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The current climate of rapid technological evolution is reflected in newer and better methods to modulate and direct radiation beams for cancer therapy. This Vision 20/20 paper focuses on part of this evolution, locating and targeting moving tumors. The two processes are somewhat independent and in principle different implementations of the locating and targeting processes can be interchanged. Advanced localization and targeting methods have an impact on treatment planning and also present new challenges for quality assurance (QA), that of verifying real-time delivery. Some methods to locate and target moving tumors with radiation beams are currently FDA approved for clinical use-and this availability and implementation will increase with time. Extensions of current capabilities will be the integration of higher order dimensionality, such as rotation and deformation in addition to translation, into the estimate of the patient pose and real-time reoptimization and adaption of delivery to the dynamically changing anatomy of cancer patients.

The accuracy of fiducial-based alignment of external radiotherapy beams is analyzed. The study considers three basic computational methods to determine the target position--the exact closed-form solution for three fiducials, the solution via singular value decomposition for four or more fiducials, and the iterative solution for any number of fiducials--and assesses their accuracy, robustness, and efficiency. Particular attention is paid to inaccuracies arising from the variability of fiducial positions in soft tissue. In nearly every test case it is found that all three solution methods, when properly implemented, yield the same result for the target position, but that the method of singular value decomposition must be modified to distinguish rotations from reflections. When an accurate measure of the rotation of the target site is needed, four fiducials give much better results than three, while more than five fiducials gain little further improvement.

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set. Methods: A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections. Results: When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low. Conclusions: By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs. [ABSTRACT FROM AUTHOR]

Real-time tumor targeting involves the continuous realignment of the radiation beam with the tumor. Real-time tumor targeting offers several advantages such as improved accuracy of tumor treatment and reduced dose to surrounding tissue. Current limitations to this technique include mechanical motion constraints. The purpose of this study was to investigate an alternative treatment scenario using a moving average algorithm. The algorithm, using a suitable averaging period, accounts for variations in the average tumor position, but respiratory induced target position variations about this average are ignored during delivery and can be treated as a random error during planning. In order to test the method a comparison between five different treatment techniques was performed: (1) moving average algorithm, (2) real-time motion tracking, (3) respiration motion gating (at both inhale and exhale), (4) moving average gating (at both inhale and exhale) and (5) static beam delivery. Two data sets were used for the purpose of this analysis: (a) external respiratory-motion traces using different coaching techniques included 331 respiration motion traces from 24 lung-cancer patients acquired using three different breathing types [free breathing (FB), audio coaching (A) and audio-visual biofeedback (AV)]; (b) 3D tumor motion included implanted fiducial motion data for over 160 treatment fractions for 46 thoracic and abdominal cancer patients obtained from the Cyberknife Synchrony. The metrics used for comparison were the group systematic error (M), the standard deviation (SD) of the systematic error (Σ) and the root mean square of the random error (σ). Margins were calculated using the formula by Stroom et al. [Int. J. Radiat. Oncol., Biol., Phys. 43(4), 905–919 (1999)]: 2Σ+0.7σ. The resultant calculations for implanted fiducial motion traces (all values in cm) show that M and Σ are negligible for moving average algorithm, moving average gating, and real-time tracking (i.e., M and Σ=0 cm) compared to static beam (M=0.02 cm and Σ=0.16 cm) or gated beam delivery (M=-0.05 and 0.16 cm at both exhale and inhale, respectively, and Σ=0.17 and 0.26 cm at both exhale and inhale, respectively). Moving average algorithm σ=0.22 cm has a slightly lower random error than static beam delivery σ=0.24 cm, though gating, moving average gating, and real-time tracking have much lower random error values for implanted fiducial motion. Similar trends were also observed for the results using the external respiratory motion data. Moving average algorithm delivery significantly reduces M and Σ compared with static beam delivery. The moving average algorithm removes the nonstationary part of the respiration motion which is also achieved by AV, and thus the addition of the moving average algorithm shows little improvement with AV. Overall, a moving average algorithm shows margin reduction compared with gating and static beam delivery, and may have some mechanical advantages over real-time tracking when the beam is aligned with the target and patient compliance advantages over real-time tracking when the target is aligned to the beam. [ABSTRACT FROM AUTHOR]

