties usually vary between patients. A common issue is therefore the achievable accuracy of the calculation due to the limited access to tissue elastic material constants. In this study, we performed a systematic investigation on this subject based on tissue biomechanics and computer simulations to establish the relationships between achievable registration accuracy and tissue mechanical and organ geometrical properties. Primarily we focused on image registration for three organs: rectal wall, bladder wall, and prostate. The tissue anisotropy due to orientation preference in tissue fiber alignment is captured by using an orthotropic or a transversely isotropic elastic model. First we developed biomechanical models for the rectal wall, bladder wall, and prostate using simplified geometries and investigated the effect of varying material parameters on the resulting organ deformation. Then computer models based on patient image data were constructed, and image registrations were performed. The sensitivity of registration errors was studied by perturbating the tissue material properties from their mean values while fixing the boundary conditions. The simulation results demonstrated that registration error for a subvolume increases as its distance from the boundary increases. Also, a variable associated with material stability was found to be a dominant factor in registration accuracy in the context of material uncertainty. For hollow thin organs such as rectal walls and bladder walls, the registration errors are limited. Given 30% in material uncertainty, the registration error is limited to within 1.3 mm. For a solid organ such as the prostate, the registration errors are much larger. Given 30% in material uncertainty, the registration error can reach 4.5 mm. However, the registration error distribution for prostates shows that most of the subvolumes have a much smaller registration error. A deformable organ registration technique that uses FEM is a good candidate in IGART if the mean material parameters are available.

With respect to the demands of adaptive and 4D-radiotherapy applications, an algorithm is proposed for a fully automatic, multimodality deformable registration that follows the concept of translational relocation of regularly distributed image subvolumes governed by local anatomical features. Thereby, the problem of global deformable registration is broken down to multiple independent local registration steps which allows for straightforward parallelization of the algorithm. In a subsequent step, possible local misregistrations are corrected for by minimization of the elastic energy of the displacement field under consideration of image information. The final displacement field results from interpolation of the subvolume shift vectors. The algorithm can employ as a similarity measure both the correlation coefficient and mutual information. The latter allows the application to intermodality deformable registration problems. The typical calculation time on a modern multiprocessor PC is well below 1 min, which facilitates almost-interactive, "online" usage. CT-to-MRI and CT-to-cone-beam-CT registrations of head-and-neck data sets are presented, as well as inhale-to-exhale registrations of lung CT data sets. For quantitative evaluation of registration accuracy, a virtual thorax phantom was developed; additionally, a landmark-based evaluation on four lung respiratory-correlated CT data sets was performed. This consistently resulted in average registration residuals on the order of the voxel size or less (3D-residuals approximately 1-2 mm). Summarizing, the presented algorithm allows an accurate multimodality deformable registration with calculation times well below 1 min, and thus bears promise as a versatile basic tool in adaptive and 4D-radiotherapy applications.

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Purpose: CBCT provides useful information for image guidance, however, uncertainty in CT number prevents it from being used for accuracy dose calculation. The purpose of this study is to quantify the accuracy of dose calculation using different CT to density mapping methods and investigate achievable accuracy when applying these methods for dose calculation based on CBCT. Method and Materials: The heterogeneous dose calculated on helical CT (HCT) is used as reference, and the resulting dose from other three methods are compared to reference. In homogeneous dose calculation (HO) method, the density value inside skin contour is overwritten as 1g/cm3. In air‐bone‐soft tissue (ABS) method, the source CT image were replaced by three regions, air, bone, and soft tissue with density overwritten as 0.1 g/cm3, 1.4 g/cm3, and 1 g/cm3 respectively. In stepwise CT density (SWD) method, a CT‐density table representing a stepwise function is applied to CT and used in dose calculation. The three methods are applied to both HCT and CBCT on Pinnacle3 planning system. The resulting dose distributions are compared. Results: Head and neck patients are selected for study. For dose calculation on HCT, the maximum discrepancies in mean dose for HO, ABS, and SWD are within 4%, 2%, and 0.3% for critical organs respectively, and within 1%, 0.5%, and 0.5% for targets. The accuracy results for ABS can be extended to CBCT. For dose calculation on CBCT, dose distribution from ABS is used as reference. The maximum discrepancy between HO and ABS is 2% for critical organs and 0.5% for targets; the maximum discrepancy between SWD and ABS is %1 for critical organs, and 0.5% for targets. Conclusions: Both ABS and SWD can achieve 2% accuracy in dose calculation on CBCT. No contour information is required for SWD, however, normalization of CT number may be necessary.

An innovative phase sorting method (k-means method) is presented in this study. The method is dependent only on tumor motion PDF. It could provide a way to refine the phase sorting in 4DCT reconstruction and is effective for 4D dose accumulation. Optimized sorting techniques could achieve acceptable residuals (less than 0.5% of the prescription dose) using 6 sorting samples, which is much better than amplitude-based or phase-based sorting. Further increase in sorting phase number exceeding 6 or more may not be necessary when using the k-means sampling or optimal sampling points.

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