Purpose: To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT, which might reduce registration accuracy. We have investigated the effect of image quality differences on the deformable registration of fan-beam CTs and CTs with simulated cone-beam noise. Method: Our study used three CT studies for each of five prostate patients. Each CT was contoured by three experienced radiation oncologists. For each patient, one CT was designated the source image and the other two were target images. A deformable image registration process was used to register each source CT to each target CT and then transfer the manually drawn treatment planning contours from the source CT to the target CTs. The accuracy of the automatically transferred contours (and thus of the deformable registration process) was assessed by comparing them to the manual contours on the target CTs, with the differences evaluated with respect to interobserver variability in the manual contours. Then each of the target CTs was modified to include increased noise characteristic of cone-beam CT and the tests were repeated. Changes in registration accuracy due to increased noise were detected by monitoring changes in the automatically transferred contours. Results: We found that the additional noise caused no significant loss of registration accuracy at magnitudes that exceeded what would normally be found in an actual cone-beam CT. Summary: We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy. [ABSTRACT FROM AUTHOR]

Radiographic image guidance has emerged as the new paradigm for patient positioning, target localization, and external beam alignment in radiotherapy. Although widely varied in modality and method, all radiographic guidance techniques have one thing in common—they can give a significant radiation dose to the patient. As with all medical uses of ionizing radiation, the general view is that this exposure should be carefully managed. The philosophy for dose management adopted by the diagnostic imaging community is summarized by the acronym ALARA, i.e., as low as reasonably achievable. But unlike the general situation with diagnostic imaging and image-guided surgery, image-guided radiotherapy (IGRT) adds the imaging dose to an already high level of therapeutic radiation. There is furthermore an interplay between increased imaging and improved therapeutic dose conformity that suggests the possibility of optimizing rather than simply minimizing the imaging dose. For this reason, the management of imaging dose during radiotherapy is a different problem than its management during routine diagnostic or image-guided surgical procedures. The imaging dose received as part of a radiotherapy treatment has long been regarded as negligible and thus has been quantified in a fairly loose manner. On the other hand, radiation oncologists examine the therapy dose distribution in minute detail. The introduction of more intensive imaging procedures for IGRT now obligates the clinician to evaluate therapeutic and imaging doses in a more balanced manner. This task group is charged with addressing the issue of radiation dose delivered via image guidance techniques during radiotherapy. The group has developed this charge into three objectives: (1) Compile an overview of image-guidance techniques and their associated radiation dose levels, to provide the clinician using a particular set of image guidance techniques with enough data to estimate the total diagnostic dose for a specific treatment scenario, (2) identify ways to reduce the total imaging dose without sacrificing essential imaging information, and (3) recommend optimization strategies to trade off imaging dose with improvements in therapeutic dose delivery. The end goal is to enable the design of image guidance regimens that are as effective and efficient as possible. [ABSTRACT FROM AUTHOR]

When setting up patients via image-guided positioning for external-beam radiotherapy, one can often determine the rotational corrections needed for optimal alignment, but once measured, they are not always applied. However, in rigid-body setup calculations the optimal translational component of the setup correction will be different depending on whether rotations are included or excluded from the correction procedure. Furthermore, if rotations go uncorrected then the optimal translation becomes dependent on the relative locations of the registration landmarks, the treatment site, and the rotational axes. If one is not going to make rotational adjustments to the patient position, then two guidelines should be followed: (1) the registration landmarks should closely demarcate the treatment site, and (2) rotational degrees of freedom should not be included in the calculation of setup adjustments. [ABSTRACT FROM AUTHOR]

The sequence of incremental dose delivery during a radiotherapy fraction can potentially influence the radiobiological effect. This would be most noticeable during the long fractions characteristic of hypo-fractionated stereotactic radiotherapy and radiosurgery. We demonstrate here the spatio-temporal variation of dose delivery by the CyberKnife to a lung tumor and propose strategies to reduce and/or correct for any resultant dose-time cytotoxic effects. [ABSTRACT FROM AUTHOR]

Purpose: To investigate how respiration influences the motion of lung and pancreas tumors and to relate the observations to treatment procedures intended to improve dose alignment by predicting the moving tumor’s position from external breathing indicators.Methods and materials: Breathing characteristics for five healthy subjects were observed by optically tracking the displacement of the chest and abdomen, and by measuring tidal air volume with a spirometer. Fluoroscopic imaging of five radiotherapy patients detected the motion of lung and pancreas tumors synchronously with external breathing indicators.Results: The external and fluoroscopic data showed a wide range of behavior in the normal breathing pattern and its effects on the position of lung and pancreas tumors. This included transient phase shifts between two different external measures of breathing that diminished to zero over a period of minutes, modulated phase shifts between tumor and chest wall motion, and other complex phenomena.Conclusions: Respiratory compensation strategies that infer tumor position from external breathing signals, including methods of beam gating and dynamic beam tracking, require three-dimensional knowledge of the tumor’s motion trajectory as well as the ability to detect and adapt to transient and continuously changing characteristics of respiratory motion during treatment. [Copyright &y& Elsevier]

A letter to the editor is presented in response to an article on how to make rigid-body setup corrections in radiotherapy.